{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T05:34:40Z","timestamp":1793424880565,"version":"4.5.0"},"reference-count":29,"publisher":"American Medical Association (AMA)","issue":"9","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Netw Open"],"abstract":"\n Importance<\/jats:title>\n Safe integration of artificial intelligence (AI) into clinical settings often requires randomized clinical trials (RCT) to compare AI efficacy with conventional care. Diabetic retinopathy (DR) screening is at the forefront of clinical AI applications, marked by the first US Food and Drug Administration (FDA) De Novo authorization for an autonomous AI for such use.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n To determine the generalizability of the 7 ethical research principles for clinical trials endorsed by the National Institute of Health (NIH), and identify ethical concerns unique to clinical trials of AI.<\/jats:p>\n <\/jats:sec>\n \n Design, Setting, and Participants<\/jats:title>\n This qualitative study included semistructured interviews conducted with 11 investigators engaged in the design and implementation of clinical trials of AI for DR screening from November 11, 2022, to February 20, 2023. The study was a collaboration with the ACCESS (AI for Children\u2019s Diabetic Eye Exams) trial, the first clinical trial of autonomous AI in pediatrics. Participant recruitment initially utilized purposeful sampling, and later expanded with snowball sampling. Study methodology for analysis combined a deductive approach to explore investigators\u2019 perspectives of the 7 ethical principles for clinical research endorsed by the NIH and an inductive approach to uncover the broader ethical considerations implementing clinical trials of AI within care delivery.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n A total of 11 participants (mean [SD] age, 47.5 [12.0] years; 7 male [64%], 4 female [36%]; 3 Asian [27%], 8 White [73%]) were included, with diverse expertise in ethics, ophthalmology, translational medicine, biostatistics, and AI development. Key themes revealed several ethical challenges unique to clinical trials of AI. These themes included difficulties in measuring social value, establishing scientific validity, ensuring fair participant selection, evaluating risk-benefit ratios across various patient subgroups, and addressing the complexities inherent in the data use terms of informed consent.<\/jats:p>\n <\/jats:sec>\n \n Conclusions and Relevance<\/jats:title>\n This qualitative study identified practical ethical challenges that investigators need to consider and negotiate when conducting AI clinical trials, exemplified by the DR screening use-case. These considerations call for further guidance on where to focus empirical and normative ethical efforts to best support conduct clinical trials of AI and minimize unintended harm to trial participants.<\/jats:p>\n <\/jats:sec>","DOI":"10.1001\/jamanetworkopen.2024.32482","type":"journal-article","created":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T11:36:42Z","timestamp":1725622602000},"page":"e2432482","source":"Crossref","is-referenced-by-count":45,"title":["Ethical Considerations in the Design and Conduct of Clinical Trials of Artificial Intelligence"],"prefix":"10.1001","volume":"7","author":[{"given":"Alaa","family":"Youssef","sequence":"first","affiliation":[{"name":"Departments of Radiology, Stanford University School of Medicine, Stanford, California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ariadne A.","family":"Nichol","sequence":"additional","affiliation":[{"name":"Center for Biomedical Ethics, Stanford University School of Medicine, Stanford, 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