TY - JOUR AU - Ruotsalainen, Pekka AU - Blobel, Bernd PY - 2025/4/28 TI - A System Model and Requirements for Transformation to Human-Centric Digital Health JO - J Med Internet Res SP - e68661 VL - 27 KW - digital health KW - human rights KW - privacy KW - dignity KW - autonomy KW - digital economy KW - neoliberalism KW - modeling KW - system analysis KW - artificial intelligence UR - https://www.jmir.org/2025/1/e68661 UR - http://dx.doi.org/10.2196/68661 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/68661 ER - TY - JOUR AU - Sakaguchi, Kota AU - Sakama, Reiko AU - Watari, Takashi PY - 2025/4/24 TI - Evaluating ChatGPT in Qualitative Thematic Analysis With Human Researchers in the Japanese Clinical Context and Its Cultural Interpretation Challenges: Comparative Qualitative Study JO - J Med Internet Res SP - e71521 VL - 27 KW - ChatGPT KW - large language models KW - qualitative research KW - sacred moment(s) KW - thematic analysis N2 - Background: Qualitative research is crucial for understanding the values and beliefs underlying individual experiences, emotions, and behaviors, particularly in social sciences and health care. Traditionally reliant on manual analysis by experienced researchers, this methodology requires significant time and effort. The advent of artificial intelligence (AI) technology, especially large language models such as ChatGPT (OpenAI), holds promise for enhancing qualitative data analysis. However, existing studies have predominantly focused on AI?s application to English-language datasets, leaving its applicability to non-English languages, particularly structurally and contextually complex languages such as Japanese, insufficiently explored. Objective: This study aims to evaluate the feasibility, strengths, and limitations of ChatGPT-4 in analyzing qualitative Japanese interview data by directly comparing its performance with that of experienced human researchers. Methods: A comparative qualitative study was conducted to assess the performance of ChatGPT-4 and human researchers in analyzing transcribed Japanese semistructured interviews. The analysis focused on thematic agreement rates, interpretative depth, and ChatGPT?s ability to process culturally nuanced concepts, particularly for descriptive and socio-culturally embedded themes. This study analyzed transcripts from 30 semistructured interviews conducted between February and March 2024 in an urban community hospital (Hospital A) and a rural university hospital (Hospital B) in Japan. Interviews centered on the theme of ?sacred moments? and involved health care providers and patients. Transcripts were digitized using NVivo (version 14; Lumivero) and analyzed using ChatGPT-4 with iterative prompts for thematic analysis. The results were compared with a reflexive thematic analysis performed by human researchers. Furthermore, to assess the adaptability and consistency of ChatGPT in qualitative analysis, Charmaz?s grounded theory and Pope?s five-step framework approach were applied. Results: ChatGPT-4 demonstrated high thematic agreement rates (>80%) with human researchers for descriptive themes such as ?personal experience of a sacred moment? and ?building relationships.? However, its performance declined for themes requiring deeper cultural and emotional interpretation, such as ?difficult to answer, no experience of sacred moments? and ?fate.? For these themes, agreement rates were approximately 30%, revealing significant limitations in ChatGPT?s ability to process context-dependent linguistic structures and implicit emotional expressions in Japanese. Conclusions: ChatGPT-4 demonstrates potential as an auxiliary tool in qualitative research, particularly for efficiently identifying descriptive themes within Japanese-language datasets. However, its limited capacity to interpret cultural and emotional nuances highlights the continued necessity of human expertise in qualitative analysis. These findings emphasize the complementary role of AI-assisted qualitative research and underscore the importance of further advancements in AI models tailored to non-English linguistic and cultural contexts. Future research should explore strategies to enhance AI?s interpretability, expand multilingual training datasets, and assess the applicability of emerging AI models in diverse cultural settings. In addition, ethical and legal considerations in AI-driven qualitative analysis require continued scrutiny. UR - https://www.jmir.org/2025/1/e71521 UR - http://dx.doi.org/10.2196/71521 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/71521 ER - TY - JOUR AU - Leitner, Kirstin AU - Cutri-French, Clare AU - Mandel, Abigail AU - Christ, Lori AU - Koelper, Nathaneal AU - McCabe, Meaghan AU - Seltzer, Emily AU - Scalise, Laura AU - Colbert, A. James AU - Dokras, Anuja AU - Rosin, Roy AU - Levine, Lisa PY - 2025/4/22 TI - A Conversational Agent Using Natural Language Processing for Postpartum Care for New Mothers: Development and Engagement Analysis JO - JMIR AI SP - e58454 VL - 4 KW - conversational agent KW - postpartum care KW - text messaging KW - postpartum KW - natural language processing KW - pregnancy KW - parents KW - newborns KW - development KW - patient engagement KW - physical recovery KW - infant KW - infant care KW - survey KW - breastfeeding KW - support KW - patient support KW - patient satisfaction N2 - Background: The ?fourth trimester,? or postpartum time period, remains a critical phase of pregnancy that significantly impacts parents and newborns. Care poses challenges due to complex individual needs as well as low attendance rates at routine appointments. A comprehensive technological solution could provide a holistic and equitable solution to meet care goals. Objective: This paper describes the development of patient engagement data with a novel postpartum conversational agent that uses natural language processing to support patients post partum. Methods: We report on the development of a postpartum conversational agent from concept to usable product as well as the patient engagement with this technology. Content for the program was developed using patient- and provider-based input and clinical algorithms. Our program offered 2-way communication to patients and details on physical recovery, lactation support, infant care, and warning signs for problems. This was iterated upon by our core clinical team and an external expert clinical panel before being tested on patients. Patients eligible for discharge around 24 hours after delivery who had delivered a singleton full-term infant vaginally were offered use of the program. Patient demographics, accuracy, and patient engagement were collected over the first 6 months of use. Results: A total of 290 patients used our conversational agent over the first 6 months, of which 112 (38.6%) were first time parents and 162 (56%) were Black. In total, 286 (98.6%) patients interacted with the platform at least once, 271 patients (93.4%) completed at least one survey, and 151 (52%) patients asked a question. First time parents and those breastfeeding their infants had higher rates of engagement overall. Black patients were more likely to promote the program than White patients (P=.047). The overall accuracy of the conversational agent during the first 6 months was 77%. Conclusions: It is possible to develop a comprehensive, automated postpartum conversational agent. The use of such a technology to support patients postdischarge appears to be acceptable with very high engagement and patient satisfaction. UR - https://ai.jmir.org/2025/1/e58454 UR - http://dx.doi.org/10.2196/58454 ID - info:doi/10.2196/58454 ER - TY - JOUR AU - Morand-Grondin, Dorothée AU - Berthod, Jeanne AU - Sigouin, Jennifer AU - Beaulieu-Bonneau, Simon AU - Kairy, Dahlia PY - 2025/4/22 TI - Paving the Road for More Ethical and Equitable Policies and Practices in Telerehabilitation in Psychology and Neuropsychology: Protocol for a Rapid Review JO - JMIR Res Protoc SP - e66639 VL - 14 KW - telerehabilitation KW - psychology KW - neuropsychology KW - equity KW - ethics KW - virtual rehabilitation KW - database KW - rapid review KW - Canada KW - telemedicine N2 - Background: Virtual rehabilitation, or telerehabilitation (TR), has exponentially evolved in the last few years, gaining particular momentum since the COVID-19 pandemic. In response to a new reality of strict restrictions of physical contact necessitating the shift from in-person health services to tele-health visits, TR has seen widespread adoption. In this context, ensuring ethical and equitable TR services is crucial for establishing sustainable TR models for psychology and neuropsychology into health care systems. This requires complete and consistent guidance for clinicians and patients involved. Objective: The objective of this study is to synthesize existing evidence to provide timely insights on potential ethical and equitable benefits and pitfalls associated with the use of TR in a psychological and neuropsychological framework. Methods: A rapid review of TR practices will be conducted specifically within the context of neuropsychology and psychology rehabilitation. We will include review articles published between 2010 and 2020 as well as original articles published between 2020 and 2023, all addressing TR issues with a main focus on neuropsychological and/or psychological rehabilitation activities. This research protocol describes the methodology, including search strategy, screening process, data extraction, and analysis methods. Results: Guided by an experienced librarian, the search strategy was designed and performed in 3 relevant databases. Articles were screened in accordance with the inclusion and exclusion criteria, and data were collected by 2 independent reviewers. Data extraction is underway, and we expect to complete the rapid review in January 2025. Conclusions: This study is part of a broader cross-Canadian initiative aimed at informing policy development and clinical practices in TR. By evaluating the ethical and equitable considerations specific to psychology and neuropsychology, this review aims to contribute to help shape future TR practices to ensure access to high-quality, accessible TR services supporting diverse patient needs in psychology and neuropsychology. International Registered Report Identifier (IRRID): DERR1-10.2196/66639 UR - https://www.researchprotocols.org/2025/1/e66639 UR - http://dx.doi.org/10.2196/66639 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/66639 ER - TY - JOUR AU - Zhang, Jia AU - Wang, Jing AU - Zhang, JingBo AU - Xia, XiaoQian AU - Zhou, ZiYun AU - Zhou, XiaoMing AU - Wu, YiBo PY - 2025/4/9 TI - Young Adult Perspectives on Artificial Intelligence?Based Medication Counseling in China: Discrete Choice Experiment JO - J Med Internet Res SP - e67744 VL - 27 KW - artificial intelligence KW - medication counseling services KW - discrete choice experiment KW - willingness to pay N2 - Background: As artificial intelligence (AI) permeates the current society, the young generation is becoming increasingly accustomed to using digital solutions. AI-based medication counseling services may help people take medications more accurately and reduce adverse events. However, it is not known which AI-based medication counseling service will be preferred by young people. Objective: This study aims to assess young people?s preferences for AI-based medication counseling services. Methods: A discrete choice experiment (DCE) approach was the main analysis method applied in this study, involving 6 attributes: granularity, linguistic comprehensibility, symptom-specific results, access platforms, content model, and costs. The participants in this study were screened and recruited through web-based registration and investigator visits, and the questionnaire was filled out online, with the questionnaire platform provided by Questionnaire Star. The sample population in this study consisted of young adults aged 18-44 years. A mixed logit model was used to estimate attribute preference coefficients and to estimate the willingness to pay (WTP) and relative importance (RI) scores. Subgroups were also analyzed to check for heterogeneity in preferences. Results: In this analysis, 340 participants were included, generating 8160 DCE observations. Participants exhibited a strong preference for receiving 100% symptom-specific results (?=3.18, 95% CI 2.54-3.81; P<.001), and the RI of the attributes (RI=36.99%) was consistent with this. Next, they showed preference for the content model of the video (?=0.86, 95% CI 0.51-1.22; P<.001), easy-to-understand language (?=0.81, 95% CI 0.46-1.16; P<.001), and when considering the granularity, refined content was preferred over general information (?=0.51, 95% CI 0.21-0.8; P<.001). Finally, participants exhibited a notable preference for accessing information through WeChat applets rather than websites (?=0.66, 95% CI 0.27-1.05; P<.001). The WTP for AI-based medication counseling services ranked from the highest to the lowest for symptom-specific results, easy-to-understand language, video content, WeChat applet platform, and refined medication counseling. Among these, the WTP for 100% symptom-specific results was the highest (¥24.01, 95% CI 20.16-28.77; US $1=¥7.09). High-income participants exhibited significantly higher WTP for highly accurate results (¥45.32) compared to low-income participants (¥20.65). Similarly, participants with higher education levels showed greater preferences for easy-to-understand language (¥5.93) and video content (¥12.53). Conclusions: We conducted an in-depth investigation of the preference of young people for AI-based medication counseling services. Service providers should pay attention to symptom-specific results, support more convenient access platforms, and optimize the language description, content models that add multiple digital media interactions, and more refined medication counseling to develop AI-based medication counseling services. UR - https://www.jmir.org/2025/1/e67744 UR - http://dx.doi.org/10.2196/67744 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/67744 ER - TY - JOUR AU - Perakslis, Eric AU - Nolen, Kimberly AU - Fricklas, Ethan AU - Tubb, Tracy PY - 2025/4/7 TI - Striking a Balance: Innovation, Equity, and Consistency in AI Health Technologies JO - JMIR AI SP - e57421 VL - 4 KW - artificial intelligence KW - algorithm KW - regulatory landscape KW - predictive model KW - predictive analytics KW - predictive system KW - practical model KW - machine learning KW - large language model KW - natural language processing KW - deep learning KW - digital health KW - regulatory KW - health technology UR - https://ai.jmir.org/2025/1/e57421 UR - http://dx.doi.org/10.2196/57421 ID - info:doi/10.2196/57421 ER - TY - JOUR AU - Lewis, Claire AU - Groarke, Jenny AU - Graham-Wisener, Lisa AU - James, Jacqueline PY - 2025/4/2 TI - Public Awareness of and Attitudes Toward the Use of AI in Pathology Research and Practice: Mixed Methods Study JO - J Med Internet Res SP - e59591 VL - 27 KW - artificial intelligence KW - AI KW - public opinion KW - pathology KW - health care KW - public awareness KW - survey N2 - Background: The last decade has witnessed major advances in the development of artificial intelligence (AI) technologies for use in health care. One of the most promising areas of research that has potential clinical utility is the use of AI in pathology to aid cancer diagnosis and management. While the value of using AI to improve the efficiency and accuracy of diagnosis cannot be underestimated, there are challenges in the development and implementation of such technologies. Notably, questions remain about public support for the use of AI to assist in pathological diagnosis and for the use of health care data, including data obtained from tissue samples, to train algorithms. Objective: This study aimed to investigate public awareness of and attitudes toward AI in pathology research and practice. Methods: A nationally representative, cross-sectional, web-based mixed methods survey (N=1518) was conducted to assess the UK public?s awareness of and views on the use of AI in pathology research and practice. Respondents were recruited via Prolific, an online research platform. To be eligible for the study, participants had to be aged >18 years, be UK residents, and have the capacity to express their own opinion. Respondents answered 30 closed-ended questions and 2 open-ended questions. Sociodemographic information and previous experience with cancer were collected. Descriptive and inferential statistics were used to analyze quantitative data; qualitative data were analyzed thematically. Results: Awareness was low, with only 23.19% (352/1518) of the respondents somewhat or moderately aware of AI being developed for use in pathology. Most did not support a diagnosis of cancer (908/1518, 59.82%) or a diagnosis based on biomarkers (694/1518, 45.72%) being made using AI only. However, most (1478/1518, 97.36%) supported diagnoses made by pathologists with AI assistance. The adjusted odds ratio (aOR) for supporting AI in cancer diagnosis and management was higher for men (aOR 1.34, 95% CI 1.02-1.75). Greater awareness (aOR 1.25, 95% CI 1.10-1.42), greater trust in data security and privacy protocols (aOR 1.04, 95% CI 1.01-1.07), and more positive beliefs (aOR 1.27, 95% CI 1.20-1.36) also increased support, whereas identifying more risks reduced the likelihood of support (aOR 0.80, 95% CI 0.73-0.89). In total, 3 main themes emerged from the qualitative data: bringing the public along, the human in the loop, and more hard evidence needed, indicating conditional support for AI in pathology with human decision-making oversight, robust measures for data handling and protection, and evidence for AI benefit and effectiveness. Conclusions: Awareness of AI?s potential use in pathology was low, but attitudes were positive, with high but conditional support. Challenges remain, particularly among women, regarding AI use in cancer diagnosis and management. Apprehension persists about the access to and use of health care data by private organizations. UR - https://www.jmir.org/2025/1/e59591 UR - http://dx.doi.org/10.2196/59591 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/59591 ER - TY - JOUR AU - Chen, Jun AU - Liu, Yu AU - Liu, Peng AU - Zhao, Yiming AU - Zuo, Yan AU - Duan, Hui PY - 2025/4/1 TI - Adoption of Large Language Model AI Tools in Everyday Tasks: Multisite Cross-Sectional Qualitative Study of Chinese Hospital Administrators JO - J Med Internet Res SP - e70789 VL - 27 KW - large language model KW - artificial intelligence KW - health care administration KW - technology adoption KW - hospital administrator KW - qualitative study KW - barriers to adoption N2 - Background: Large language model (LLM) artificial intelligence (AI) tools have the potential to streamline health care administration by enhancing efficiency in document drafting, resource allocation, and communication tasks. Despite this potential, the adoption of such tools among hospital administrators remains understudied, particularly at the individual level. Objective: This study aims to explore factors influencing the adoption and use of LLM AI tools among hospital administrators in China, focusing on enablers, barriers, and practical applications in daily administrative tasks. Methods: A multicenter, cross-sectional, descriptive qualitative design was used. Data were collected through semistructured face-to-face interviews with 31 hospital administrators across 3 tertiary hospitals in Beijing, Shenzhen, and Chengdu from June 2024 to August 2024. The Colaizzi method was used for thematic analysis to identify patterns in participants? experiences and perspectives. Results: Adoption of LLM AI tools was generally low, with significant site-specific variations. Participants with higher technological familiarity and positive early experiences reported more frequent use, while barriers such as mistrust in tool accuracy, limited prompting skills, and insufficient training hindered broader adoption. Tools were primarily used for document drafting, with limited exploration of advanced functionalities. Participants strongly emphasized the need for structured training programs and institutional support to enhance usability and confidence. Conclusions: Familiarity with technology, positive early experiences, and openness to innovation may facilitate adoption, while barriers such as limited knowledge, mistrust in tool accuracy, and insufficient prompting skills can hinder broader use. LLM AI tools are now primarily used for basic tasks such as document drafting, with limited application to more advanced functionalities due to a lack of training and confidence. Structured tutorials and institutional support are needed to enhance usability and integration. Targeted training programs, combined with organizational strategies to build trust and improve accessibility, could enhance adoption rates and broaden tool use. Future quantitative investigations should validate the adoption rate and influencing factors. UR - https://www.jmir.org/2025/1/e70789 UR - http://dx.doi.org/10.2196/70789 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/70789 ER - TY - JOUR AU - Dorémus, Océane AU - Russon, Dylan AU - Contrand, Benjamin AU - Guerra-Adames, Ariel AU - Avalos-Fernandez, Marta AU - Gil-Jardiné, Cédric AU - Lagarde, Emmanuel PY - 2025/4/1 TI - Harnessing Moderate-Sized Language Models for Reliable Patient Data Deidentification in Emergency Department Records: Algorithm Development, Validation, and Implementation Study JO - JMIR AI SP - e57828 VL - 4 KW - de-identification KW - machine learning KW - large language model KW - natural language processing KW - electronic health records KW - transformers KW - general data protection regulation KW - clinical notes N2 - Background: The digitization of health care, facilitated by the adoption of electronic health records systems, has revolutionized data-driven medical research and patient care. While this digital transformation offers substantial benefits in health care efficiency and accessibility, it concurrently raises significant concerns over privacy and data security. Initially, the journey toward protecting patient data deidentification saw the transition from rule-based systems to more mixed approaches including machine learning for deidentifying patient data. Subsequently, the emergence of large language models has represented a further opportunity in this domain, offering unparalleled potential for enhancing the accuracy of context-sensitive deidentification. However, despite large language models offering significant potential, the deployment of the most advanced models in hospital environments is frequently hindered by data security issues and the extensive hardware resources required. Objective: The objective of our study is to design, implement, and evaluate deidentification algorithms using fine-tuned moderate-sized open-source language models, ensuring their suitability for production inference tasks on personal computers. Methods: We aimed to replace personal identifying information (PII) with generic placeholders or labeling non-PII texts as ?ANONYMOUS,? ensuring privacy while preserving textual integrity. Our dataset, derived from over 425,000 clinical notes from the adult emergency department of the Bordeaux University Hospital in France, underwent independent double annotation by 2 experts to create a reference for model validation with 3000 clinical notes randomly selected. Three open-source language models of manageable size were selected for their feasibility in hospital settings: Llama 2 (Meta) 7B, Mistral 7B, and Mixtral 8×7B (Mistral AI). Fine-tuning used the quantized low-rank adaptation technique. Evaluation focused on PII-level (recall, precision, and F1-score) and clinical note-level metrics (recall and BLEU [bilingual evaluation understudy] metric), assessing deidentification effectiveness and content preservation. Results: The generative model Mistral 7B performed the highest with an overall F1-score of 0.9673 (vs 0.8750 for Llama 2 and 0.8686 for Mixtral 8×7B). At the clinical notes level, the model?s overall recall was 0.9326 (vs 0.6888 for Llama 2 and 0.6417 for Mixtral 8×7B). This rate increased to 0.9915 when Mistral 7B only deleted names. Four notes of 3000 failed to be fully pseudonymized for names: in 1 case, the nondeleted name belonged to a patient, while in the others, it belonged to medical staff. Beyond the fifth epoch, the BLEU score consistently exceeded 0.9864, indicating no significant text alteration. Conclusions: Our research underscores the significant capabilities of generative natural language processing models, with Mistral 7B standing out for its superior ability to deidentify clinical texts efficiently. Achieving notable performance metrics, Mistral 7B operates effectively without requiring high-end computational resources. These methods pave the way for a broader availability of pseudonymized clinical texts, enabling their use for research purposes and the optimization of the health care system. UR - https://ai.jmir.org/2025/1/e57828 UR - http://dx.doi.org/10.2196/57828 ID - info:doi/10.2196/57828 ER - TY - JOUR AU - Yan, Zelin AU - Liu, Jingwen AU - Fan, Yihong AU - Lu, Shiyuan AU - Xu, Dingting AU - Yang, Yun AU - Wang, Honggang AU - Mao, Jie AU - Tseng, Hou-Chiang AU - Chang, Tao-Hsing AU - Chen, Yan PY - 2025/3/31 TI - Ability of ChatGPT to Replace Doctors in Patient Education: Cross-Sectional Comparative Analysis of Inflammatory Bowel Disease JO - J Med Internet Res SP - e62857 VL - 27 KW - AI-assisted KW - patient education KW - inflammatory bowel disease KW - artificial intelligence KW - ChatGPT KW - patient communities KW - social media KW - disease management KW - readability KW - online health information KW - conversational agents N2 - Background: Although large language models (LLMs) such as ChatGPT show promise for providing specialized information, their quality requires further evaluation. This is especially true considering that these models are trained on internet text and the quality of health-related information available online varies widely. Objective: The aim of this study was to evaluate the performance of ChatGPT in the context of patient education for individuals with chronic diseases, comparing it with that of industry experts to elucidate its strengths and limitations. Methods: This evaluation was conducted in September 2023 by analyzing the responses of ChatGPT and specialist doctors to questions posed by patients with inflammatory bowel disease (IBD). We compared their performance in terms of subjective accuracy, empathy, completeness, and overall quality, as well as readability to support objective analysis. Results: In a series of 1578 binary choice assessments, ChatGPT was preferred in 48.4% (95% CI 45.9%-50.9%) of instances. There were 12 instances where ChatGPT?s responses were unanimously preferred by all evaluators, compared with 17 instances for specialist doctors. In terms of overall quality, there was no significant difference between the responses of ChatGPT (3.98, 95% CI 3.93-4.02) and those of specialist doctors (3.95, 95% CI 3.90-4.00; t524=0.95, P=.34), both being considered ?good.? Although differences in accuracy (t521=0.48, P=.63) and empathy (t511=2.19, P=.03) lacked statistical significance, the completeness of textual output (t509=9.27, P<.001) was a distinct advantage of the LLM (ChatGPT). In the sections of the questionnaire where patients and doctors responded together (Q223-Q242), ChatGPT demonstrated inferior performance (t36=2.91, P=.006). Regarding readability, no statistical difference was found between the responses of specialist doctors (median: 7th grade; Q1: 4th grade; Q3: 8th grade) and those of ChatGPT (median: 7th grade; Q1: 7th grade; Q3: 8th grade) according to the Mann-Whitney U test (P=.09). The overall quality of ChatGPT?s output exhibited strong correlations with other subdimensions (with empathy: r=0.842; with accuracy: r=0.839; with completeness: r=0.795), and there was also a high correlation between the subdimensions of accuracy and completeness (r=0.762). Conclusions: ChatGPT demonstrated more stable performance across various dimensions. Its output of health information content is more structurally sound, addressing the issue of variability in the information from individual specialist doctors. ChatGPT?s performance highlights its potential as an auxiliary tool for health information, despite limitations such as artificial intelligence hallucinations. It is recommended that patients be involved in the creation and evaluation of health information to enhance the quality and relevance of the information. UR - https://www.jmir.org/2025/1/e62857 UR - http://dx.doi.org/10.2196/62857 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/62857 ER - TY - JOUR AU - Kauttonen, Janne AU - Rousi, Rebekah AU - Alamäki, Ari PY - 2025/3/21 TI - Trust and Acceptance Challenges in the Adoption of AI Applications in Health Care: Quantitative Survey Analysis JO - J Med Internet Res SP - e65567 VL - 27 KW - artificial intelligence KW - AI KW - health care technology KW - technology adoption KW - predictive modeling KW - user trust KW - user acceptance N2 - Background: Artificial intelligence (AI) has potential to transform health care, but its successful implementation depends on the trust and acceptance of consumers and patients. Understanding the factors that influence attitudes toward AI is crucial for effective adoption. Despite AI?s growing integration into health care, consumer and patient acceptance remains a critical challenge. Research has largely focused on applications or attitudes, lacking a comprehensive analysis of how factors, such as demographics, personality traits, technology attitudes, and AI knowledge, affect and interact across different health care AI contexts. Objective: We aimed to investigate people?s trust in and acceptance of AI across health care use cases and determine how context and perceived risk affect individuals? propensity to trust and accept AI in specific health care scenarios. Methods: We collected and analyzed web-based survey data from 1100 Finnish participants, presenting them with 8 AI use cases in health care: 5 (62%) noninvasive applications (eg, activity monitoring and mental health support) and 3 (38%) physical interventions (eg, AI-controlled robotic surgery). Respondents evaluated intention to use, trust, and willingness to trade off personal data for these use cases. Gradient boosted tree regression models were trained to predict responses based on 33 demographic-, personality-, and technology-related variables. To interpret the results of our predictive models, we used the Shapley additive explanations method, a game theory?based approach for explaining the output of machine learning models. It quantifies the contribution of each feature to individual predictions, allowing us to determine the relative importance of various demographic-, personality-, and technology-related factors and their interactions in shaping participants? trust in and acceptance of AI in health care. Results: Consumer attitudes toward technology, technology use, and personality traits were the primary drivers of trust and intention to use AI in health care. Use cases were ranked by acceptance, with noninvasive monitors being the most preferred. However, the specific use case had less impact in general than expected. Nonlinear dependencies were observed, including an inverted U-shaped pattern in positivity toward AI based on self-reported AI knowledge. Certain personality traits, such as being more disorganized and careless, were associated with more positive attitudes toward AI in health care. Women seemed more cautious about AI applications in health care than men. Conclusions: The findings highlight the complex interplay of factors influencing trust and acceptance of AI in health care. Consumer trust and intention to use AI in health care are driven by technology attitudes and use rather than specific use cases. AI service providers should consider demographic factors, personality traits, and technology attitudes when designing and implementing AI systems in health care. The study demonstrates the potential of using predictive AI models as decision-making tools for implementing and interacting with clients in health care AI applications. UR - https://www.jmir.org/2025/1/e65567 UR - http://dx.doi.org/10.2196/65567 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/65567 ER - TY - JOUR AU - Lau, Jerry AU - Bisht, Shivani AU - Horton, Robert AU - Crisan, Annamaria AU - Jones, John AU - Gantotti, Sandeep AU - Hermes-DeSantis, Evelyn PY - 2025/3/13 TI - Creation of Scientific Response Documents for Addressing Product Medical Information Inquiries: Mixed Method Approach Using Artificial Intelligence JO - JMIR AI SP - e55277 VL - 4 KW - AI KW - LLM KW - GPT KW - biopharmaceutical KW - medical information KW - content generation KW - artificial intelligence KW - pharmaceutical KW - scientific response KW - documentation KW - information KW - clinical data KW - strategy KW - reference KW - feasibility KW - development KW - machine learning KW - large language model KW - accuracy KW - context KW - traceability KW - accountability KW - survey KW - scientific response documentation KW - SRD KW - benefit KW - content generator KW - content analysis KW - Generative Pre-trained Transformer N2 - Background: Pharmaceutical manufacturers address health care professionals? information needs through scientific response documents (SRDs), offering evidence-based answers to medication and disease state questions. Medical information departments, staffed by medical experts, develop SRDs that provide concise summaries consisting of relevant background information, search strategies, clinical data, and balanced references. With an escalating demand for SRDs and the increasing complexity of therapies, medical information departments are exploring advanced technologies and artificial intelligence (AI) tools like large language models (LLMs) to streamline content development. While AI and LLMs show promise in generating draft responses, a synergistic approach combining an LLM with traditional machine learning classifiers in a series of human-supervised and -curated steps could help address limitations, including hallucinations. This will ensure accuracy, context, traceability, and accountability in the development of the concise clinical data summaries of an SRD. Objective: This study aims to quantify the challenges of SRD development and develop a framework exploring the feasibility and value addition of integrating AI capabilities in the process of creating concise summaries for an SRD. Methods: To measure the challenges in SRD development, a survey was conducted by phactMI, a nonprofit consortium of medical information leaders in the pharmaceutical industry, assessing aspects of SRD creation among its member companies. The survey collected data on the time and tediousness of various activities related to SRD development. Another working group, consisting of medical information professionals and data scientists, used AI to aid SRD authoring, focusing on data extraction and abstraction. They used logistic regression on semantic embedding features to train classification models and transformer-based summarization pipelines to generate concise summaries. Results: Of the 33 companies surveyed, 64% (21/33) opened the survey, and 76% (16/21) of those responded. On average, medical information departments generate 614 new documents and update 1352 documents each year. Respondents considered paraphrasing scientific articles to be the most tedious and time-intensive task. In the project?s second phase, sentence classification models showed the ability to accurately distinguish target categories with receiver operating characteristic scores ranging from 0.67 to 0.85 (all P<.001), allowing for accurate data extraction. For data abstraction, the comparison of the bilingual evaluation understudy (BLEU) score and semantic similarity in the paraphrased texts yielded different results among reviewers, with each preferring different trade-offs between these metrics. Conclusions: This study establishes a framework for integrating LLM and machine learning into SRD development, supported by a pharmaceutical company survey emphasizing the challenges of paraphrasing content. While machine learning models show potential for section identification and content usability assessment in data extraction and abstraction, further optimization and research are essential before full-scale industry implementation. The working group?s insights guide an AI-driven content analysis; address limitations; and advance efficient, precise, and responsive frameworks to assist with pharmaceutical SRD development. UR - https://ai.jmir.org/2025/1/e55277 UR - http://dx.doi.org/10.2196/55277 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/55277 ER - TY - JOUR AU - Guo, Weiqi AU - Chen, Yang PY - 2025/3/5 TI - Investigating Whether AI Will Replace Human Physicians and Understanding the Interplay of the Source of Consultation, Health-Related Stigma, and Explanations of Diagnoses on Patients? Evaluations of Medical Consultations: Randomized Factorial Experiment JO - J Med Internet Res SP - e66760 VL - 27 KW - artificial intelligence KW - AI KW - medical artificial intelligence KW - medical AI KW - human?artificial intelligence interaction KW - human-AI interaction KW - medical consultation KW - health-related stigma KW - diagnosis explanation KW - health communication N2 - Background: The increasing use of artificial intelligence (AI) in medical diagnosis and consultation promises benefits such as greater accuracy and efficiency. However, there is little evidence to systematically test whether the ideal technological promises translate into an improved evaluation of the medical consultation from the patient?s perspective. This perspective is significant because AI as a technological solution does not necessarily improve patient confidence in diagnosis and adherence to treatment at the functional level, create meaningful interactions between the medical agent and the patient at the relational level, evoke positive emotions, or reduce the patient?s pessimism at the emotional level. Objective: This study aims to investigate, from a patient-centered perspective, whether AI or human-involved AI can replace the role of human physicians in diagnosis at the functional, relational, and emotional levels as well as how some health-related differences between human-AI and human-human interactions affect patients? evaluations of the medical consultation. Methods: A 3 (consultation source: AI vs human-involved AI vs human) × 2 (health-related stigma: low vs high) × 2 (diagnosis explanation: without vs with explanation) factorial experiment was conducted with 249 participants. The main effects and interaction effects of the variables were examined on individuals? functional, relational, and emotional evaluations of the medical consultation. Results: Functionally, people trusted the diagnosis of the human physician (mean 4.78-4.85, SD 0.06-0.07) more than medical AI (mean 4.34-4.55, SD 0.06-0.07) or human-involved AI (mean 4.39-4.56, SD 0.06-0.07; P<.001), but at the relational and emotional levels, there was no significant difference between human-AI and human-human interactions (P>.05). Health-related stigma had no significant effect on how people evaluated the medical consultation or contributed to preferring AI-powered systems over humans (P>.05); however, providing explanations of the diagnosis significantly improved the functional (P<.001), relational (P<.05), and emotional (P<.05) evaluations of the consultation for all 3 medical agents. Conclusions: The findings imply that at the current stage of AI development, people trust human expertise more than accurate AI, especially for decisions traditionally made by humans, such as medical diagnosis, supporting the algorithm aversion theory. Surprisingly, even for highly stigmatized diseases such as AIDS, where we assume anonymity and privacy are preferred in medical consultations, the dehumanization of AI does not contribute significantly to the preference for AI-powered medical agents over humans, suggesting that instrumental needs of diagnosis override patient privacy concerns. Furthermore, explaining the diagnosis effectively improves treatment adherence, strengthens the physician-patient relationship, and fosters positive emotions during the consultation. This provides insights for the design of AI medical agents, which have long been criticized for lacking transparency while making highly consequential decisions. This study concludes by outlining theoretical contributions to research on health communication and human-AI interaction and discusses the implications for the design and application of medical AI. UR - https://www.jmir.org/2025/1/e66760 UR - http://dx.doi.org/10.2196/66760 UR - http://www.ncbi.nlm.nih.gov/pubmed/40053785 ID - info:doi/10.2196/66760 ER - TY - JOUR AU - Cabral, Pereira Bernardo AU - Braga, Maciel Luiza Amara AU - Conte Filho, Gilbert Carlos AU - Penteado, Bruno AU - Freire de Castro Silva, Luis Sandro AU - Castro, Leonardo AU - Fornazin, Marcelo AU - Mota, Fabio PY - 2025/2/27 TI - Future Use of AI in Diagnostic Medicine: 2-Wave Cross-Sectional Survey Study JO - J Med Internet Res SP - e53892 VL - 27 KW - artificial intelligence KW - AI KW - diagnostic medicine KW - survey research KW - researcher opinion KW - future N2 - Background: The rapid evolution of artificial intelligence (AI) presents transformative potential for diagnostic medicine, offering opportunities to enhance diagnostic accuracy, reduce costs, and improve patient outcomes. Objective: This study aimed to assess the expected future impact of AI on diagnostic medicine by comparing global researchers? expectations using 2 cross-sectional surveys. Methods: The surveys were conducted in September 2020 and February 2023. Each survey captured a 10-year projection horizon, gathering insights from >3700 researchers with expertise in AI and diagnostic medicine from all over the world. The survey sought to understand the perceived benefits, integration challenges, and evolving attitudes toward AI use in diagnostic settings. Results: Results indicated a strong expectation among researchers that AI will substantially influence diagnostic medicine within the next decade. Key anticipated benefits include enhanced diagnostic reliability, reduced screening costs, improved patient care, and decreased physician workload, addressing the growing demand for diagnostic services outpacing the supply of medical professionals. Specifically, x-ray diagnosis, heart rhythm interpretation, and skin malignancy detection were identified as the diagnostic tools most likely to be integrated with AI technologies due to their maturity and existing AI applications. The surveys highlighted the growing optimism regarding AI?s ability to transform traditional diagnostic pathways and enhance clinical decision-making processes. Furthermore, the study identified barriers to the integration of AI in diagnostic medicine. The primary challenges cited were the difficulties of embedding AI within existing clinical workflows, ethical and regulatory concerns, and data privacy issues. Respondents emphasized uncertainties around legal responsibility and accountability for AI-supported clinical decisions, data protection challenges, and the need for robust regulatory frameworks to ensure safe AI deployment. Ethical concerns, particularly those related to algorithmic transparency and bias, were noted as increasingly critical, reflecting a heightened awareness of the potential risks associated with AI adoption in clinical settings. Differences between the 2 survey waves indicated a growing focus on ethical and regulatory issues, suggesting an evolving recognition of these challenges over time. Conclusions: Despite these barriers, there was notable consistency in researchers? expectations across the 2 survey periods, indicating a stable and sustained outlook on AI?s transformative potential in diagnostic medicine. The findings show the need for interdisciplinary collaboration among clinicians, AI developers, and regulators to address ethical and practical challenges while maximizing AI?s benefits. This study offers insights into the projected trajectory of AI in diagnostic medicine, guiding stakeholders, including health care providers, policy makers, and technology developers, on navigating the opportunities and challenges of AI integration. UR - https://www.jmir.org/2025/1/e53892 UR - http://dx.doi.org/10.2196/53892 UR - http://www.ncbi.nlm.nih.gov/pubmed/40053779 ID - info:doi/10.2196/53892 ER - TY - JOUR AU - Hadar-Shoval, Dorit AU - Lvovsky, Maya AU - Asraf, Kfir AU - Shimoni, Yoav AU - Elyoseph, Zohar PY - 2025/2/24 TI - The Feasibility of Large Language Models in Verbal Comprehension Assessment: Mixed Methods Feasibility Study JO - JMIR Form Res SP - e68347 VL - 9 KW - large language models KW - verbal comprehension assessment KW - artificial intelligence KW - AI in psychodiagnostics KW - personalized intelligence tests KW - verbal comprehension index KW - Wechsler Adult Intelligence Scale KW - WAIS-III KW - psychological test validity KW - ethics in computerized cognitive assessment N2 - Background: Cognitive assessment is an important component of applied psychology, but limited access and high costs make these evaluations challenging. Objective: This study aimed to examine the feasibility of using large language models (LLMs) to create personalized artificial intelligence?based verbal comprehension tests (AI-BVCTs) for assessing verbal intelligence, in contrast with traditional assessment methods based on standardized norms. Methods: We used a within-participants design, comparing scores obtained from AI-BVCTs with those from the Wechsler Adult Intelligence Scale (WAIS-III) verbal comprehension index (VCI). In total, 8 Hebrew-speaking participants completed both the VCI and AI-BVCT, the latter being generated using the LLM Claude. Results: The concordance correlation coefficient (CCC) demonstrated strong agreement between AI-BVCT and VCI scores (Claude: CCC=.75, 90% CI 0.266-0.933; GPT-4: CCC=.73, 90% CI 0.170-0.935). Pearson correlations further supported these findings, showing strong associations between VCI and AI-BVCT scores (Claude: r=.84, P<.001; GPT-4: r=.77, P=.02). No statistically significant differences were found between AI-BVCT and VCI scores (P>.05). Conclusions: These findings support the potential of LLMs to assess verbal intelligence. The study attests to the promise of AI-based cognitive tests in increasing the accessibility and affordability of assessment processes, enabling personalized testing. The research also raises ethical concerns regarding privacy and overreliance on AI in clinical work. Further research with larger and more diverse samples is needed to establish the validity and reliability of this approach and develop more accurate scoring procedures. UR - https://formative.jmir.org/2025/1/e68347 UR - http://dx.doi.org/10.2196/68347 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/68347 ER - TY - JOUR AU - Rinderknecht, Fatuma-Ayaan AU - Yang, B. Vivian AU - Tilahun, Mekaleya AU - Lester, C. Jenna PY - 2025/2/21 TI - Perspectives of Black, Latinx, Indigenous, and Asian Communities on Health Data Use and AI: Cross-Sectional Survey Study JO - J Med Internet Res SP - e50708 VL - 27 KW - augmented intelligence KW - artificial intelligence KW - health equity KW - dermatology KW - Black KW - Latinx KW - Indigenous KW - Asian KW - racial and ethnic minority communities KW - AI KW - health care KW - health data KW - survey KW - racism KW - large language model KW - LLM KW - diversity UR - https://www.jmir.org/2025/1/e50708 UR - http://dx.doi.org/10.2196/50708 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/50708 ER - TY - JOUR AU - Owoyemi, Ayomide AU - Osuchukwu, Joanne AU - Salwei, E. Megan AU - Boyd, Andrew PY - 2025/2/20 TI - Checklist Approach to Developing and Implementing AI in Clinical Settings: Instrument Development Study JO - JMIRx Med SP - e65565 VL - 6 KW - artificial intelligence KW - machine learning KW - algorithm KW - model KW - analytics KW - AI deployment KW - human-AI interaction KW - AI integration KW - checklist KW - clinical workflow KW - clinical setting KW - literature review N2 - Background: The integration of artificial intelligence (AI) in health care settings demands a nuanced approach that considers both technical performance and sociotechnical factors. Objective: This study aimed to develop a checklist that addresses the sociotechnical aspects of AI deployment in health care and provides a structured, holistic guide for teams involved in the life cycle of AI systems. Methods: A literature synthesis identified 20 relevant studies, forming the foundation for the Clinical AI Sociotechnical Framework checklist. A modified Delphi study was then conducted with 35 global health care professionals. Participants assessed the checklist?s relevance across 4 stages: ?Planning,? ?Design,? ?Development,? and ?Proposed Implementation.? A consensus threshold of 80% was established for each item. IQRs and Cronbach ? were calculated to assess agreement and reliability. Results: The initial checklist had 45 questions. Following participant feedback, the checklist was refined to 34 items, and a final round saw 100% consensus on all items (mean score >0.8, IQR 0). Based on the outcome of the Delphi study, a final checklist was outlined, with 1 more question added to make 35 questions in total. Conclusions: The Clinical AI Sociotechnical Framework checklist provides a comprehensive, structured approach to developing and implementing AI in clinical settings, addressing technical and social factors critical for adoption and success. This checklist is a practical tool that aligns AI development with real-world clinical needs, aiming to enhance patient outcomes and integrate smoothly into health care workflows. UR - https://xmed.jmir.org/2025/1/e65565 UR - http://dx.doi.org/10.2196/65565 ID - info:doi/10.2196/65565 ER - TY - JOUR AU - King, C. Abby AU - Doueiri, N. Zakaria AU - Kaulberg, Ankita AU - Goldman Rosas, Lisa PY - 2025/2/14 TI - The Promise and Perils of Artificial Intelligence in Advancing Participatory Science and Health Equity in Public Health JO - JMIR Public Health Surveill SP - e65699 VL - 11 KW - digital health KW - artificial intelligence KW - community-based participatory research KW - citizen science KW - health equity KW - societal trends KW - public health KW - viewpoint KW - policy makers KW - public participation KW - information technology KW - micro-level data KW - macro-level data KW - LLM KW - natural language processing KW - machine learning KW - language model KW - Our Voice UR - https://publichealth.jmir.org/2025/1/e65699 UR - http://dx.doi.org/10.2196/65699 ID - info:doi/10.2196/65699 ER - TY - JOUR AU - Choudhury, Ananya AU - Volmer, Leroy AU - Martin, Frank AU - Fijten, Rianne AU - Wee, Leonard AU - Dekker, Andre AU - Soest, van Johan PY - 2025/2/6 TI - Advancing Privacy-Preserving Health Care Analytics and Implementation of the Personal Health Train: Federated Deep Learning Study JO - JMIR AI SP - e60847 VL - 4 KW - gross tumor volume segmentation KW - federated learning infrastructure KW - privacy-preserving technology KW - cancer KW - deep learning KW - artificial intelligence KW - lung cancer KW - oncology KW - radiotherapy KW - imaging KW - data protection KW - data privacy N2 - Background: The rapid advancement of deep learning in health care presents significant opportunities for automating complex medical tasks and improving clinical workflows. However, widespread adoption is impeded by data privacy concerns and the necessity for large, diverse datasets across multiple institutions. Federated learning (FL) has emerged as a viable solution, enabling collaborative artificial intelligence model development without sharing individual patient data. To effectively implement FL in health care, robust and secure infrastructures are essential. Developing such federated deep learning frameworks is crucial to harnessing the full potential of artificial intelligence while ensuring patient data privacy and regulatory compliance. Objective: The objective is to introduce an innovative FL infrastructure called the Personal Health Train (PHT) that includes the procedural, technical, and governance components needed to implement FL on real-world health care data, including training deep learning neural networks. The study aims to apply this federated deep learning infrastructure to the use case of gross tumor volume segmentation on chest computed tomography images of patients with lung cancer and present the results from a proof-of-concept experiment. Methods: The PHT framework addresses the challenges of data privacy when sharing data, by keeping data close to the source and instead bringing the analysis to the data. Technologically, PHT requires 3 interdependent components: ?tracks? (protected communication channels), ?trains? (containerized software apps), and ?stations? (institutional data repositories), which are supported by the open source ?Vantage6? software. The study applies this federated deep learning infrastructure to the use case of gross tumor volume segmentation on chest computed tomography images of patients with lung cancer, with the introduction of an additional component called the secure aggregation server, where the model averaging is done in a trusted and inaccessible environment. Results: We demonstrated the feasibility of executing deep learning algorithms in a federated manner using PHT and presented the results from a proof-of-concept study. The infrastructure linked 12 hospitals across 8 nations, covering 4 continents, demonstrating the scalability and global reach of the proposed approach. During the execution and training of the deep learning algorithm, no data were shared outside the hospital. Conclusions: The findings of the proof-of-concept study, as well as the implications and limitations of the infrastructure and the results, are discussed. The application of federated deep learning to unstructured medical imaging data, facilitated by the PHT framework and Vantage6 platform, represents a significant advancement in the field. The proposed infrastructure addresses the challenges of data privacy and enables collaborative model development, paving the way for the widespread adoption of deep learning?based tools in the medical domain and beyond. The introduction of the secure aggregation server implied that data leakage problems in FL can be prevented by careful design decisions of the infrastructure. Trial Registration: ClinicalTrials.gov NCT05775068; https://clinicaltrials.gov/study/NCT05775068 UR - https://ai.jmir.org/2025/1/e60847 UR - http://dx.doi.org/10.2196/60847 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/60847 ER - TY - JOUR AU - Jacob, Christine AU - Brasier, Noé AU - Laurenzi, Emanuele AU - Heuss, Sabina AU - Mougiakakou, Stavroula-Georgia AU - Cöltekin, Arzu AU - Peter, K. Marc PY - 2025/2/5 TI - AI for IMPACTS Framework for Evaluating the Long-Term Real-World Impacts of AI-Powered Clinician Tools: Systematic Review and Narrative Synthesis JO - J Med Internet Res SP - e67485 VL - 27 KW - eHealth KW - assessment KW - adoption KW - implementation KW - artificial intelligence KW - clinician KW - efficiency KW - health technology assessment KW - clinical practice N2 - Background: Artificial intelligence (AI) has the potential to revolutionize health care by enhancing both clinical outcomes and operational efficiency. However, its clinical adoption has been slower than anticipated, largely due to the absence of comprehensive evaluation frameworks. Existing frameworks remain insufficient and tend to emphasize technical metrics such as accuracy and validation, while overlooking critical real-world factors such as clinical impact, integration, and economic sustainability. This narrow focus prevents AI tools from being effectively implemented, limiting their broader impact and long-term viability in clinical practice. Objective: This study aimed to create a framework for assessing AI in health care, extending beyond technical metrics to incorporate social and organizational dimensions. The framework was developed by systematically reviewing, analyzing, and synthesizing the evaluation criteria necessary for successful implementation, focusing on the long-term real-world impact of AI in clinical practice. Methods: A search was performed in July 2024 across the PubMed, Cochrane, Scopus, and IEEE Xplore databases to identify relevant studies published in English between January 2019 and mid-July 2024, yielding 3528 results, among which 44 studies met the inclusion criteria. The systematic review followed PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines and the Cochrane Handbook for Systematic Reviews. Data were analyzed using NVivo through thematic analysis and narrative synthesis to identify key emergent themes in the studies. Results: By synthesizing the included studies, we developed a framework that goes beyond the traditional focus on technical metrics or study-level methodologies. It integrates clinical context and real-world implementation factors, offering a more comprehensive approach to evaluating AI tools. With our focus on assessing the long-term real-world impact of AI technologies in health care, we named the framework AI for IMPACTS. The criteria are organized into seven key clusters, each corresponding to a letter in the acronym: (1) I?integration, interoperability, and workflow; (2) M?monitoring, governance, and accountability; (3) P?performance and quality metrics; (4) A?acceptability, trust, and training; (5) C?cost and economic evaluation; (6) T?technological safety and transparency; and (7) S?scalability and impact. These are further broken down into 28 specific subcriteria. Conclusions: The AI for IMPACTS framework offers a holistic approach to evaluate the long-term real-world impact of AI tools in the heterogeneous and challenging health care context and lays the groundwork for further validation through expert consensus and testing of the framework in real-world health care settings. It is important to emphasize that multidisciplinary expertise is essential for assessment, yet many assessors lack the necessary training. In addition, traditional evaluation methods struggle to keep pace with AI?s rapid development. To ensure successful AI integration, flexible, fast-tracked assessment processes and proper assessor training are needed to maintain rigorous standards while adapting to AI?s dynamic evolution. Trial Registration: reviewregistry1859; https://tinyurl.com/ysn2d7sh UR - https://www.jmir.org/2025/1/e67485 UR - http://dx.doi.org/10.2196/67485 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/67485 ER - TY - JOUR AU - Gazquez-Garcia, Javier AU - Sánchez-Bocanegra, Luis Carlos AU - Sevillano, Luis Jose PY - 2025/2/5 TI - AI in the Health Sector: Systematic Review of Key Skills for Future Health Professionals JO - JMIR Med Educ SP - e58161 VL - 11 KW - artificial intelligence KW - healthcare competencies KW - systematic review KW - healthcare education KW - AI regulation N2 - Background: Technological advancements have significantly reshaped health care, introducing digital solutions that enhance diagnostics and patient care. Artificial intelligence (AI) stands out, offering unprecedented capabilities in data analysis, diagnostic support, and personalized medicine. However, effectively integrating AI into health care necessitates specialized competencies among professionals, an area still in its infancy in terms of comprehensive literature and formalized training programs. Objective: This systematic review aims to consolidate the essential skills and knowledge health care professionals need to integrate AI into their clinical practice effectively, according to the published literature. Methods: We conducted a systematic review, across databases PubMed, Scopus, and Web of Science, of peer-reviewed literature that directly explored the required skills for health care professionals to integrate AI into their practice, published in English or Spanish from 2018 onward. Studies that did not refer to specific skills or training in digital health were not included, discarding those that did not directly contribute to understanding the competencies necessary to integrate AI into health care practice. Bias in the examined works was evaluated following Cochrane?s domain-based recommendations. Results: The initial database search yielded a total of 2457 articles. After deleting duplicates and screening titles and abstracts, 37 articles were selected for full-text review. Out of these, only 7 met all the inclusion criteria for this systematic review. The review identified a diverse range of skills and competencies, that we categorized into 14 key areas classified based on their frequency of appearance in the selected studies, including AI fundamentals, data analytics and management, and ethical considerations. Conclusions: Despite the broadening of search criteria to capture the evolving nature of AI in health care, the review underscores a significant gap in focused studies on the required competencies. Moreover, the review highlights the critical role of regulatory bodies such as the US Food and Drug Administration in facilitating the adoption of AI technologies by establishing trust and standardizing algorithms. Key areas were identified for developing competencies among health care professionals for the implementation of AI, including: AI fundamentals knowledge (more focused on assessing the accuracy, reliability, and validity of AI algorithms than on more technical abilities such as programming or mathematics), data analysis skills (including data acquisition, cleaning, visualization, management, and governance), and ethical and legal considerations. In an AI-enhanced health care landscape, the ability to humanize patient care through effective communication is paramount. This balance ensures that while AI streamlines tasks and potentially increases patient interaction time, health care professionals maintain a focus on compassionate care, thereby leveraging AI to enhance, rather than detract from, the patient experience.? UR - https://mededu.jmir.org/2025/1/e58161 UR - http://dx.doi.org/10.2196/58161 ID - info:doi/10.2196/58161 ER - TY - JOUR AU - Werder, Karl AU - Cao, Lan AU - Park, Hee Eun AU - Ramesh, Balasubramaniam PY - 2025/1/31 TI - Why AI Monitoring Faces Resistance and What Healthcare Organizations Can Do About It: An Emotion-Based Perspective JO - J Med Internet Res SP - e51785 VL - 27 KW - artificial intelligence KW - AI monitoring KW - emotion KW - resistance KW - health care UR - https://www.jmir.org/2025/1/e51785 UR - http://dx.doi.org/10.2196/51785 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/51785 ER - TY - JOUR AU - Eysenbach, Gunther PY - 2025/1/22 TI - Crisis Text Line and Loris.ai Controversy Highlights the Complexity of Informed Consent on the Internet and Data-Sharing Ethics for Machine Learning and Research JO - J Med Internet Res SP - e67878 VL - 27 KW - data ethics KW - data sharing KW - informed consent KW - disclosure KW - conflict of interest KW - transparency KW - trust UR - https://www.jmir.org/2025/1/e67878 UR - http://dx.doi.org/10.2196/67878 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/67878 ER - TY - JOUR AU - Bazzano, N. Alessandra AU - Mantsios, Andrea AU - Mattei, Nicholas AU - Kosorok, R. Michael AU - Culotta, Aron PY - 2025/1/22 TI - AI Can Be a Powerful Social Innovation for Public Health if Community Engagement Is at the Core JO - J Med Internet Res SP - e68198 VL - 27 KW - Artificial Intelligence KW - Generative Artificial Intelligence KW - Citizen Science KW - Community Participation KW - Innovation Diffusion UR - https://www.jmir.org/2025/1/e68198 UR - http://dx.doi.org/10.2196/68198 UR - http://www.ncbi.nlm.nih.gov/pubmed/39841529 ID - info:doi/10.2196/68198 ER - TY - JOUR AU - Sasseville, Maxime AU - Ouellet, Steven AU - Rhéaume, Caroline AU - Sahlia, Malek AU - Couture, Vincent AU - Després, Philippe AU - Paquette, Jean-Sébastien AU - Darmon, David AU - Bergeron, Frédéric AU - Gagnon, Marie-Pierre PY - 2025/1/7 TI - Bias Mitigation in Primary Health Care Artificial Intelligence Models: Scoping Review JO - J Med Internet Res SP - e60269 VL - 27 KW - artificial intelligence KW - AI KW - algorithms KW - expert system KW - decision support KW - bias KW - community health services KW - primary health care KW - health disparities KW - social equity KW - scoping review N2 - Background: Artificial intelligence (AI) predictive models in primary health care have the potential to enhance population health by rapidly and accurately identifying individuals who should receive care and health services. However, these models also carry the risk of perpetuating or amplifying existing biases toward diverse groups. We identified a gap in the current understanding of strategies used to assess and mitigate bias in primary health care algorithms related to individuals? personal or protected attributes. Objective: This study aimed to describe the attempts, strategies, and methods used to mitigate bias in AI models within primary health care, to identify the diverse groups or protected attributes considered, and to evaluate the results of these approaches on both bias reduction and AI model performance. Methods: We conducted a scoping review following Joanna Briggs Institute (JBI) guidelines, searching Medline (Ovid), CINAHL (EBSCO), PsycINFO (Ovid), and Web of Science databases for studies published between January 1, 2017, and November 15, 2022. Pairs of reviewers independently screened titles and abstracts, applied selection criteria, and performed full-text screening. Discrepancies regarding study inclusion were resolved by consensus. Following reporting standards for AI in health care, we extracted data on study objectives, model features, targeted diverse groups, mitigation strategies used, and results. Using the mixed methods appraisal tool, we appraised the quality of the studies. Results: After removing 585 duplicates, we screened 1018 titles and abstracts. From the remaining 189 full-text articles, we included 17 studies. The most frequently investigated protected attributes were race (or ethnicity), examined in 12 of the 17 studies, and sex (often identified as gender), typically classified as ?male versus female? in 10 of the studies. We categorized bias mitigation approaches into four clusters: (1) modifying existing AI models or datasets, (2) sourcing data from electronic health records, (3) developing tools with a ?human-in-the-loop? approach, and (4) identifying ethical principles for informed decision-making. Algorithmic preprocessing methods, such as relabeling and reweighing data, along with natural language processing techniques that extract data from unstructured notes, showed the greatest potential for bias mitigation. Other methods aimed at enhancing model fairness included group recalibration and the application of the equalized odds metric. However, these approaches sometimes exacerbated prediction errors across groups or led to overall model miscalibrations. Conclusions: The results suggest that biases toward diverse groups are more easily mitigated when data are open-sourced, multiple stakeholders are engaged, and during the algorithm?s preprocessing stage. Further empirical studies that include a broader range of groups, such as Indigenous peoples in Canada, are needed to validate and expand upon these findings. Trial Registration: OSF Registry osf.io/9ngz5/; https://osf.io/9ngz5/ International Registered Report Identifier (IRRID): RR2-10.2196/46684 UR - https://www.jmir.org/2025/1/e60269 UR - http://dx.doi.org/10.2196/60269 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/60269 ER - TY - JOUR AU - O'Malley, Andrew AU - Veenhuizen, Miriam AU - Ahmed, Ayla PY - 2024/11/27 TI - Ensuring Appropriate Representation in Artificial Intelligence?Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias JO - JMIR AI SP - e58275 VL - 3 KW - artificial intelligence KW - generative AI KW - AI images KW - dermatology KW - anatomy KW - medical education KW - medical imaging KW - skin KW - skin tone KW - United States KW - educational material KW - psoriasis KW - digital imagery N2 - Background: In medical education, particularly in anatomy and dermatology, generative artificial intelligence (AI) can be used to create customized illustrations. However, the underrepresentation of darker skin tones in medical textbooks and elsewhere, which serve as training data for AI, poses a significant challenge in ensuring diverse and inclusive educational materials. Objective: This study aims to evaluate the extent of skin tone diversity in AI-generated medical images and to test whether the representation of skin tones can be improved by modifying AI prompts to better reflect the demographic makeup of the US population. Methods: In total, 2 standard AI models (Dall-E [OpenAI] and Midjourney [Midjourney Inc]) each generated 100 images of people with psoriasis. In addition, a custom model was developed that incorporated a prompt injection aimed at ?forcing? the AI (Dall-E 3) to reflect the skin tone distribution of the US population according to the 2012 American National Election Survey. This custom model generated another set of 100 images. The skin tones in these images were assessed by 3 researchers using the New Immigrant Survey skin tone scale, with the median value representing each image. A chi-square goodness of fit analysis compared the skin tone distributions from each set of images to that of the US population. Results: The standard AI models (Dalle-3 and Midjourney) demonstrated a significant difference between the expected skin tones of the US population and the observed tones in the generated images (P<.001). Both standard AI models overrepresented lighter skin. Conversely, the custom model with the modified prompt yielded a distribution of skin tones that closely matched the expected demographic representation, showing no significant difference (P=.04). Conclusions: This study reveals a notable bias in AI-generated medical images, predominantly underrepresenting darker skin tones. This bias can be effectively addressed by modifying AI prompts to incorporate real-life demographic distributions. The findings emphasize the need for conscious efforts in AI development to ensure diverse and representative outputs, particularly in educational and medical contexts. Users of generative AI tools should be aware that these biases exist, and that similar tendencies may also exist in other types of generative AI (eg, large language models) and in other characteristics (eg, sex, gender, culture, and ethnicity). Injecting demographic data into AI prompts may effectively counteract these biases, ensuring a more accurate representation of the general population. UR - https://ai.jmir.org/2024/1/e58275 UR - http://dx.doi.org/10.2196/58275 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/58275 ER - TY - JOUR AU - Ralevski, Alexandra AU - Taiyab, Nadaa AU - Nossal, Michael AU - Mico, Lindsay AU - Piekos, Samantha AU - Hadlock, Jennifer PY - 2024/11/19 TI - Using Large Language Models to Abstract Complex Social Determinants of Health From Original and Deidentified Medical Notes: Development and Validation Study JO - J Med Internet Res SP - e63445 VL - 26 KW - housing instability KW - housing insecurity KW - housing KW - machine learning KW - artificial intelligence KW - AI KW - large language model KW - LLM KW - natural language processing KW - NLP KW - electronic health record KW - EHR KW - electronic medical record KW - EMR KW - social determinants of health KW - exposome KW - pregnancy KW - obstetric KW - deidentification N2 - Background: Social determinants of health (SDoH) such as housing insecurity are known to be intricately linked to patients? health status. More efficient methods for abstracting structured data on SDoH can help accelerate the inclusion of exposome variables in biomedical research and support health care systems in identifying patients who could benefit from proactive outreach. Large language models (LLMs) developed from Generative Pre-trained Transformers (GPTs) have shown potential for performing complex abstraction tasks on unstructured clinical notes. Objective: Here, we assess the performance of GPTs on identifying temporal aspects of housing insecurity and compare results between both original and deidentified notes. Methods: We compared the ability of GPT-3.5 and GPT-4 to identify instances of both current and past housing instability, as well as general housing status, from 25,217 notes from 795 pregnant women. Results were compared with manual abstraction, a named entity recognition model, and regular expressions. Results: Compared with GPT-3.5 and the named entity recognition model, GPT-4 had the highest performance and had a much higher recall (0.924) than human abstractors (0.702) in identifying patients experiencing current or past housing instability, although precision was lower (0.850) compared with human abstractors (0.971). GPT-4?s precision improved slightly (0.936 original, 0.939 deidentified) on deidentified versions of the same notes, while recall dropped (0.781 original, 0.704 deidentified). Conclusions: This work demonstrates that while manual abstraction is likely to yield slightly more accurate results overall, LLMs can provide a scalable, cost-effective solution with the advantage of greater recall. This could support semiautomated abstraction, but given the potential risk for harm, human review would be essential before using results for any patient engagement or care decisions. Furthermore, recall was lower when notes were deidentified prior to LLM abstraction. UR - https://www.jmir.org/2024/1/e63445 UR - http://dx.doi.org/10.2196/63445 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/63445 ER - TY - JOUR AU - Chustecki, Margaret PY - 2024/11/18 TI - Benefits and Risks of AI in Health Care: Narrative Review JO - Interact J Med Res SP - e53616 VL - 13 KW - artificial intelligence KW - safety risks KW - biases KW - AI KW - benefit KW - risk KW - health care KW - safety KW - ethics KW - transparency KW - data privacy KW - accuracy N2 - Background: The integration of artificial intelligence (AI) into health care has the potential to transform the industry, but it also raises ethical, regulatory, and safety concerns. This review paper provides an in-depth examination of the benefits and risks associated with AI in health care, with a focus on issues like biases, transparency, data privacy, and safety. Objective: This study aims to evaluate the advantages and drawbacks of incorporating AI in health care. This assessment centers on the potential biases in AI algorithms, transparency challenges, data privacy issues, and safety risks in health care settings. Methods: Studies included in this review were selected based on their relevance to AI applications in health care, focusing on ethical, regulatory, and safety considerations. Inclusion criteria encompassed peer-reviewed articles, reviews, and relevant research papers published in English. Exclusion criteria included non?peer-reviewed articles, editorials, and studies not directly related to AI in health care. A comprehensive literature search was conducted across 8 databases: OVID MEDLINE, OVID Embase, OVID PsycINFO, EBSCO CINAHL Plus with Full Text, ProQuest Sociological Abstracts, ProQuest Philosopher?s Index, ProQuest Advanced Technologies & Aerospace, and Wiley Cochrane Library. The search was last updated on June 23, 2023. Results were synthesized using qualitative methods to identify key themes and findings related to the benefits and risks of AI in health care. Results: The literature search yielded 8796 articles. After removing duplicates and applying the inclusion and exclusion criteria, 44 studies were included in the qualitative synthesis. This review highlights the significant promise that AI holds in health care, such as enhancing health care delivery by providing more accurate diagnoses, personalized treatment plans, and efficient resource allocation. However, persistent concerns remain, including biases ingrained in AI algorithms, a lack of transparency in decision-making, potential compromises of patient data privacy, and safety risks associated with AI implementation in clinical settings. Conclusions: In conclusion, while AI presents the opportunity for a health care revolution, it is imperative to address the ethical, regulatory, and safety challenges linked to its integration. Proactive measures are required to ensure that AI technologies are developed and deployed responsibly, striking a balance between innovation and the safeguarding of patient well-being. UR - https://www.i-jmr.org/2024/1/e53616 UR - http://dx.doi.org/10.2196/53616 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/53616 ER - TY - JOUR AU - Abbasgholizadeh Rahimi, Samira AU - Shrivastava, Richa AU - Brown-Johnson, Anita AU - Caidor, Pascale AU - Davies, Claire AU - Idrissi Janati, Amal AU - Kengne Talla, Pascaline AU - Madathil, Sreenath AU - Willie, M. Bettina AU - Emami, Elham PY - 2024/11/15 TI - EDAI Framework for Integrating Equity, Diversity, and Inclusion Throughout the Lifecycle of AI to Improve Health and Oral Health Care: Qualitative Study JO - J Med Internet Res SP - e63356 VL - 26 KW - equity, diversity, and inclusion KW - EDI KW - health care KW - oral health care KW - machine learning KW - artificial intelligence KW - AI N2 - Background: Recent studies have identified significant gaps in equity, diversity, and inclusion (EDI) considerations within the lifecycle of artificial intelligence (AI), spanning from data collection and problem definition to implementation stages. Despite the recognized need for integrating EDI principles, there is currently no existing guideline or framework to support this integration in the AI lifecycle. Objective: This study aimed to address this gap by identifying EDI principles and indicators to be integrated into the AI lifecycle. The goal was to develop a comprehensive guiding framework to guide the development and implementation of future AI systems. Methods: This study was conducted in 3 phases. In phase 1, a comprehensive systematic scoping review explored how EDI principles have been integrated into AI in health and oral health care settings. In phase 2, a multidisciplinary team was established, and two 2-day, in-person international workshops with over 60 representatives from diverse backgrounds, expertise, and communities were conducted. The workshops included plenary presentations, round table discussions, and focused group discussions. In phase 3, based on the workshops? insights, the EDAI framework was developed and refined through iterative feedback from participants. The results of the initial systematic scoping review have been published separately, and this paper focuses on subsequent phases of the project, which is related to framework development. Results: In this study, we developed the EDAI framework, a comprehensive guideline that integrates EDI principles and indicators throughout the entire AI lifecycle. This framework addresses existing gaps at various stages, from data collection to implementation, and focuses on individual, organizational, and systemic levels. Additionally, we identified both the facilitators and barriers to integrating EDI within the AI lifecycle in health and oral health care. Conclusions: The developed EDAI framework provides a comprehensive, actionable guideline for integrating EDI principles into AI development and deployment. By facilitating the systematic incorporation of these principles, the framework supports the creation and implementation of AI systems that are not only technologically advanced but also sensitive to EDI principles. UR - https://www.jmir.org/2024/1/e63356 UR - http://dx.doi.org/10.2196/63356 UR - http://www.ncbi.nlm.nih.gov/pubmed/39546793 ID - info:doi/10.2196/63356 ER - TY - JOUR AU - Wang, Leyao AU - Wan, Zhiyu AU - Ni, Congning AU - Song, Qingyuan AU - Li, Yang AU - Clayton, Ellen AU - Malin, Bradley AU - Yin, Zhijun PY - 2024/11/7 TI - Applications and Concerns of ChatGPT and Other Conversational Large Language Models in Health Care: Systematic Review JO - J Med Internet Res SP - e22769 VL - 26 KW - large language model KW - ChatGPT KW - artificial intelligence KW - natural language processing KW - health care KW - summarization KW - medical knowledge inquiry KW - reliability KW - bias KW - privacy N2 - Background: The launch of ChatGPT (OpenAI) in November 2022 attracted public attention and academic interest to large language models (LLMs), facilitating the emergence of many other innovative LLMs. These LLMs have been applied in various fields, including health care. Numerous studies have since been conducted regarding how to use state-of-the-art LLMs in health-related scenarios. Objective: This review aims to summarize applications of and concerns regarding conversational LLMs in health care and provide an agenda for future research in this field. Methods: We used PubMed, ACM, and the IEEE digital libraries as primary sources for this review. We followed the guidance of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) to screen and select peer-reviewed research articles that (1) were related to health care applications and conversational LLMs and (2) were published before September 1, 2023, the date when we started paper collection. We investigated these papers and classified them according to their applications and concerns. Results: Our search initially identified 820 papers according to targeted keywords, out of which 65 (7.9%) papers met our criteria and were included in the review. The most popular conversational LLM was ChatGPT (60/65, 92% of papers), followed by Bard (Google LLC; 1/65, 2% of papers), LLaMA (Meta; 1/65, 2% of papers), and other LLMs (6/65, 9% papers). These papers were classified into four categories of applications: (1) summarization, (2) medical knowledge inquiry, (3) prediction (eg, diagnosis, treatment recommendation, and drug synergy), and (4) administration (eg, documentation and information collection), and four categories of concerns: (1) reliability (eg, training data quality, accuracy, interpretability, and consistency in responses), (2) bias, (3) privacy, and (4) public acceptability. There were 49 (75%) papers using LLMs for either summarization or medical knowledge inquiry, or both, and there are 58 (89%) papers expressing concerns about either reliability or bias, or both. We found that conversational LLMs exhibited promising results in summarization and providing general medical knowledge to patients with a relatively high accuracy. However, conversational LLMs such as ChatGPT are not always able to provide reliable answers to complex health-related tasks (eg, diagnosis) that require specialized domain expertise. While bias or privacy issues are often noted as concerns, no experiments in our reviewed papers thoughtfully examined how conversational LLMs lead to these issues in health care research. Conclusions: Future studies should focus on improving the reliability of LLM applications in complex health-related tasks, as well as investigating the mechanisms of how LLM applications bring bias and privacy issues. Considering the vast accessibility of LLMs, legal, social, and technical efforts are all needed to address concerns about LLMs to promote, improve, and regularize the application of LLMs in health care. UR - https://www.jmir.org/2024/1/e22769 UR - http://dx.doi.org/10.2196/22769 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/22769 ER - TY - JOUR AU - Rosenbacke, Rikard AU - Melhus, Åsa AU - McKee, Martin AU - Stuckler, David PY - 2024/10/30 TI - How Explainable Artificial Intelligence Can Increase or Decrease Clinicians? Trust in AI Applications in Health Care: Systematic Review JO - JMIR AI SP - e53207 VL - 3 KW - explainable artificial intelligence KW - XAI KW - trustworthy AI KW - clinician trust KW - affect-based measures KW - cognitive measures KW - clinical use KW - clinical decision-making KW - clinical informatics N2 - Background: Artificial intelligence (AI) has significant potential in clinical practice. However, its ?black box? nature can lead clinicians to question its value. The challenge is to create sufficient trust for clinicians to feel comfortable using AI, but not so much that they defer to it even when it produces results that conflict with their clinical judgment in ways that lead to incorrect decisions. Explainable AI (XAI) aims to address this by providing explanations of how AI algorithms reach their conclusions. However, it remains unclear whether such explanations foster an appropriate degree of trust to ensure the optimal use of AI in clinical practice. Objective: This study aims to systematically review and synthesize empirical evidence on the impact of XAI on clinicians? trust in AI-driven clinical decision-making. Methods: A systematic review was conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, searching PubMed and Web of Science databases. Studies were included if they empirically measured the impact of XAI on clinicians? trust using cognition- or affect-based measures. Out of 778 articles screened, 10 met the inclusion criteria. We assessed the risk of bias using standard tools appropriate to the methodology of each paper. Results: The risk of bias in all papers was moderate or moderate to high. All included studies operationalized trust primarily through cognitive-based definitions, with 2 also incorporating affect-based measures. Out of these, 5 studies reported that XAI increased clinicians? trust compared with standard AI, particularly when the explanations were clear, concise, and relevant to clinical practice. In addition, 3 studies found no significant effect of XAI on trust, and the presence of explanations does not automatically improve trust. Notably, 2 studies highlighted that XAI could either enhance or diminish trust, depending on the complexity and coherence of the provided explanations. The majority of studies suggest that XAI has the potential to enhance clinicians? trust in recommendations generated by AI. However, complex or contradictory explanations can undermine this trust. More critically, trust in AI is not inherently beneficial, as AI recommendations are not infallible. These findings underscore the nuanced role of explanation quality and suggest that trust can be modulated through the careful design of XAI systems. Conclusions: Excessive trust in incorrect advice generated by AI can adversely impact clinical accuracy, just as can happen when correct advice is distrusted. Future research should focus on refining both cognitive and affect-based measures of trust and on developing strategies to achieve an appropriate balance in terms of trust, preventing both blind trust and undue skepticism. Optimizing trust in AI systems is essential for their effective integration into clinical practice. UR - https://ai.jmir.org/2024/1/e53207 UR - http://dx.doi.org/10.2196/53207 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/53207 ER - TY - JOUR AU - Ball Dunlap, A. Patricia AU - Michalowski, Martin PY - 2024/10/25 TI - Advancing AI Data Ethics in Nursing: Future Directions for Nursing Practice, Research, and Education JO - JMIR Nursing SP - e62678 VL - 7 KW - artificial intelligence KW - AI data ethics KW - data-centric AI KW - nurses KW - nursing informatics KW - machine learning KW - data literacy KW - health care AI KW - responsible AI UR - https://nursing.jmir.org/2024/1/e62678 UR - http://dx.doi.org/10.2196/62678 ID - info:doi/10.2196/62678 ER - TY - JOUR AU - Elyoseph, Zohar AU - Gur, Tamar AU - Haber, Yuval AU - Simon, Tomer AU - Angert, Tal AU - Navon, Yuval AU - Tal, Amir AU - Asman, Oren PY - 2024/10/17 TI - An Ethical Perspective on the Democratization of Mental Health With Generative AI JO - JMIR Ment Health SP - e58011 VL - 11 KW - ethics KW - generative artificial intelligence KW - generative AI KW - mental health KW - ChatGPT KW - large language model KW - LLM KW - digital mental health KW - machine learning KW - AI KW - technology KW - accessibility KW - knowledge KW - GenAI UR - https://mental.jmir.org/2024/1/e58011 UR - http://dx.doi.org/10.2196/58011 ID - info:doi/10.2196/58011 ER - TY - JOUR AU - Germani, Federico AU - Spitale, Giovanni AU - Biller-Andorno, Nikola PY - 2024/10/15 TI - The Dual Nature of AI in Information Dissemination: Ethical Considerations JO - JMIR AI SP - e53505 VL - 3 KW - AI KW - bioethics KW - infodemic management KW - disinformation KW - artificial intelligence KW - ethics KW - ethical KW - infodemic KW - infodemics KW - public health KW - misinformation KW - information dissemination KW - information literacy UR - https://ai.jmir.org/2024/1/e53505 UR - http://dx.doi.org/10.2196/53505 UR - http://www.ncbi.nlm.nih.gov/pubmed/39405099 ID - info:doi/10.2196/53505 ER - TY - JOUR AU - Tavory, Tamar PY - 2024/9/19 TI - Regulating AI in Mental Health: Ethics of Care Perspective JO - JMIR Ment Health SP - e58493 VL - 11 KW - artificial intelligence KW - ethics of care KW - regulation KW - legal KW - relationship KW - mental health KW - mental healthcare KW - AI KW - ethic KW - ethics KW - ethical KW - regulations KW - law KW - framework KW - frameworks KW - regulatory KW - relationships KW - chatbot KW - chatbots KW - conversational agent KW - conversational agents KW - European Artificial Intelligence Act UR - https://mental.jmir.org/2024/1/e58493 UR - http://dx.doi.org/10.2196/58493 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/58493 ER - TY - JOUR AU - Lorenzini, Giorgia AU - Arbelaez Ossa, Laura AU - Milford, Stephen AU - Elger, Simone Bernice AU - Shaw, Martin David AU - De Clercq, Eva PY - 2024/8/19 TI - The ?Magical Theory? of AI in Medicine: Thematic Narrative Analysis JO - JMIR AI SP - e49795 VL - 3 KW - artificial intelligence KW - medicine KW - physicians KW - hype KW - narratives KW - qualitative research N2 - Background: The discourse surrounding medical artificial intelligence (AI) often focuses on narratives that either hype the technology?s potential or predict dystopian futures. AI narratives have a significant influence on the direction of research, funding, and public opinion and thus shape the future of medicine. Objective: The paper aims to offer critical reflections on AI narratives, with a specific focus on medical AI, and to raise awareness as to how people working with medical AI talk about AI and discharge their ?narrative responsibility.? Methods: Qualitative semistructured interviews were conducted with 41 participants from different disciplines who were exposed to medical AI in their profession. The research represents a secondary analysis of data using a thematic narrative approach. The analysis resulted in 2 main themes, each with 2 other subthemes. Results: Stories about the AI-physician interaction depicted either a competitive or collaborative relationship. Some participants argued that AI might replace physicians, as it performs better than physicians. However, others believed that physicians should not be replaced and that AI should rather assist and support physicians. The idea of excessive technological deferral and automation bias was discussed, highlighting the risk of ?losing? decisional power. The possibility that AI could relieve physicians from burnout and allow them to spend more time with patients was also considered. Finally, a few participants reported an extremely optimistic account of medical AI, while the majority criticized this type of story. The latter lamented the existence of a ?magical theory? of medical AI, identified with techno-solutionist positions. Conclusions: Most of the participants reported a nuanced view of technology, recognizing both its benefits and challenges and avoiding polarized narratives. However, some participants did contribute to the hype surrounding medical AI, comparing it to human capabilities and depicting it as superior. Overall, the majority agreed that medical AI should assist rather than replace clinicians. The study concludes that a balanced narrative (that focuses on the technology?s present capabilities and limitations) is necessary to fully realize the potential of medical AI while avoiding unrealistic expectations and hype. UR - https://ai.jmir.org/2024/1/e49795 UR - http://dx.doi.org/10.2196/49795 UR - http://www.ncbi.nlm.nih.gov/pubmed/39158953 ID - info:doi/10.2196/49795 ER - TY - JOUR AU - Han, Yu AU - Ceross, Aaron AU - Bergmann, Jeroen PY - 2024/7/29 TI - Regulatory Frameworks for AI-Enabled Medical Device Software in China: Comparative Analysis and Review of Implications for Global Manufacturer JO - JMIR AI SP - e46871 VL - 3 KW - NMPA KW - medical device software KW - device registration KW - registration pathway KW - artificial intelligence KW - machine learning KW - medical device KW - device development KW - China KW - regulations KW - medical software UR - https://ai.jmir.org/2024/1/e46871 UR - http://dx.doi.org/10.2196/46871 UR - http://www.ncbi.nlm.nih.gov/pubmed/39073860 ID - info:doi/10.2196/46871 ER - TY - JOUR AU - Bragazzi, Luigi Nicola AU - Garbarino, Sergio PY - 2024/6/7 TI - Toward Clinical Generative AI: Conceptual Framework JO - JMIR AI SP - e55957 VL - 3 KW - clinical intelligence KW - artificial intelligence KW - iterative process KW - abduction KW - benchmarking KW - verification paradigms UR - https://ai.jmir.org/2024/1/e55957 UR - http://dx.doi.org/10.2196/55957 UR - http://www.ncbi.nlm.nih.gov/pubmed/38875592 ID - info:doi/10.2196/55957 ER - TY - JOUR AU - Jordan, Alexis AU - Park, Albert PY - 2024/6/3 TI - Understanding the Long Haulers of COVID-19: Mixed Methods Analysis of YouTube Content JO - JMIR AI SP - e54501 VL - 3 KW - long haulers KW - post?COVID-19 condition KW - COVID-19 KW - YouTube KW - topic modeling KW - natural language processing N2 - Background: The COVID-19 pandemic had a devastating global impact. In the United States, there were >98 million COVID-19 cases and >1 million resulting deaths. One consequence of COVID-19 infection has been post?COVID-19 condition (PCC). People with this syndrome, colloquially called long haulers, experience symptoms that impact their quality of life. The root cause of PCC and effective treatments remains unknown. Many long haulers have turned to social media for support and guidance. Objective: In this study, we sought to gain a better understanding of the long hauler experience by investigating what has been discussed and how information about long haulers is perceived on social media. We specifically investigated the following: (1) the range of symptoms that are discussed, (2) the ways in which information about long haulers is perceived, (3) informational and emotional support that is available to long haulers, and (4) discourse between viewers and creators. We selected YouTube as our data source due to its popularity and wide range of audience. Methods: We systematically gathered data from 3 different types of content creators: medical sources, news sources, and long haulers. To computationally understand the video content and viewers? reactions, we used Biterm, a topic modeling algorithm created specifically for short texts, to analyze snippets of video transcripts and all top-level comments from the comment section. To triangulate our findings about viewers? reactions, we used the Valence Aware Dictionary and Sentiment Reasoner to conduct sentiment analysis on comments from each type of content creator. We grouped the comments into positive and negative categories and generated topics for these groups using Biterm. We then manually grouped resulting topics into broader themes for the purpose of analysis. Results: We organized the resulting topics into 28 themes across all sources. Examples of medical source transcript themes were Explanations in layman?s terms and Biological explanations. Examples of news source transcript themes were Negative experiences and handling the long haul. The 2 long hauler transcript themes were Taking treatments into own hands and Changes to daily life. News sources received a greater share of negative comments. A few themes of these negative comments included Misinformation and disinformation and Issues with the health care system. Similarly, negative long hauler comments were organized into several themes, including Disillusionment with the health care system and Requiring more visibility. In contrast, positive medical source comments captured themes such as Appreciation of helpful content and Exchange of helpful information. In addition to this theme, one positive theme found in long hauler comments was Community building. Conclusions: The results of this study could help public health agencies, policy makers, organizations, and health researchers understand symptomatology and experiences related to PCC. They could also help these agencies develop their communication strategy concerning PCC. UR - https://ai.jmir.org/2024/1/e54501 UR - http://dx.doi.org/10.2196/54501 UR - http://www.ncbi.nlm.nih.gov/pubmed/38875666 ID - info:doi/10.2196/54501 ER - TY - JOUR AU - Quttainah, Majdi AU - Mishra, Vinaytosh AU - Madakam, Somayya AU - Lurie, Yotam AU - Mark, Shlomo PY - 2024/4/23 TI - Cost, Usability, Credibility, Fairness, Accountability, Transparency, and Explainability Framework for Safe and Effective Large Language Models in Medical Education: Narrative Review and Qualitative Study JO - JMIR AI SP - e51834 VL - 3 KW - large language model KW - LLM KW - ChatGPT KW - CUC-FATE framework KW - cost, usability, credibility, fairness, accountability, transparency, and explainability KW - analytical hierarchy process KW - AHP KW - total interpretive structural modeling KW - TISM KW - medical education KW - adoption KW - guideline KW - development KW - health care KW - chat generative pretrained transformer KW - generative language model tool KW - user KW - innovation KW - data generation KW - narrative review KW - health care professional N2 - Background: The world has witnessed increased adoption of large language models (LLMs) in the last year. Although the products developed using LLMs have the potential to solve accessibility and efficiency problems in health care, there is a lack of available guidelines for developing LLMs for health care, especially for medical education. Objective: The aim of this study was to identify and prioritize the enablers for developing successful LLMs for medical education. We further evaluated the relationships among these identified enablers. Methods: A narrative review of the extant literature was first performed to identify the key enablers for LLM development. We additionally gathered the opinions of LLM users to determine the relative importance of these enablers using an analytical hierarchy process (AHP), which is a multicriteria decision-making method. Further, total interpretive structural modeling (TISM) was used to analyze the perspectives of product developers and ascertain the relationships and hierarchy among these enablers. Finally, the cross-impact matrix-based multiplication applied to a classification (MICMAC) approach was used to determine the relative driving and dependence powers of these enablers. A nonprobabilistic purposive sampling approach was used for recruitment of focus groups. Results: The AHP demonstrated that the most important enabler for LLMs was credibility, with a priority weight of 0.37, followed by accountability (0.27642) and fairness (0.10572). In contrast, usability, with a priority weight of 0.04, showed negligible importance. The results of TISM concurred with the findings of the AHP. The only striking difference between expert perspectives and user preference evaluation was that the product developers indicated that cost has the least importance as a potential enabler. The MICMAC analysis suggested that cost has a strong influence on other enablers. The inputs of the focus group were found to be reliable, with a consistency ratio less than 0.1 (0.084). Conclusions: This study is the first to identify, prioritize, and analyze the relationships of enablers of effective LLMs for medical education. Based on the results of this study, we developed a comprehendible prescriptive framework, named CUC-FATE (Cost, Usability, Credibility, Fairness, Accountability, Transparency, and Explainability), for evaluating the enablers of LLMs in medical education. The study findings are useful for health care professionals, health technology experts, medical technology regulators, and policy makers. UR - https://ai.jmir.org/2024/1/e51834 UR - http://dx.doi.org/10.2196/51834 UR - http://www.ncbi.nlm.nih.gov/pubmed/38875562 ID - info:doi/10.2196/51834 ER - TY - JOUR AU - Waheed, Atif Muhammad AU - Liu, Lu PY - 2024/4/17 TI - Perceptions of Family Physicians About Applying AI in Primary Health Care: Case Study From a Premier Health Care Organization JO - JMIR AI SP - e40781 VL - 3 KW - AI KW - artificial intelligence KW - perception KW - attitude KW - opinion KW - surveys and questionnaires KW - family physician KW - primary care KW - health care service provider KW - health care professional KW - ethical KW - AI decision-making KW - AI challenges N2 - Background: The COVID-19 pandemic has led to the rapid proliferation of artificial intelligence (AI), which was not previously anticipated; this is an unforeseen development. The use of AI in health care settings is increasing, as it proves to be a promising tool for transforming health care systems, improving operational and business processes, and efficiently simplifying health care tasks for family physicians and health care administrators. Therefore, it is necessary to assess the perspective of family physicians on AI and its impact on their job roles. Objective: This study aims to determine the impact of AI on the management and practices of Qatar?s Primary Health Care Corporation (PHCC) in improving health care tasks and service delivery. Furthermore, it seeks to evaluate the impact of AI on family physicians? job roles, including associated risks and ethical ramifications from their perspective. Methods: We conducted a cross-sectional survey and sent a web-based questionnaire survey link to 724 practicing family physicians at the PHCC. In total, we received 102 eligible responses. Results: Of the 102 respondents, 72 (70.6%) were men and 94 (92.2%) were aged between 35 and 54 years. In addition, 58 (56.9%) of the 102 respondents were consultants. The overall awareness of AI was 80 (78.4%) out of 102, with no difference between gender (P=.06) and age groups (P=.12). AI is perceived to play a positive role in improving health care practices at PHCC (P<.001), managing health care tasks (P<.001), and positively impacting health care service delivery (P<.001). Family physicians also perceived that their clinical, administrative, and opportunistic health care management roles were positively influenced by AI (P<.001). Furthermore, perceptions of family physicians indicate that AI improves operational and human resource management (P<.001), does not undermine patient-physician relationships (P<.001), and is not considered superior to human physicians in the clinical judgment process (P<.001). However, its inclusion is believed to decrease patient satisfaction (P<.001). AI decision-making and accountability were recognized as ethical risks, along with data protection and confidentiality. The optimism regarding using AI for future medical decisions was low among family physicians. Conclusions: This study indicated a positive perception among family physicians regarding AI integration into primary care settings. AI demonstrates significant potential for enhancing health care task management and overall service delivery at the PHCC. It augments family physicians? roles without replacing them and proves beneficial for operational efficiency, human resource management, and public health during pandemics. While the implementation of AI is anticipated to bring benefits, the careful consideration of ethical, privacy, confidentiality, and patient-centric concerns is essential. These insights provide valuable guidance for the strategic integration of AI into health care systems, with a focus on maintaining high-quality patient care and addressing the multifaceted challenges that arise during this transformative process. UR - https://ai.jmir.org/2024/1/e40781 UR - http://dx.doi.org/10.2196/40781 UR - http://www.ncbi.nlm.nih.gov/pubmed/38875531 ID - info:doi/10.2196/40781 ER - TY - JOUR AU - Späth, Julian AU - Sewald, Zeno AU - Probul, Niklas AU - Berland, Magali AU - Almeida, Mathieu AU - Pons, Nicolas AU - Le Chatelier, Emmanuelle AU - Ginès, Pere AU - Solé, Cristina AU - Juanola, Adrià AU - Pauling, Josch AU - Baumbach, Jan PY - 2024/3/29 TI - Privacy-Preserving Federated Survival Support Vector Machines for Cross-Institutional Time-To-Event Analysis: Algorithm Development and Validation JO - JMIR AI SP - e47652 VL - 3 KW - federated learning KW - survival analysis KW - support vector machine KW - machine learning KW - federated KW - algorithm KW - survival KW - FeatureCloud KW - predict KW - predictive KW - prediction KW - predictions KW - Implementation science KW - Implementation KW - centralized model KW - privacy regulation N2 - Background: Central collection of distributed medical patient data is problematic due to strict privacy regulations. Especially in clinical environments, such as clinical time-to-event studies, large sample sizes are critical but usually not available at a single institution. It has been shown recently that federated learning, combined with privacy-enhancing technologies, is an excellent and privacy-preserving alternative to data sharing. Objective: This study aims to develop and validate a privacy-preserving, federated survival support vector machine (SVM) and make it accessible for researchers to perform cross-institutional time-to-event analyses. Methods: We extended the survival SVM algorithm to be applicable in federated environments. We further implemented it as a FeatureCloud app, enabling it to run in the federated infrastructure provided by the FeatureCloud platform. Finally, we evaluated our algorithm on 3 benchmark data sets, a large sample size synthetic data set, and a real-world microbiome data set and compared the results to the corresponding central method. Results: Our federated survival SVM produces highly similar results to the centralized model on all data sets. The maximal difference between the model weights of the central model and the federated model was only 0.001, and the mean difference over all data sets was 0.0002. We further show that by including more data in the analysis through federated learning, predictions are more accurate even in the presence of site-dependent batch effects. Conclusions: The federated survival SVM extends the palette of federated time-to-event analysis methods by a robust machine learning approach. To our knowledge, the implemented FeatureCloud app is the first publicly available implementation of a federated survival SVM, is freely accessible for all kinds of researchers, and can be directly used within the FeatureCloud platform. UR - https://ai.jmir.org/2024/1/e47652 UR - http://dx.doi.org/10.2196/47652 UR - http://www.ncbi.nlm.nih.gov/pubmed/38875678 ID - info:doi/10.2196/47652 ER - TY - JOUR AU - Wiepert, Daniela AU - Malin, A. Bradley AU - Duffy, R. Joseph AU - Utianski, L. Rene AU - Stricker, L. John AU - Jones, T. David AU - Botha, Hugo PY - 2024/3/15 TI - Reidentification of Participants in Shared Clinical Data Sets: Experimental Study JO - JMIR AI SP - e52054 VL - 3 KW - reidentification KW - privacy KW - adversarial attack KW - health care KW - speech disorders KW - voiceprint N2 - Background: Large curated data sets are required to leverage speech-based tools in health care. These are costly to produce, resulting in increased interest in data sharing. As speech can potentially identify speakers (ie, voiceprints), sharing recordings raises privacy concerns. This is especially relevant when working with patient data protected under the Health Insurance Portability and Accountability Act. Objective: We aimed to determine the reidentification risk for speech recordings, without reference to demographics or metadata, in clinical data sets considering both the size of the search space (ie, the number of comparisons that must be considered when reidentifying) and the nature of the speech recording (ie, the type of speech task). Methods: Using a state-of-the-art speaker identification model, we modeled an adversarial attack scenario in which an adversary uses a large data set of identified speech (hereafter, the known set) to reidentify as many unknown speakers in a shared data set (hereafter, the unknown set) as possible. We first considered the effect of search space size by attempting reidentification with various sizes of known and unknown sets using VoxCeleb, a data set with recordings of natural, connected speech from >7000 healthy speakers. We then repeated these tests with different types of recordings in each set to examine whether the nature of a speech recording influences reidentification risk. For these tests, we used our clinical data set composed of recordings of elicited speech tasks from 941 speakers. Results: We found that the risk was inversely related to the number of comparisons an adversary must consider (ie, the search space), with a positive linear correlation between the number of false acceptances (FAs) and the number of comparisons (r=0.69; P<.001). The true acceptances (TAs) stayed relatively stable, and the ratio between FAs and TAs rose from 0.02 at 1 × 105 comparisons to 1.41 at 6 × 106 comparisons, with a near 1:1 ratio at the midpoint of 3 × 106 comparisons. In effect, risk was high for a small search space but dropped as the search space grew. We also found that the nature of a speech recording influenced reidentification risk, with nonconnected speech (eg, vowel prolongation: FA/TA=98.5; alternating motion rate: FA/TA=8) being harder to identify than connected speech (eg, sentence repetition: FA/TA=0.54) in cross-task conditions. The inverse was mostly true in within-task conditions, with the FA/TA ratio for vowel prolongation and alternating motion rate dropping to 0.39 and 1.17, respectively. Conclusions: Our findings suggest that speaker identification models can be used to reidentify participants in specific circumstances, but in practice, the reidentification risk appears small. The variation in risk due to search space size and type of speech task provides actionable recommendations to further increase participant privacy and considerations for policy regarding public release of speech recordings. UR - https://ai.jmir.org/2024/1/e52054 UR - http://dx.doi.org/10.2196/52054 UR - http://www.ncbi.nlm.nih.gov/pubmed/38875581 ID - info:doi/10.2196/52054 ER - TY - JOUR AU - Lu, Jiahui AU - Zhang, Huibin AU - Xiao, Yi AU - Wang, Yingyu PY - 2024/1/29 TI - An Environmental Uncertainty Perception Framework for Misinformation Detection and Spread Prediction in the COVID-19 Pandemic: Artificial Intelligence Approach JO - JMIR AI SP - e47240 VL - 3 KW - misinformation detection KW - misinformation spread prediction KW - uncertainty KW - COVID-19 KW - information environment N2 - Background: Amidst the COVID-19 pandemic, misinformation on social media has posed significant threats to public health. Detecting and predicting the spread of misinformation are crucial for mitigating its adverse effects. However, prevailing frameworks for these tasks have predominantly focused on post-level signals of misinformation, neglecting features of the broader information environment where misinformation originates and proliferates. Objective: This study aims to create a novel framework that integrates the uncertainty of the information environment into misinformation features, with the goal of enhancing the model?s accuracy in tasks such as misinformation detection and predicting the scale of dissemination. The objective is to provide better support for online governance efforts during health crises. Methods: In this study, we embraced uncertainty features within the information environment and introduced a novel Environmental Uncertainty Perception (EUP) framework for the detection of misinformation and the prediction of its spread on social media. The framework encompasses uncertainty at 4 scales of the information environment: physical environment, macro-media environment, micro-communicative environment, and message framing. We assessed the effectiveness of the EUP using real-world COVID-19 misinformation data sets. Results: The experimental results demonstrated that the EUP alone achieved notably good performance, with detection accuracy at 0.753 and prediction accuracy at 0.71. These results were comparable to state-of-the-art baseline models such as bidirectional long short-term memory (BiLSTM; detection accuracy 0.733 and prediction accuracy 0.707) and bidirectional encoder representations from transformers (BERT; detection accuracy 0.755 and prediction accuracy 0.728). Additionally, when the baseline models collaborated with the EUP, they exhibited improved accuracy by an average of 1.98% for the misinformation detection and 2.4% for spread-prediction tasks. On unbalanced data sets, the EUP yielded relative improvements of 21.5% and 5.7% in macro-F1-score and area under the curve, respectively. Conclusions: This study makes a significant contribution to the literature by recognizing uncertainty features within information environments as a crucial factor for improving misinformation detection and spread-prediction algorithms during the pandemic. The research elaborates on the complexities of uncertain information environments for misinformation across 4 distinct scales, including the physical environment, macro-media environment, micro-communicative environment, and message framing. The findings underscore the effectiveness of incorporating uncertainty into misinformation detection and spread prediction, providing an interdisciplinary and easily implementable framework for the field. UR - https://ai.jmir.org/2024/1/e47240 UR - http://dx.doi.org/10.2196/47240 UR - http://www.ncbi.nlm.nih.gov/pubmed/38875583 ID - info:doi/10.2196/47240 ER - TY - JOUR AU - Hansen, Steffan AU - Brandt, Joakim Carl AU - Søndergaard, Jens PY - 2024/1/22 TI - Beyond the Hype?The Actual Role and Risks of AI in Today?s Medical Practice: Comparative-Approach Study JO - JMIR AI SP - e49082 VL - 3 KW - AI KW - artificial intelligence KW - ChatGPT-4 KW - Microsoft Bing KW - general practice KW - ChatGPT KW - chatbot KW - chatbots KW - writing KW - academic KW - academia KW - Bing N2 - Background: The evolution of artificial intelligence (AI) has significantly impacted various sectors, with health care witnessing some of its most groundbreaking contributions. Contemporary models, such as ChatGPT-4 and Microsoft Bing, have showcased capabilities beyond just generating text, aiding in complex tasks like literature searches and refining web-based queries. Objective: This study explores a compelling query: can AI author an academic paper independently? Our assessment focuses on four core dimensions: relevance (to ensure that AI?s response directly addresses the prompt), accuracy (to ascertain that AI?s information is both factually correct and current), clarity (to examine AI?s ability to present coherent and logical ideas), and tone and style (to evaluate whether AI can align with the formality expected in academic writings). Additionally, we will consider the ethical implications and practicality of integrating AI into academic writing. Methods: To assess the capabilities of ChatGPT-4 and Microsoft Bing in the context of academic paper assistance in general practice, we used a systematic approach. ChatGPT-4, an advanced AI language model by Open AI, excels in generating human-like text and adapting responses based on user interactions, though it has a knowledge cut-off in September 2021. Microsoft Bing's AI chatbot facilitates user navigation on the Bing search engine, offering tailored search Results: In terms of relevance, ChatGPT-4 delved deeply into AI?s health care role, citing academic sources and discussing diverse applications and concerns, while Microsoft Bing provided a concise, less detailed overview. In terms of accuracy, ChatGPT-4 correctly cited 72% (23/32) of its peer-reviewed articles but included some nonexistent references. Microsoft Bing?s accuracy stood at 46% (6/13), supplemented by relevant non?peer-reviewed articles. In terms of clarity, both models conveyed clear, coherent text. ChatGPT-4 was particularly adept at detailing technical concepts, while Microsoft Bing was more general. In terms of tone, both models maintained an academic tone, but ChatGPT-4 exhibited superior depth and breadth in content delivery. Conclusions: Comparing ChatGPT-4 and Microsoft Bing for academic assistance revealed strengths and limitations. ChatGPT-4 excels in depth and relevance but falters in citation accuracy. Microsoft Bing is concise but lacks robust detail. Though both models have potential, neither can independently handle comprehensive academic tasks. As AI evolves, combining ChatGPT-4?s depth with Microsoft Bing?s up-to-date referencing could optimize academic support. Researchers should critically assess AI outputs to maintain academic credibility. UR - https://ai.jmir.org/2024/1/e49082 UR - http://dx.doi.org/10.2196/49082 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/49082 ER - TY - JOUR AU - Weidener, Lukas AU - Fischer, Michael PY - 2024/1/12 TI - Role of Ethics in Developing AI-Based Applications in Medicine: Insights From Expert Interviews and Discussion of Implications JO - JMIR AI SP - e51204 VL - 3 KW - artificial intelligence KW - AI KW - medicine KW - ethics KW - expert interviews KW - AI development KW - AI ethics N2 - Background: The integration of artificial intelligence (AI)?based applications in the medical field has increased significantly, offering potential improvements in patient care and diagnostics. However, alongside these advancements, there is growing concern about ethical considerations, such as bias, informed consent, and trust in the development of these technologies. Objective: This study aims to assess the role of ethics in the development of AI-based applications in medicine. Furthermore, this study focuses on the potential consequences of neglecting ethical considerations in AI development, particularly their impact on patients and physicians. Methods: Qualitative content analysis was used to analyze the responses from expert interviews. Experts were selected based on their involvement in the research or practical development of AI-based applications in medicine for at least 5 years, leading to the inclusion of 7 experts in the study. Results: The analysis revealed 3 main categories and 7 subcategories reflecting a wide range of views on the role of ethics in AI development. This variance underscores the subjectivity and complexity of integrating ethics into the development of AI in medicine. Although some experts view ethics as fundamental, others prioritize performance and efficiency, with some perceiving ethics as potential obstacles to technological progress. This dichotomy of perspectives clearly emphasizes the subjectivity and complexity surrounding the role of ethics in AI development, reflecting the inherent multifaceted nature of this issue. Conclusions: Despite the methodological limitations impacting the generalizability of the results, this study underscores the critical importance of consistent and integrated ethical considerations in AI development for medical applications. It advocates further research into effective strategies for ethical AI development, emphasizing the need for transparent and responsible practices, consideration of diverse data sources, physician training, and the establishment of comprehensive ethical and legal frameworks. UR - https://ai.jmir.org/2024/1/e51204 UR - http://dx.doi.org/10.2196/51204 UR - http://www.ncbi.nlm.nih.gov/pubmed/38875585 ID - info:doi/10.2196/51204 ER - TY - JOUR AU - Hendricks-Sturrup, Rachele AU - Simmons, Malaika AU - Anders, Shilo AU - Aneni, Kammarauche AU - Wright Clayton, Ellen AU - Coco, Joseph AU - Collins, Benjamin AU - Heitman, Elizabeth AU - Hussain, Sajid AU - Joshi, Karuna AU - Lemieux, Josh AU - Lovett Novak, Laurie AU - Rubin, J. Daniel AU - Shanker, Anil AU - Washington, Talitha AU - Waters, Gabriella AU - Webb Harris, Joyce AU - Yin, Rui AU - Wagner, Teresa AU - Yin, Zhijun AU - Malin, Bradley PY - 2023/12/6 TI - Developing Ethics and Equity Principles, Terms, and Engagement Tools to Advance Health Equity and Researcher Diversity in AI and Machine Learning: Modified Delphi Approach JO - JMIR AI SP - e52888 VL - 2 KW - artificial intelligence KW - AI KW - Delphi KW - disparities KW - disparity KW - engagement KW - equitable KW - equities KW - equity KW - ethic KW - ethical KW - ethics KW - fair KW - fairness KW - health disparities KW - health equity KW - humanitarian KW - machine learning KW - ML N2 - Background: Artificial intelligence (AI) and machine learning (ML) technology design and development continues to be rapid, despite major limitations in its current form as a practice and discipline to address all sociohumanitarian issues and complexities. From these limitations emerges an imperative to strengthen AI and ML literacy in underserved communities and build a more diverse AI and ML design and development workforce engaged in health research. Objective: AI and ML has the potential to account for and assess a variety of factors that contribute to health and disease and to improve prevention, diagnosis, and therapy. Here, we describe recent activities within the Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) Ethics and Equity Workgroup (EEWG) that led to the development of deliverables that will help put ethics and fairness at the forefront of AI and ML applications to build equity in biomedical research, education, and health care. Methods: The AIM-AHEAD EEWG was created in 2021 with 3 cochairs and 51 members in year 1 and 2 cochairs and ~40 members in year 2. Members in both years included AIM-AHEAD principal investigators, coinvestigators, leadership fellows, and research fellows. The EEWG used a modified Delphi approach using polling, ranking, and other exercises to facilitate discussions around tangible steps, key terms, and definitions needed to ensure that ethics and fairness are at the forefront of AI and ML applications to build equity in biomedical research, education, and health care. Results: The EEWG developed a set of ethics and equity principles, a glossary, and an interview guide. The ethics and equity principles comprise 5 core principles, each with subparts, which articulate best practices for working with stakeholders from historically and presently underrepresented communities. The glossary contains 12 terms and definitions, with particular emphasis on optimal development, refinement, and implementation of AI and ML in health equity research. To accompany the glossary, the EEWG developed a concept relationship diagram that describes the logical flow of and relationship between the definitional concepts. Lastly, the interview guide provides questions that can be used or adapted to garner stakeholder and community perspectives on the principles and glossary. Conclusions: Ongoing engagement is needed around our principles and glossary to identify and predict potential limitations in their uses in AI and ML research settings, especially for institutions with limited resources. This requires time, careful consideration, and honest discussions around what classifies an engagement incentive as meaningful to support and sustain their full engagement. By slowing down to meet historically and presently underresourced institutions and communities where they are and where they are capable of engaging and competing, there is higher potential to achieve needed diversity, ethics, and equity in AI and ML implementation in health research. UR - https://ai.jmir.org/2023/1/e52888 UR - http://dx.doi.org/10.2196/52888 UR - http://www.ncbi.nlm.nih.gov/pubmed/38875540 ID - info:doi/10.2196/52888 ER - TY - JOUR AU - Hummelsberger, Pia AU - Koch, K. Timo AU - Rauh, Sabrina AU - Dorn, Julia AU - Lermer, Eva AU - Raue, Martina AU - Hudecek, C. Matthias F. AU - Schicho, Andreas AU - Colak, Errol AU - Ghassemi, Marzyeh AU - Gaube, Susanne PY - 2023/10/31 TI - Insights on the Current State and Future Outlook of AI in Health Care: Expert Interview Study JO - JMIR AI SP - e47353 VL - 2 KW - artificial intelligence KW - AI KW - machine learning KW - health care KW - digital health technology KW - technology implementation KW - expert interviews KW - mixed methods KW - topic modeling N2 - Background: Artificial intelligence (AI) is often promoted as a potential solution for many challenges health care systems face worldwide. However, its implementation in clinical practice lags behind its technological development. Objective: This study aims to gain insights into the current state and prospects of AI technology from the stakeholders most directly involved in its adoption in the health care sector whose perspectives have received limited attention in research to date. Methods: For this purpose, the perspectives of AI researchers and health care IT professionals in North America and Western Europe were collected and compared for profession-specific and regional differences. In this preregistered, mixed methods, cross-sectional study, 23 experts were interviewed using a semistructured guide. Data from the interviews were analyzed using deductive and inductive qualitative methods for the thematic analysis along with topic modeling to identify latent topics. Results: Through our thematic analysis, four major categories emerged: (1) the current state of AI systems in health care, (2) the criteria and requirements for implementing AI systems in health care, (3) the challenges in implementing AI systems in health care, and (4) the prospects of the technology. Experts discussed the capabilities and limitations of current AI systems in health care in addition to their prevalence and regional differences. Several criteria and requirements deemed necessary for the successful implementation of AI systems were identified, including the technology?s performance and security, smooth system integration and human-AI interaction, costs, stakeholder involvement, and employee training. However, regulatory, logistical, and technical issues were identified as the most critical barriers to an effective technology implementation process. In the future, our experts predicted both various threats and many opportunities related to AI technology in the health care sector. Conclusions: Our work provides new insights into the current state, criteria, challenges, and outlook for implementing AI technology in health care from the perspective of AI researchers and IT professionals in North America and Western Europe. For the full potential of AI-enabled technologies to be exploited and for them to contribute to solving current health care challenges, critical implementation criteria must be met, and all groups involved in the process must work together. UR - https://ai.jmir.org/2023/1/e47353 UR - http://dx.doi.org/10.2196/47353 UR - http://www.ncbi.nlm.nih.gov/pubmed/38875571 ID - info:doi/10.2196/47353 ER - TY - JOUR AU - Kim, Paik Jane AU - Ryan, Katie AU - Kasun, Max AU - Hogg, Justin AU - Dunn, B. Laura AU - Roberts, Weiss Laura PY - 2023/10/30 TI - Physicians? and Machine Learning Researchers? Perspectives on Ethical Issues in the Early Development of Clinical Machine Learning Tools: Qualitative Interview Study JO - JMIR AI SP - e47449 VL - 2 KW - artificial intelligence KW - machine learning KW - ethical considerations KW - qualitative study KW - qualitative KW - ethic KW - ethics KW - ethical KW - perspective N2 - Background: Innovative tools leveraging artificial intelligence (AI) and machine learning (ML) are rapidly being developed for medicine, with new applications emerging in prediction, diagnosis, and treatment across a range of illnesses, patient populations, and clinical procedures. One barrier for successful innovation is the scarcity of research in the current literature seeking and analyzing the views of AI or ML researchers and physicians to support ethical guidance. Objective: This study aims to describe, using a qualitative approach, the landscape of ethical issues that AI or ML researchers and physicians with professional exposure to AI or ML tools observe or anticipate in the development and use of AI and ML in medicine. Methods: Semistructured interviews were used to facilitate in-depth, open-ended discussion, and a purposeful sampling technique was used to identify and recruit participants. We conducted 21 semistructured interviews with a purposeful sample of AI and ML researchers (n=10) and physicians (n=11). We asked interviewees about their views regarding ethical considerations related to the adoption of AI and ML in medicine. Interviews were transcribed and deidentified by members of our research team. Data analysis was guided by the principles of qualitative content analysis. This approach, in which transcribed data is broken down into descriptive units that are named and sorted based on their content, allows for the inductive emergence of codes directly from the data set. Results: Notably, both researchers and physicians articulated concerns regarding how AI and ML innovations are shaped in their early development (ie, the problem formulation stage). Considerations encompassed the assessment of research priorities and motivations, clarity and centeredness of clinical needs, professional and demographic diversity of research teams, and interdisciplinary knowledge generation and collaboration. Phase-1 ethical issues identified by interviewees were notably interdisciplinary in nature and invited questions regarding how to align priorities and values across disciplines and ensure clinical value throughout the development and implementation of medical AI and ML. Relatedly, interviewees suggested interdisciplinary solutions to these issues, for example, more resources to support knowledge generation and collaboration between developers and physicians, engagement with a broader range of stakeholders, and efforts to increase diversity in research broadly and within individual teams. Conclusions: These qualitative findings help elucidate several ethical challenges anticipated or encountered in AI and ML for health care. Our study is unique in that its use of open-ended questions allowed interviewees to explore their sentiments and perspectives without overreliance on implicit assumptions about what AI and ML currently are or are not. This analysis, however, does not include the perspectives of other relevant stakeholder groups, such as patients, ethicists, industry researchers or representatives, or other health care professionals beyond physicians. Additional qualitative and quantitative research is needed to reproduce and build on these findings. UR - https://ai.jmir.org/2023/1/e47449 UR - http://dx.doi.org/10.2196/47449 UR - http://www.ncbi.nlm.nih.gov/pubmed/38875536 ID - info:doi/10.2196/47449 ER - TY - JOUR AU - Malgaroli, Matteo AU - Tseng, Emily AU - Hull, D. Thomas AU - Jennings, Emma AU - Choudhury, K. Tanzeem AU - Simon, M. Naomi PY - 2023/10/24 TI - Association of Health Care Work With Anxiety and Depression During the COVID-19 Pandemic: Structural Topic Modeling Study JO - JMIR AI SP - e47223 VL - 2 KW - depression KW - anxiety KW - health care workers KW - COVID-19 KW - natural language processing KW - topic modeling KW - stressor KW - mental health KW - treatment KW - psychotherapy KW - digital health N2 - Background: Stressors for health care workers (HCWs) during the COVID-19 pandemic have been manifold, with high levels of depression and anxiety alongside gaps in care. Identifying the factors most tied to HCWs? psychological challenges is crucial to addressing HCWs? mental health needs effectively, now and for future large-scale events. Objective: In this study, we used natural language processing methods to examine deidentified psychotherapy transcripts from telemedicine treatment during the initial wave of COVID-19 in the United States. Psychotherapy was delivered by licensed therapists while HCWs were managing increased clinical demands and elevated hospitalization rates, in addition to population-level social distancing measures and infection risks. Our goal was to identify specific concerns emerging in treatment for HCWs and to compare differences with matched non-HCW patients from the general population. Methods: We conducted a case-control study with a sample of 820 HCWs and 820 non-HCW matched controls who received digitally delivered psychotherapy in 49 US states in the spring of 2020 during the first US wave of the COVID-19 pandemic. Depression was measured during the initial assessment using the Patient Health Questionnaire-9, and anxiety was measured using the General Anxiety Disorder-7 questionnaire. Structural topic models (STMs) were used to determine treatment topics from deidentified transcripts from the first 3 weeks of treatment. STM effect estimators were also used to examine topic prevalence in patients with moderate to severe anxiety and depression. Results: The median treatment enrollment date was April 15, 2020 (IQR March 31 to April 27, 2020) for HCWs and April 19, 2020 (IQR April 5 to April 27, 2020) for matched controls. STM analysis of deidentified transcripts identified 4 treatment topics centered on health care and 5 on mental health for HCWs. For controls, 3 STM topics on pandemic-related disruptions and 5 on mental health were identified. Several STM treatment topics were significantly associated with moderate to severe anxiety and depression, including working on the hospital unit (topic prevalence 0.035, 95% CI 0.022-0.048; P<.001), mood disturbances (prevalence 0.014, 95% CI 0.002-0.026; P=.03), and sleep disturbances (prevalence 0.016, 95% CI 0.002-0.030; P=.02). No significant associations emerged between pandemic-related topics and moderate to severe anxiety and depression for non-HCW controls. Conclusions: The study provides large-scale quantitative evidence that during the initial wave of the COVID-19 pandemic, HCWs faced unique work-related challenges and stressors associated with anxiety and depression, which required dedicated treatment efforts. The study further demonstrates how natural language processing methods have the potential to surface clinically relevant markers of distress while preserving patient privacy. UR - https://ai.jmir.org/2023/1/e47223 UR - http://dx.doi.org/10.2196/47223 UR - http://www.ncbi.nlm.nih.gov/pubmed/38875560 ID - info:doi/10.2196/47223 ER - TY - JOUR AU - Saraswat, Nidhi AU - Li, Chuqin AU - Jiang, Min PY - 2023/9/26 TI - Identifying the Question Similarity of Regulatory Documents in the Pharmaceutical Industry by Using the Recognizing Question Entailment System: Evaluation Study JO - JMIR AI SP - e43483 VL - 2 KW - regulatory affairs KW - frequently asked questions KW - FAQs KW - Recognizing Question Entailment system KW - RQE system KW - transformer-based models KW - textual data augmentations N2 - Background: The regulatory affairs (RA) division in a pharmaceutical establishment is the point of contact between regulatory authorities and pharmaceutical companies. They are delegated the crucial and strenuous task of extracting and summarizing relevant information in the most meticulous manner from various search systems. An artificial intelligence (AI)?based intelligent search system that can significantly bring down the manual efforts in the existing processes of the RA department while maintaining and improving the quality of final outcomes is desirable. We proposed a ?frequently asked questions? component and its utility in an AI-based intelligent search system in this paper. The scenario is further complicated by the lack of publicly available relevant data sets in the RA domain to train the machine learning models that can facilitate cognitive search systems for regulatory authorities. Objective: In this study, we aimed to use AI-based intelligent computational models to automatically recognize semantically similar question pairs in the RA domain and evaluate the Recognizing Question Entailment?based system. Methods: We used transfer learning techniques and experimented with transformer-based models pretrained on corpora collected from different resources, such as Bidirectional Encoder Representations from Transformers (BERT), Clinical BERT, BioBERT, and BlueBERT. We used a manually labeled data set that contained 150 question pairs in the pharmaceutical regulatory domain to evaluate the performance of our model. Results: The Clinical BERT model performed better than other domain-specific BERT-based models in identifying question similarity from the RA domain. The BERT model had the best ability to learn domain-specific knowledge with transfer learning, which reached the best performance when fine-tuned with sufficient clinical domain question pairs. The top-performing model achieved an accuracy of 90.66% on the test set. Conclusions: This study demonstrates the possibility of using pretrained language models to recognize question similarity in the pharmaceutical regulatory domain. Transformer-based models that are pretrained on clinical notes perform better than models pretrained on biomedical text in recognizing the question?s semantic similarity in this domain. We also discuss the challenges of using data augmentation techniques to address the lack of relevant data in this domain. The results of our experiment indicated that increasing the number of training samples using back translation and entity replacement did not enhance the model?s performance. This lack of improvement may be attributed to the intricate and specialized nature of texts in the regulatory domain. Our work provides the foundation for further studies that apply state-of-the-art linguistic models to regulatory documents in the pharmaceutical industry. UR - https://ai.jmir.org/2023/1/e43483 UR - http://dx.doi.org/10.2196/43483 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/43483 ER - TY - JOUR AU - Robinson, Renee AU - Liday, Cara AU - Lee, Sarah AU - Williams, C. Ishan AU - Wright, Melanie AU - An, Sungjoon AU - Nguyen, Elaine PY - 2023/6/19 TI - Artificial Intelligence in Health Care?Understanding Patient Information Needs and Designing Comprehensible Transparency: Qualitative Study JO - JMIR AI SP - e46487 VL - 2 KW - artificial intelligence KW - machine learning KW - diabetes KW - equipment safety KW - equipment design KW - health care N2 - Background: Artificial intelligence (AI) is a branch of computer science that uses advanced computational methods, such as machine learning (ML), to calculate and predict health outcomes and address patient and provider health needs. While these technologies show great promise for improving health care, especially in diabetes management, there are usability and safety concerns for both patients and providers about the use of AI/ML in health care management. Objective: We aimed to support and ensure safe use of AI/ML technologies in health care; thus, the team worked to better understand (1) patient information and training needs, (2) the factors that influence patients? perceived value and trust in AI/ML health care applications, and (3) how best to support safe and appropriate use of AI/ML-enabled devices and applications among people living with diabetes. Methods: To understand general patient perspectives and information needs related to the use of AI/ML in health care, we conducted a series of focus groups (n=9) and interviews (n=3) with patients (n=41) and interviews with providers (n=6) in Alaska, Idaho, and Virginia. Grounded theory guided data gathering, synthesis, and analysis. Thematic content and constant comparison analysis were used to identify relevant themes and subthemes. Inductive approaches were used to link data to key concepts, including preferred patient-provider interactions and patient perceptions of trust, accuracy, value, assurances, and information transparency. Results: Key summary themes and recommendations focused on (1) patient preferences for AI/ML-enabled device and application information, (2) patient and provider AI/ML-related device and application training needs, (3) factors contributing to patient and provider trust in AI/ML-enabled devices and applications, and (4) AI/ML-related device and application functionality and safety considerations. A number of participants (patients and providers) made recommendations to improve device functionality to guide information and labeling mandates (eg, link to online video resources and provide access to 24/7 live in-person or virtual emergency support). Other patient recommendations included (1) providing access to practice devices, (2) providing connections to local supports and reputable community resources, and (3) simplifying the display and alert limits. Conclusions: Recommendations from both patients and providers could be used by federal oversight agencies to improve utilization of AI/ML monitoring of technology use in diabetes, improving device safety and efficacy. UR - https://ai.jmir.org/2023/1/e46487 UR - http://dx.doi.org/10.2196/46487 UR - http://www.ncbi.nlm.nih.gov/pubmed/38333424 ID - info:doi/10.2196/46487 ER - TY - JOUR AU - Benjamens, Stan AU - Dhunnoo, Pranavsingh AU - Görög, Márton AU - Mesko, Bertalan PY - 2023/5/26 TI - Forecasting Artificial Intelligence Trends in Health Care: Systematic International Patent Analysis JO - JMIR AI SP - e47283 VL - 2 KW - artificial intelligence KW - patent KW - healthcare KW - health care KW - medical KW - forecasting KW - future KW - AI KW - machine learning KW - medical device KW - open-access KW - AI technology N2 - Background: Artificial intelligence (AI)? and machine learning (ML)?based medical devices and algorithms are rapidly changing the medical field. To provide an insight into the trends in AI and ML in health care, we conducted an international patent analysis. Objective: It is pivotal to obtain a clear overview on upcoming AI and MLtrends in health care to provide regulators with a better position to foresee what technologies they will have to create regulations for, which are not yet available on the market. Therefore, in this study, we provide insights and forecasts into the trends in AI and ML in health care by conducting an international patent analysis. Methods: A systematic patent analysis, focusing on AI- and ML-based patents in health care, was performed using the Espacenet database (from January 2012 until July 2022). This database includes patents from the China National Intellectual Property Administration, European Patent Office, Japan Patent Office, Korean Intellectual Property Office, and the United States Patent and Trademark Office. Results: We identified 10,967 patents: 7332 (66.9%) from the China National Intellectual Property Administration, 191 (1.7%) from the European Patent Office, 163 (1.5%) from the Japan Patent Office, 513 (4.7%) from the Korean Intellectual Property Office, and 2768 (25.2%) from the United States Patent and Trademark Office. The number of published patents showed a yearly doubling from 2015 until 2021. Five international companies that had the greatest impact on this increase were Ping An Medical and Healthcare Management Co Ltd with 568 (5.2%) patents, Siemens Healthineers with 273 (2.5%) patents, IBM Corp with 226 (2.1%) patents, Philips Healthcare with 150 (1.4%) patents, and Shanghai United Imaging Healthcare Co Ltd with 144 (1.3%) patents. Conclusions: This international patent analysis showed a linear increase in patents published by the 5 largest patent offices. An open access database with interactive search options was launched for AI- and ML-based patents in health care. UR - https://ai.jmir.org/2023/1/e47283 UR - http://dx.doi.org/10.2196/47283 UR - http://www.ncbi.nlm.nih.gov/pubmed/10449890 ID - info:doi/10.2196/47283 ER - TY - JOUR AU - Owen, David AU - Antypas, Dimosthenis AU - Hassoulas, Athanasios AU - Pardiñas, F. Antonio AU - Espinosa-Anke, Luis AU - Collados, Camacho Jose PY - 2023/3/24 TI - Enabling Early Health Care Intervention by Detecting Depression in Users of Web-Based Forums using Language Models: Longitudinal Analysis and Evaluation JO - JMIR AI SP - e41205 VL - 2 KW - mental health KW - depression KW - internet KW - natural language processing KW - transformers KW - language models KW - sentiment N2 - Background: Major depressive disorder is a common mental disorder affecting 5% of adults worldwide. Early contact with health care services is critical for achieving accurate diagnosis and improving patient outcomes. Key symptoms of major depressive disorder (depression hereafter) such as cognitive distortions are observed in verbal communication, which can also manifest in the structure of written language. Thus, the automatic analysis of text outputs may provide opportunities for early intervention in settings where written communication is rich and regular, such as social media and web-based forums. Objective: The objective of this study was 2-fold. We sought to gauge the effectiveness of different machine learning approaches to identify users of the mass web-based forum Reddit, who eventually disclose a diagnosis of depression. We then aimed to determine whether the time between a forum post and a depression diagnosis date was a relevant factor in performing this detection. Methods: A total of 2 Reddit data sets containing posts belonging to users with and without a history of depression diagnosis were obtained. The intersection of these data sets provided users with an estimated date of depression diagnosis. This derived data set was used as an input for several machine learning classifiers, including transformer-based language models (LMs). Results: Bidirectional Encoder Representations from Transformers (BERT) and MentalBERT transformer-based LMs proved the most effective in distinguishing forum users with a known depression diagnosis from those without. They each obtained a mean F1-score of 0.64 across the experimental setups used for binary classification. The results also suggested that the final 12 to 16 weeks (about 3-4 months) of posts before a depressed user?s estimated diagnosis date are the most indicative of their illness, with data before that period not helping the models detect more accurately. Furthermore, in the 4- to 8-week period before the user?s estimated diagnosis date, their posts exhibited more negative sentiment than any other 4-week period in their post history. Conclusions: Transformer-based LMs may be used on data from web-based social media forums to identify users at risk for psychiatric conditions such as depression. Language features picked up by these classifiers might predate depression onset by weeks to months, enabling proactive mental health care interventions to support those at risk for this condition. UR - https://ai.jmir.org/2023/1/e41205 UR - http://dx.doi.org/10.2196/41205 UR - http://www.ncbi.nlm.nih.gov/pubmed/37525646 ID - info:doi/10.2196/41205 ER - TY - JOUR AU - Jeyakumar, Tharshini AU - Younus, Sarah AU - Zhang, Melody AU - Clare, Megan AU - Charow, Rebecca AU - Karsan, Inaara AU - Dhalla, Azra AU - Al-Mouaswas, Dalia AU - Scandiffio, Jillian AU - Aling, Justin AU - Salhia, Mohammad AU - Lalani, Nadim AU - Overholt, Scott AU - Wiljer, David PY - 2023/3/2 TI - Preparing for an Artificial Intelligence?Enabled Future: Patient Perspectives on Engagement and Health Care Professional Training for Adopting Artificial Intelligence Technologies in Health Care Settings JO - JMIR AI SP - e40973 VL - 2 KW - artificial intelligence KW - patient KW - education KW - attitude KW - health data KW - adoption KW - health equity KW - patient engagement N2 - Background: As new technologies emerge, there is a significant shift in the way care is delivered on a global scale. Artificial intelligence (AI) technologies have been rapidly and inexorably used to optimize patient outcomes, reduce health system costs, improve workflow efficiency, and enhance population health. Despite the widespread adoption of AI technologies, the literature on patient engagement and their perspectives on how AI will affect clinical care is scarce. Minimal patient engagement can limit the optimization of these novel technologies and contribute to suboptimal use in care settings. Objective: We aimed to explore patients? views on what skills they believe health care professionals should have in preparation for this AI-enabled future and how we can better engage patients when adopting and deploying AI technologies in health care settings. Methods: Semistructured interviews were conducted from August 2020 to December 2021 with 12 individuals who were a patient in any Canadian health care setting. Interviews were conducted until thematic saturation occurred. A thematic analysis approach outlined by Braun and Clarke was used to inductively analyze the data and identify overarching themes. Results: Among the 12 patients interviewed, 8 (67%) were from urban settings and 4 (33%) were from rural settings. A majority of the participants were very comfortable with technology (n=6, 50%) and somewhat familiar with AI (n=7, 58%). In total, 3 themes emerged: cultivating patients? trust, fostering patient engagement, and establishing data governance and validation of AI technologies. Conclusions: With the rapid surge of AI solutions, there is a critical need to understand patient values in advancing the quality of care and contributing to an equitable health system. Our study demonstrated that health care professionals play a synergetic role in the future of AI and digital technologies. Patient engagement is vital in addressing underlying health inequities and fostering an optimal care experience. Future research is warranted to understand and capture the diverse perspectives of patients with various racial, ethnic, and socioeconomic backgrounds. UR - https://ai.jmir.org/2023/1/e40973 UR - http://dx.doi.org/10.2196/40973 UR - http://www.ncbi.nlm.nih.gov/pubmed/38875561 ID - info:doi/10.2196/40973 ER - TY - JOUR AU - Berdahl, Thomas Carl AU - Baker, Lawrence AU - Mann, Sean AU - Osoba, Osonde AU - Girosi, Federico PY - 2023/2/7 TI - Strategies to Improve the Impact of Artificial Intelligence on Health Equity: Scoping Review JO - JMIR AI SP - e42936 VL - 2 KW - artificial intelligence KW - machine learning KW - health equity KW - health care disparities KW - algorithmic bias KW - social determinants of health KW - decision making KW - algorithms KW - gray literature KW - equity KW - health data N2 - Background: Emerging artificial intelligence (AI) applications have the potential to improve health, but they may also perpetuate or exacerbate inequities. Objective: This review aims to provide a comprehensive overview of the health equity issues related to the use of AI applications and identify strategies proposed to address them. Methods: We searched PubMed, Web of Science, the IEEE (Institute of Electrical and Electronics Engineers) Xplore Digital Library, ProQuest U.S. Newsstream, Academic Search Complete, the Food and Drug Administration (FDA) website, and ClinicalTrials.gov to identify academic and gray literature related to AI and health equity that were published between 2014 and 2021 and additional literature related to AI and health equity during the COVID-19 pandemic from 2020 and 2021. Literature was eligible for inclusion in our review if it identified at least one equity issue and a corresponding strategy to address it. To organize and synthesize equity issues, we adopted a 4-step AI application framework: Background Context, Data Characteristics, Model Design, and Deployment. We then created a many-to-many mapping of the links between issues and strategies. Results: In 660 documents, we identified 18 equity issues and 15 strategies to address them. Equity issues related to Data Characteristics and Model Design were the most common. The most common strategies recommended to improve equity were improving the quantity and quality of data, evaluating the disparities introduced by an application, increasing model reporting and transparency, involving the broader community in AI application development, and improving governance. Conclusions: Stakeholders should review our many-to-many mapping of equity issues and strategies when planning, developing, and implementing AI applications in health care so that they can make appropriate plans to ensure equity for populations affected by their products. AI application developers should consider adopting equity-focused checklists, and regulators such as the FDA should consider requiring them. Given that our review was limited to documents published online, developers may have unpublished knowledge of additional issues and strategies that we were unable to identify. UR - https://ai.jmir.org/2023/1/e42936 UR - http://dx.doi.org/10.2196/42936 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/42936 ER - TY - JOUR AU - Mashar, Meghavi AU - Chawla, Shreya AU - Chen, Fangyue AU - Lubwama, Baker AU - Patel, Kyle AU - Kelshiker, A. Mihir AU - Bachtiger, Patrik AU - Peters, S. Nicholas PY - 2023/1/16 TI - Artificial Intelligence Algorithms in Health Care: Is the Current Food and Drug Administration Regulation Sufficient? JO - JMIR AI SP - e42940 VL - 2 KW - artificial intelligence KW - machine learning KW - regulation UR - https://ai.jmir.org/2023/1/e42940 UR - http://dx.doi.org/10.2196/42940 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/42940 ER - TY - JOUR AU - Barry, Barbara AU - Zhu, Xuan AU - Behnken, Emma AU - Inselman, Jonathan AU - Schaepe, Karen AU - McCoy, Rozalina AU - Rushlow, David AU - Noseworthy, Peter AU - Richardson, Jordan AU - Curtis, Susan AU - Sharp, Richard AU - Misra, Artika AU - Akfaly, Abdulla AU - Molling, Paul AU - Bernard, Matthew AU - Yao, Xiaoxi PY - 2022/10/14 TI - Provider Perspectives on Artificial Intelligence?Guided Screening for Low Ejection Fraction in Primary Care: Qualitative Study JO - JMIR AI SP - e41940 VL - 1 IS - 1 KW - artificial intelligence KW - AI KW - machine learning KW - human-AI interaction KW - health informatics KW - primary care KW - cardiology KW - pragmatic clinical trial KW - AI-enabled clinical decision support KW - human-computer interaction KW - health care delivery KW - clinical decision support KW - health care KW - AI tools N2 - Background: The promise of artificial intelligence (AI) to transform health care is threatened by a tangle of challenges that emerge as new AI tools are introduced into clinical practice. AI tools with high accuracy, especially those that detect asymptomatic cases, may be hindered by barriers to adoption. Understanding provider needs and concerns is critical to inform implementation strategies that improve provider buy-in and adoption of AI tools in medicine. Objective: This study aimed to describe provider perspectives on the adoption of an AI-enabled screening tool in primary care to inform effective integration and sustained use. Methods: A qualitative study was conducted between December 2019 and February 2020 as part of a pragmatic randomized controlled trial at a large academic medical center in the United States. In all, 29 primary care providers were purposively sampled using a positive deviance approach for participation in semistructured focus groups after their use of the AI tool in the randomized controlled trial was complete. Focus group data were analyzed using a grounded theory approach; iterative analysis was conducted to identify codes and themes, which were synthesized into findings. Results: Our findings revealed that providers understood the purpose and functionality of the AI tool and saw potential value for more accurate and faster diagnoses. However, successful adoption into routine patient care requires the smooth integration of the tool with clinical decision-making and existing workflow to address provider needs and preferences during implementation. To fulfill the AI tool?s promise of clinical value, providers identified areas for improvement including integration with clinical decision-making, cost-effectiveness and resource allocation, provider training, workflow integration, care pathway coordination, and provider-patient communication. Conclusions: The implementation of AI-enabled tools in medicine can benefit from sensitivity to the nuanced context of care and provider needs to enable the useful adoption of AI tools at the point of care. Trial Registration: ClinicalTrials.gov NCT04000087; https://clinicaltrials.gov/ct2/show/NCT04000087 UR - https://ai.jmir.org/2022/1/e41940 UR - http://dx.doi.org/10.2196/41940 UR - http://www.ncbi.nlm.nih.gov/pubmed/38875550 ID - info:doi/10.2196/41940 ER -