JMIR AI
A new peer reviewed journal focused on research and applications for the health artificial intelligence (AI) community.
Editor-in-Chief:
Khaled El Emam, PhD, Canada Research Chair in Medical AI, University of Ottawa; Senior Scientist, Children’s Hospital of Eastern Ontario Research Institute: Professor, School of Epidemiology and Public Health, University of Ottawa, Canada Bradley Malin, PhD, Accenture Professor of Biomedical Informatics, Biostatistics, and Computer Science; Vice Chair for Research Affairs, Department of Biomedical Informatics: Affiliated Faculty, Center for Biomedical Ethics & Society, Vanderbilt University Medical Center, Nashville, Tennessee, USA
Recent Articles
![Use of Deep Neural Networks to Predict Obesity With Short Audio Recordings: Development and Usability Study Article Thumbnail](https://asset.jmir.pub/assets/3996e27c83f837c5a4398ae883a6c2df.png 480w,https://asset.jmir.pub/assets/3996e27c83f837c5a4398ae883a6c2df.png 960w,https://asset.jmir.pub/assets/3996e27c83f837c5a4398ae883a6c2df.png 1920w,https://asset.jmir.pub/assets/3996e27c83f837c5a4398ae883a6c2df.png 2500w)
The escalating global prevalence of obesity has necessitated the exploration of novel diagnostic approaches. Recent scientific inquiries have indicated potential alterations in voice characteristics associated with obesity, suggesting the feasibility of using voice as a noninvasive biomarker for obesity detection.
![Enhancing Type 2 Diabetes Treatment Decisions With Interpretable Machine Learning Models for Predicting Hemoglobin A1c Changes: Machine Learning Model Development Article Thumbnail](https://asset.jmir.pub/assets/3128d49727b90c5d8dd5a36a04b71f30.png 480w,https://asset.jmir.pub/assets/3128d49727b90c5d8dd5a36a04b71f30.png 960w,https://asset.jmir.pub/assets/3128d49727b90c5d8dd5a36a04b71f30.png 1920w,https://asset.jmir.pub/assets/3128d49727b90c5d8dd5a36a04b71f30.png 2500w)
Type 2 diabetes (T2D) is a significant global health challenge. Physicians need to assess whether future glycemic control will be poor on the current trajectory of usual care and usual-care treatment intensifications so that they can consider taking extra treatment measures to prevent poor outcomes. Predicting poor glycemic control from trends in hemoglobin A1c (HbA1c) levels is difficult due to the influence of seasonal fluctuations and other factors.
![Augmenting Telepostpartum Care With Vision-Based Detection of Breastfeeding-Related Conditions: Algorithm Development and Validation Article Thumbnail](https://asset.jmir.pub/assets/91e73ffd6cca156a1da548e71f318107.png 480w,https://asset.jmir.pub/assets/91e73ffd6cca156a1da548e71f318107.png 960w,https://asset.jmir.pub/assets/91e73ffd6cca156a1da548e71f318107.png 1920w,https://asset.jmir.pub/assets/91e73ffd6cca156a1da548e71f318107.png 2500w)
Breastfeeding benefits both the mother and infant and is a topic of attention in public health. After childbirth, untreated medical conditions or lack of support lead many mothers to discontinue breastfeeding. For instance, nipple damage and mastitis affect 80% and 20% of US mothers, respectively. Lactation consultants (LCs) help mothers with breastfeeding, providing in-person, remote, and hybrid lactation support. LCs guide, encourage, and find ways for mothers to have a better experience breastfeeding. Current telehealth services help mothers seek LCs for breastfeeding support, where images help them identify and address many issues. Due to the disproportional ratio of LCs and mothers in need, these professionals are often overloaded and burned out.
![Toward Clinical Generative AI: Conceptual Framework Article Thumbnail](https://asset.jmir.pub/assets/4abf7e06482f91b774728d7495d57537.png 480w,https://asset.jmir.pub/assets/4abf7e06482f91b774728d7495d57537.png 960w,https://asset.jmir.pub/assets/4abf7e06482f91b774728d7495d57537.png 1920w,https://asset.jmir.pub/assets/4abf7e06482f91b774728d7495d57537.png 2500w)
Clinical decision-making is a crucial aspect of health care, involving the balanced integration of scientific evidence, clinical judgment, ethical considerations, and patient involvement. This process is dynamic and multifaceted, relying on clinicians’ knowledge, experience, and intuitive understanding to achieve optimal patient outcomes through informed, evidence-based choices. The advent of generative artificial intelligence (AI) presents a revolutionary opportunity in clinical decision-making. AI’s advanced data analysis and pattern recognition capabilities can significantly enhance the diagnosis and treatment of diseases, processing vast medical data to identify patterns, tailor treatments, predict disease progression, and aid in proactive patient management. However, the incorporation of AI into clinical decision-making raises concerns regarding the reliability and accuracy of AI-generated insights. To address these concerns, 11 “verification paradigms” are proposed in this paper, with each paradigm being a unique method to verify the evidence-based nature of AI in clinical decision-making. This paper also frames the concept of “clinically explainable, fair, and responsible, clinician-, expert-, and patient-in-the-loop AI.” This model focuses on ensuring AI’s comprehensibility, collaborative nature, and ethical grounding, advocating for AI to serve as an augmentative tool, with its decision-making processes being transparent and understandable to clinicians and patients. The integration of AI should enhance, not replace, the clinician’s judgment and should involve continuous learning and adaptation based on real-world outcomes and ethical and legal compliance. In conclusion, while generative AI holds immense promise in enhancing clinical decision-making, it is essential to ensure that it produces evidence-based, reliable, and impactful knowledge. Using the outlined paradigms and approaches can help the medical and patient communities harness AI’s potential while maintaining high patient care standards.
![Understanding the Long Haulers of COVID-19: Mixed Methods Analysis of YouTube Content Article Thumbnail](https://asset.jmir.pub/assets/2e39c8c2cd11f33eedb91f8454e5d637.png 480w,https://asset.jmir.pub/assets/2e39c8c2cd11f33eedb91f8454e5d637.png 960w,https://asset.jmir.pub/assets/2e39c8c2cd11f33eedb91f8454e5d637.png 1920w,https://asset.jmir.pub/assets/2e39c8c2cd11f33eedb91f8454e5d637.png 2500w)
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.
![Feasibility of Multimodal Artificial Intelligence Using GPT-4 Vision for the Classification of Middle Ear Disease: Qualitative Study and Validation Article Thumbnail](https://asset.jmir.pub/assets/a098eaa0636ca393835ac1804417deb2.png 480w,https://asset.jmir.pub/assets/a098eaa0636ca393835ac1804417deb2.png 960w,https://asset.jmir.pub/assets/a098eaa0636ca393835ac1804417deb2.png 1920w,https://asset.jmir.pub/assets/a098eaa0636ca393835ac1804417deb2.png 2500w)
The integration of artificial intelligence (AI), particularly deep learning models, has transformed the landscape of medical technology, especially in the field of diagnosis using imaging and physiological data. In otolaryngology, AI has shown promise in image classification for middle ear diseases. However, existing models often lack patient-specific data and clinical context, limiting their universal applicability. The emergence of GPT-4 Vision (GPT-4V) has enabled a multimodal diagnostic approach, integrating language processing with image analysis.
![Framework for Ranking Machine Learning Predictions of Limited, Multimodal, and Longitudinal Behavioral Passive Sensing Data: Combining User-Agnostic and Personalized Modeling Article Thumbnail](https://asset.jmir.pub/assets/ed9d4b3eefb54079da43db72a04cd69f.png 480w,https://asset.jmir.pub/assets/ed9d4b3eefb54079da43db72a04cd69f.png 960w,https://asset.jmir.pub/assets/ed9d4b3eefb54079da43db72a04cd69f.png 1920w,https://asset.jmir.pub/assets/ed9d4b3eefb54079da43db72a04cd69f.png 2500w)
Passive mobile sensing provides opportunities for measuring and monitoring health status in the wild and outside of clinics. However, longitudinal, multimodal mobile sensor data can be small, noisy, and incomplete. This makes processing, modeling, and prediction of these data challenging. The small size of the data set restricts it from being modeled using complex deep learning networks. The current state of the art (SOTA) tackles small sensor data sets following a singular modeling paradigm based on traditional machine learning (ML) algorithms. These opt for either a user-agnostic modeling approach, making the model susceptible to a larger degree of noise, or a personalized approach, where training on individual data alludes to a more limited data set, giving rise to overfitting, therefore, ultimately, having to seek a trade-off by choosing 1 of the 2 modeling approaches to reach predictions.
![A Comparison of Personalized and Generalized Approaches to Emotion Recognition Using Consumer Wearable Devices: Machine Learning Study Article Thumbnail](https://asset.jmir.pub/assets/ba1f00fcfa836fbdcc62c59b5e4b65b7.png 480w,https://asset.jmir.pub/assets/ba1f00fcfa836fbdcc62c59b5e4b65b7.png 960w,https://asset.jmir.pub/assets/ba1f00fcfa836fbdcc62c59b5e4b65b7.png 1920w,https://asset.jmir.pub/assets/ba1f00fcfa836fbdcc62c59b5e4b65b7.png 2500w)
There are a wide range of potential adverse health effects, ranging from headaches to cardiovascular disease, associated with long-term negative emotions and chronic stress. Because many indicators of stress are imperceptible to observers, the early detection of stress remains a pressing medical need, as it can enable early intervention. Physiological signals offer a noninvasive method for monitoring affective states and are recorded by a growing number of commercially available wearables.
Preprints Open for Peer-Review
There are no preprints available for open peer-review at this time. Please check back later.