TY - JOUR AU - Blaß, Marlene AU - Gimpel, Henner AU - Karnebogen, Philip PY - 2024/11/27 TI - A Taxonomy and Archetypes of AI-Based Health Care Services: Qualitative Study JO - J Med Internet Res SP - e53986 VL - 26 KW - healthcare KW - artificial intelligence KW - AI KW - taxonomy KW - services KW - cluster analysis KW - archetypes N2 - Background: To cope with the enormous burdens placed on health care systems around the world, from the strains and stresses caused by longer life expectancy to the large-scale emergency relief actions required by pandemics like COVID-19, many health care companies have been using artificial intelligence (AI) to adapt their services. Nevertheless, conceptual insights into how AI has been transforming the health care sector are still few and far between. This study aims to provide an overarching structure with which to classify the various real-world phenomena. A clear and comprehensive taxonomy will provide consensus on AI-based health care service offerings and sharpen the view of their adoption in the health care sector. Objective: The goal of this study is to identify the design characteristics of AI-based health care services. Methods: We propose a multilayered taxonomy created in accordance with an established method of taxonomy development. In doing so, we applied 268 AI-based health care services, conducted a structured literature review, and then evaluated the resulting taxonomy. Finally, we performed a cluster analysis to identify the archetypes of AI-based health care services. Results: We identified 4 critical perspectives: agents, data, AI, and health impact. Furthermore, a cluster analysis yielded 13 archetypes that demonstrate our taxonomy?s applicability. Conclusions: This contribution to conceptual knowledge of AI-based health care services enables researchers as well as practitioners to analyze such services and improve their theory-led design. UR - https://www.jmir.org/2024/1/e53986 UR - http://dx.doi.org/10.2196/53986 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/53986 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 - Zouzos, Athanasios AU - Milovanovic, Aleksandra AU - Dembrower, Karin AU - Strand, Fredrik PY - 2023/8/31 TI - Effect of Benign Biopsy Findings on an Artificial Intelligence?Based Cancer Detector in Screening Mammography: Retrospective Case-Control Study JO - JMIR AI SP - e48123 VL - 2 KW - artificial intelligence KW - AI KW - mammography KW - breast cancer KW - benign biopsy KW - screening KW - cancer screening KW - diagnostic KW - radiology KW - detection system N2 - Background: Artificial intelligence (AI)?based cancer detectors (CAD) for mammography are starting to be used for breast cancer screening in radiology departments. It is important to understand how AI CAD systems react to benign lesions, especially those that have been subjected to biopsy. Objective: Our goal was to corroborate the hypothesis that women with previous benign biopsy and cytology assessments would subsequently present increased AI CAD abnormality scores even though they remained healthy. Methods: This is a retrospective study applying a commercial AI CAD system (Insight MMG, version 1.1.4.3; Lunit Inc) to a cancer-enriched mammography screening data set of 10,889 women (median age 56, range 40-74 years). The AI CAD generated a continuous prediction score for tumor suspicion between 0.00 and 1.00, where 1.00 represented the highest level of suspicion. A binary read (flagged or not flagged) was defined on the basis of a predetermined cutoff threshold (0.40). The flagged median and proportion of AI scores were calculated for women who were healthy, those who had a benign biopsy finding, and those who were diagnosed with breast cancer. For women with a benign biopsy finding, the interval between mammography and the biopsy was used for stratification of AI scores. The effect of increasing age was examined using subgroup analysis and regression modeling. Results: Of a total of 10,889 women, 234 had a benign biopsy finding before or after screening. The proportions of flagged healthy women were 3.5%, 11%, and 84% for healthy women without a benign biopsy finding, those with a benign biopsy finding, and women with breast cancer, respectively (P<.001). For the 8307 women with complete information, radiologist 1, radiologist 2, and the AI CAD system flagged 8.5%, 6.8%, and 8.5% of examinations of women who had a prior benign biopsy finding. The AI score correlated only with increasing age of the women in the cancer group (P=.01). Conclusions: Compared to healthy women without a biopsy, the examined AI CAD system flagged a much larger proportion of women who had or would have a benign biopsy finding based on a radiologist?s decision. However, the flagging rate was not higher than that for radiologists. Further research should be focused on training the AI CAD system taking prior biopsy information into account. UR - https://ai.jmir.org/2023/1/e48123 UR - http://dx.doi.org/10.2196/48123 UR - http://www.ncbi.nlm.nih.gov/pubmed/38875554 ID - info:doi/10.2196/48123 ER - TY - JOUR AU - Sekandi, Nabbuye Juliet AU - Shi, Weili AU - Zhu, Ronghang AU - Kaggwa, Patrick AU - Mwebaze, Ernest AU - Li, Sheng PY - 2023/2/23 TI - Application of Artificial Intelligence to the Monitoring of Medication Adherence for Tuberculosis Treatment in Africa: Algorithm Development and Validation JO - JMIR AI SP - e40167 VL - 2 KW - artificial intelligence KW - deep learning KW - machine learning KW - medication adherence KW - digital technology KW - digital health KW - tuberculosis KW - video directly observed therapy KW - video therapy N2 - Background: Artificial intelligence (AI) applications based on advanced deep learning methods in image recognition tasks can increase efficiency in the monitoring of medication adherence through automation. AI has sparsely been evaluated for the monitoring of medication adherence in clinical settings. However, AI has the potential to transform the way health care is delivered even in limited-resource settings such as Africa. Objective: We aimed to pilot the development of a deep learning model for simple binary classification and confirmation of proper medication adherence to enhance efficiency in the use of video monitoring of patients in tuberculosis treatment. Methods: We used a secondary data set of 861 video images of medication intake that were collected from consenting adult patients with tuberculosis in an institutional review board?approved study evaluating video-observed therapy in Uganda. The video images were processed through a series of steps to prepare them for use in a training model. First, we annotated videos using a specific protocol to eliminate those with poor quality. After the initial annotation step, 497 videos had sufficient quality for training the models. Among them, 405 were positive samples, whereas 92 were negative samples. With some preprocessing techniques, we obtained 160 frames with a size of 224 × 224 in each video. We used a deep learning framework that leveraged 4 convolutional neural networks models to extract visual features from the video frames and automatically perform binary classification of adherence or nonadherence. We evaluated the diagnostic properties of the different models using sensitivity, specificity, F1-score, and precision. The area under the curve (AUC) was used to assess the discriminative performance and the speed per video review as a metric for model efficiency. We conducted a 5-fold internal cross-validation to determine the diagnostic and discriminative performance of the models. We did not conduct external validation due to a lack of publicly available data sets with specific medication intake video frames. Results: Diagnostic properties and discriminative performance from internal cross-validation were moderate to high in the binary classification tasks with 4 selected automated deep learning models. The sensitivity ranged from 92.8 to 95.8%, specificity from 43.5 to 55.4%, F1-score from 0.91 to 0.92, precision from 88% to 90.1%, and AUC from 0.78 to 0.85. The 3D ResNet model had the highest precision, AUC, and speed. Conclusions: All 4 deep learning models showed comparable diagnostic properties and discriminative performance. The findings serve as a reasonable proof of concept to support the potential application of AI in the binary classification of video frames to predict medication adherence. UR - https://ai.jmir.org/2023/1/e40167 UR - http://dx.doi.org/10.2196/40167 UR - http://www.ncbi.nlm.nih.gov/pubmed/38464947 ID - info:doi/10.2196/40167 ER - TY - JOUR AU - Choudhury, Avishek PY - 2022/6/21 TI - Toward an Ecologically Valid Conceptual Framework for the Use of Artificial Intelligence in Clinical Settings: Need for Systems Thinking, Accountability, Decision-making, Trust, and Patient Safety Considerations in Safeguarding the Technology and Clinicians JO - JMIR Hum Factors SP - e35421 VL - 9 IS - 2 KW - health care KW - artificial intelligence KW - ecological validity KW - trust in AI KW - clinical workload KW - patient safety KW - AI accountability KW - reliability UR - https://humanfactors.jmir.org/2022/2/e35421 UR - http://dx.doi.org/10.2196/35421 UR - http://www.ncbi.nlm.nih.gov/pubmed/35727615 ID - info:doi/10.2196/35421 ER -