{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T14:39:28Z","timestamp":1791297568477,"version":"4.1.0"},"reference-count":200,"publisher":"American Medical Association (AMA)","issue":"18","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA"],"published-print":{"date-parts":[[2025,11,11]]},"abstract":"<jats:sec id=\"ab-jsc250012-1\">\n                    <jats:title>Importance<\/jats:title>\n                    <jats:p>Artificial intelligence (AI) is changing health and health care on an unprecedented scale. Though the potential benefits are massive, so are the risks. The JAMA Summit on AI discussed how health and health care AI should be developed, evaluated, regulated, disseminated, and monitored.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"ab-jsc250012-2\">\n                    <jats:title>Observations<\/jats:title>\n                    <jats:p>Health and health care AI is wide-ranging, including clinical tools (eg, sepsis alerts or diabetic retinopathy screening software), technologies used by individuals with health concerns (eg, mobile health apps), tools used by health care systems to improve business operations (eg, revenue cycle management or scheduling), and hybrid tools supporting both business operations (eg, documentation and billing) and clinical activities (eg, suggesting diagnoses or treatment plans). Many AI tools are already widely adopted, especially for medical imaging, mobile health, health care business operations, and hybrid functions like scribing outpatient visits. All these tools can have important health effects (good or bad), but these effects are often not quantified because evaluations are extremely challenging or not required, in part because many are outside the US Food and Drug Administration\u2019s regulatory oversight. A major challenge in evaluation is that a tool\u2019s effects are highly dependent on the human-computer interface, user training, and setting in which the tool is used. Numerous efforts lay out standards for the responsible use of AI, but most focus on monitoring for safety (eg, detection of model hallucinations) or institutional compliance with various process measures, and do not address effectiveness (ie, demonstration of improved outcomes). Ensuring AI is deployed equitably and in a manner that improves health outcomes or, if improving efficiency of health care delivery, does so safely, requires progress in 4 areas. First, multistakeholder engagement throughout the total product life cycle is needed. This effort would include greater partnership of end users with developers in initial tool creation and greater partnership of developers, regulators, and health care systems in the evaluation of tools as they are deployed. Second, measurement tools for evaluation and monitoring should be developed and disseminated. Beyond proposed monitoring and certification initiatives, this will require new methods and expertise to allow health care systems to conduct or participate in rapid, efficient, and robust evaluations of effectiveness. The third priority is creation of a nationally representative data infrastructure and learning environment to support the generation of generalizable knowledge about health effects of AI tools across different settings. Fourth, an incentive structure should be promoted, using market forces and policy levers, to drive these changes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"ab-jsc250012-3\">\n                    <jats:title>Conclusions and Relevance<\/jats:title>\n                    <jats:p>AI will disrupt every part of health and health care delivery in the coming years. Given the many long-standing problems in health care, this disruption represents an incredible opportunity. However, the odds that this disruption will improve health for all will depend heavily on the creation of an ecosystem capable of rapid, efficient, robust, and generalizable knowledge about the consequences of these tools on health.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1001\/jama.2025.18490","type":"journal-article","created":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T16:30:18Z","timestamp":1760373018000},"page":"1650","source":"Crossref","is-referenced-by-count":193,"title":["AI, Health, and Health Care Today and Tomorrow"],"prefix":"10.1001","volume":"334","author":[{"given":"Derek C.","family":"Angus","sequence":"first","affiliation":[{"name":"JAMA, Chicago, Illinois"},{"name":"Yale University, New Haven, Connecticut"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rohan","family":"Khera","sequence":"additional","affiliation":[{"name":"JAMA, Chicago, Illinois"},{"name":"Yale University, New Haven, Connecticut"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tracy","family":"Lieu","sequence":"additional","affiliation":[{"name":"JAMA, Chicago, Illinois"},{"name":"Kaiser Permanente, Pleasanton, California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vincent","family":"Liu","sequence":"additional","affiliation":[{"name":"Kaiser Permanente, Pleasanton, California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Faraz S.","family":"Ahmad","sequence":"additional","affiliation":[{"name":"Northwestern University, Chicago, Illinois"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brian","family":"Anderson","sequence":"additional","affiliation":[{"name":"CHAI, Boston, Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sivasubramanium V.","family":"Bhavani","sequence":"additional","affiliation":[{"name":"Emory University, Atlanta, Georgia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Bindman","sequence":"additional","affiliation":[{"name":"Kaiser Permanente, Pleasanton, California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Troyen","family":"Brennan","sequence":"additional","affiliation":[{"name":"Harvard T.H. Chan School of Public Health, Boston, Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leo Anthony","family":"Celi","sequence":"additional","affiliation":[{"name":"MIT, Cambridge, Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frederick","family":"Chen","sequence":"additional","affiliation":[{"name":"American Medical Association, Chicago, Illinois"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"I. Glenn","family":"Cohen","sequence":"additional","affiliation":[{"name":"Harvard Law School, Cambridge, Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alastair","family":"Denniston","sequence":"additional","affiliation":[{"name":"University of Birmingham, Birmingham, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sanjay","family":"Desai","sequence":"additional","affiliation":[{"name":"American Medical Association, Chicago, Illinois"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Emb\u00ed","sequence":"additional","affiliation":[{"name":"Vanderbilt University Medical Center, Nashville, Tennessee"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aldo","family":"Faisal","sequence":"additional","affiliation":[{"name":"Imperial College London, London, United Kingdom"},{"name":"Universit\u00e4t Bayreuth, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kadija","family":"Ferryman","sequence":"additional","affiliation":[{"name":"Johns Hopkins University, Baltimore, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jackie","family":"Gerhart","sequence":"additional","affiliation":[{"name":"Epic, Verona, Wisconsin"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marielle","family":"Gross","sequence":"additional","affiliation":[{"name":"Johns Hopkins University, Baltimore, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tina","family":"Hernandez-Boussard","sequence":"additional","affiliation":[{"name":"Stanford University, Stanford, California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Howell","sequence":"additional","affiliation":[{"name":"Google, Mountain View, California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kevin","family":"Johnson","sequence":"additional","affiliation":[{"name":"University of Pennsylvania, Philadelphia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kristine","family":"Lee","sequence":"additional","affiliation":[{"name":"Kaiser Permanente, Pleasanton, California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoxuan","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Birmingham, Birmingham, United Kingdom"},{"name":"Microsoft AI, London, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kimberly","family":"Lomis","sequence":"additional","affiliation":[{"name":"American Medical Association, Chicago, Illinois"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alex John","family":"London","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, Pennsylvania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher A.","family":"Longhurst","sequence":"additional","affiliation":[{"name":"University of California, San Diego Health"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kenneth D.","family":"Mandl","sequence":"additional","affiliation":[{"name":"Boston Children\u2019s Hospital, Boston, Massachusetts"},{"name":"Harvard Medical School, Boston, Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elizabeth","family":"McGlynn","sequence":"additional","affiliation":[{"name":"Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michelle M.","family":"Mello","sequence":"additional","affiliation":[{"name":"Stanford University, Stanford, California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fatima","family":"Munoz","sequence":"additional","affiliation":[{"name":"San Ysidro Health, San Diego, California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lucila","family":"Ohno-Machado","sequence":"additional","affiliation":[{"name":"Yale School of Medicine, New Haven, Connecticut"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Ouyang","sequence":"additional","affiliation":[{"name":"Kaiser Permanente, Pleasanton, California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roy","family":"Perlis","sequence":"additional","affiliation":[{"name":"JAMA, Chicago, Illinois"},{"name":"Harvard Medical School, Boston, Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adam","family":"Phillips","sequence":"additional","affiliation":[{"name":"Apple, Cupertino, California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Rhew","sequence":"additional","affiliation":[{"name":"Microsoft, Redmond, Washington"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph S.","family":"Ross","sequence":"additional","affiliation":[{"name":"JAMA, Chicago, Illinois"},{"name":"Yale School of Medicine, New Haven, Connecticut"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suchi","family":"Saria","sequence":"additional","affiliation":[{"name":"Johns Hopkins University, Baltimore, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lee","family":"Schwamm","sequence":"additional","affiliation":[{"name":"Yale School of Medicine, New Haven, Connecticut"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher W.","family":"Seymour","sequence":"additional","affiliation":[{"name":"JAMA, Chicago, Illinois"},{"name":"University of Pittsburgh, Pittsburgh, Pennsylvania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nigam H.","family":"Shah","sequence":"additional","affiliation":[{"name":"Stanford University, Stanford, California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rashmee","family":"Shah","sequence":"additional","affiliation":[{"name":"Meta, Menlo Park, California"},{"name":"University of Utah, Salt Lake City, Utah"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karandeep","family":"Singh","sequence":"additional","affiliation":[{"name":"University of California, San Diego"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew","family":"Solomon","sequence":"additional","affiliation":[{"name":"Sutter Health, Sacramento, California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kathryn","family":"Spates","sequence":"additional","affiliation":[{"name":"The Joint Commission, Oakbrook Terrace, Illinois"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kayte","family":"Spector-Bagdady","sequence":"additional","affiliation":[{"name":"University of Michigan Medical School, Ann Arbor"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tommy","family":"Wang","sequence":"additional","affiliation":[{"name":"Health Care and Organizational Economist, Palo Alto, California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Judy Wawira","family":"Gichoya","sequence":"additional","affiliation":[{"name":"Emory University, Atlanta, Georgia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James","family":"Weinstein","sequence":"additional","affiliation":[{"name":"Microsoft, Redmond, Washington"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jenna","family":"Wiens","sequence":"additional","affiliation":[{"name":"University of Michigan, Ann Arbor"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kirsten","family":"Bibbins-Domingo","sequence":"additional","affiliation":[{"name":"Editor in Chief, JAMA and the JAMA Network, Chicago, Illinois"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"JAMA Summit on AI","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gil","family":"Alterovitz","sequence":"additional","affiliation":[{"name":"for the JAMA Summit on AI"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heather A","family":"Clancy","sequence":"additional","affiliation":[{"name":"for the JAMA Summit on AI"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lindsay","family":"Dawson","sequence":"additional","affiliation":[{"name":"for the JAMA Summit on AI"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew","family":"Diamond","sequence":"additional","affiliation":[{"name":"for the JAMA Summit on AI"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erin C","family":"Holve","sequence":"additional","affiliation":[{"name":"for the JAMA Summit on AI"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeremy","family":"Kahn","sequence":"additional","affiliation":[{"name":"for the JAMA Summit on AI"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yolande M","family":"Pengetnze","sequence":"additional","affiliation":[{"name":"for the JAMA Summit on AI"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiv","family":"Rao","sequence":"additional","affiliation":[{"name":"for the JAMA Summit on AI"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William H","family":"Shrank","sequence":"additional","affiliation":[{"name":"for the JAMA Summit on AI"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cesar","family":"Termulo","sequence":"additional","affiliation":[{"name":"for the JAMA Summit on AI"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10","reference":[{"issue":"1","key":"jsc250012r1","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1038\/s41591-021-01614-0","article-title":"AI in health and medicine.","volume":"28","author":"Rajpurkar","year":"2022","journal-title":"Nat Med"},{"issue":"1","key":"jsc250012r3","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1001\/jama.2023.25054","article-title":"Will generative artificial intelligence deliver on its promise in health care?","volume":"331","author":"Wachter","year":"2024","journal-title":"JAMA"},{"issue":"1","key":"jsc250012r4","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1186\/s12909-023-04698-z","article-title":"Revolutionizing healthcare: the role of artificial intelligence in clinical practice.","volume":"23","author":"Alowais","year":"2023","journal-title":"BMC Med Educ"},{"issue":"10","key":"jsc250012r5","doi-asserted-by":"publisher","first-page":"787","DOI":"10.1001\/jama.2024.12630","article-title":"The compelling need for shared responsibility of AI oversight: lessons from health IT certification.","volume":"332","author":"Ratwani","year":"2024","journal-title":"JAMA"},{"issue":"9","key":"jsc250012r6","doi-asserted-by":"publisher","first-page":"1337","DOI":"10.1038\/s41591-019-0548-6","article-title":"Do no harm: a roadmap for responsible machine learning for health care.","volume":"25","author":"Wiens","year":"2019","journal-title":"Nat Med"},{"issue":"3","key":"jsc250012r7","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1001\/jama.2024.21451","article-title":"FDA perspective on the regulation of artificial intelligence in health care and biomedicine.","volume":"333","author":"Warraich","year":"2025","journal-title":"JAMA"},{"issue":"3","key":"jsc250012r8","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1001\/jama.2023.25057","article-title":"Three epochs of artificial intelligence in health care.","volume":"331","author":"Howell","year":"2024","journal-title":"JAMA"},{"issue":"22","key":"jsc250012r9","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1001\/jama.2016.17216","article-title":"Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs.","volume":"316","author":"Gulshan","year":"2016","journal-title":"JAMA"},{"issue":"4","key":"jsc250012r10","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1001\/jama.2025.8731","article-title":"Complete AI-enabled echocardiography interpretation with multitask deep learning.","volume":"334","author":"Holste","year":"2025","journal-title":"JAMA"},{"issue":"3","key":"jsc250012r11","doi-asserted-by":"publisher","DOI":"10.1056\/AIoa2300032","article-title":"Evaluation of sepsis prediction models before onset of treatment.","volume":"1","author":"Kamran","year":"2024","journal-title":"NEJM"},{"issue":"8","key":"jsc250012r12","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.1001\/jamainternmed.2021.2626","article-title":"External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients.","volume":"181","author":"Wong","year":"2021","journal-title":"JAMA Intern Med"},{"key":"jsc250012r13","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2020.100377","article-title":"Diabetic retinopathy detection through deep learning techniques: a review.","volume":"20","author":"Alyoubi","year":"2020","journal-title":"Inform Med Unlocked"},{"key":"jsc250012r14","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.ahj.2024.08.004","article-title":"Use of artificial intelligence-guided echocardiography to detect cardiac dysfunction and heart valve disease in rural and remote areas: rationale and design of the AGILE-echo trial.","volume":"277","author":"Soh","year":"2024","journal-title":"Am Heart J"},{"issue":"1","key":"jsc250012r15","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1038\/s41746-023-00986-6","article-title":"Impact of a deep learning sepsis prediction model on quality of care and survival.","volume":"7","author":"Boussina","year":"2024","journal-title":"NPJ Digit Med"},{"issue":"9","key":"jsc250012r16","doi-asserted-by":"publisher","first-page":"763","DOI":"10.1001\/jama.2024.25982","article-title":"Electronic sepsis screening among patients admitted to hospital wards: a stepped-wedge cluster randomized trial.","volume":"333","author":"Arabi","year":"2025","journal-title":"JAMA"},{"issue":"9","key":"jsc250012r17","doi-asserted-by":"publisher","first-page":"e2434197","DOI":"10.1001\/jamanetworkopen.2024.34197","article-title":"The precision resuscitation with crystalloids in sepsis (PRECISE) trial: a trial protocol.","volume":"7","author":"Bhavani","year":"2024","journal-title":"JAMA Netw Open"},{"issue":"299","key":"jsc250012r18","doi-asserted-by":"publisher","DOI":"10.1126\/scitranslmed.aab3719","article-title":"A targeted real-time early warning score (TREWScore) for septic shock.","volume":"7","author":"Henry","year":"2015","journal-title":"Sci Transl Med"},{"issue":"7","key":"jsc250012r19","doi-asserted-by":"publisher","first-page":"1455","DOI":"10.1038\/s41591-022-01894-0","article-title":"Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis.","volume":"28","author":"Adams","year":"2022","journal-title":"Nat Med"},{"issue":"1","key":"jsc250012r21","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1186\/s13000-023-01375-z","article-title":"Artificial intelligence in diagnostic pathology.","volume":"18","author":"Shafi","year":"2023","journal-title":"Diagn Pathol"},{"issue":"21","key":"jsc250012r22","doi-asserted-by":"publisher","first-page":"1981","DOI":"10.1056\/NEJMra2301725","article-title":"The current and future state of AI interpretation of medical images.","volume":"388","author":"Rajpurkar","year":"2023","journal-title":"N Engl J Med"},{"issue":"7","key":"jsc250012r23","doi-asserted-by":"publisher","first-page":"1093","DOI":"10.1093\/jamia\/ocaf065","article-title":"Adoption of artificial intelligence in healthcare: survey of health system priorities, successes, and challenges.","volume":"32","author":"Poon","year":"2025","journal-title":"J Am Med Inform Assoc"},{"key":"jsc250012r26","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2024.102861","article-title":"Challenges and strategies for wide-scale artificial intelligence (AI) deployment in healthcare practices: a perspective for healthcare organizations.","volume":"151","author":"Esmaeilzadeh","year":"2024","journal-title":"Artif Intell Med"},{"issue":"1","key":"jsc250012r27","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1038\/s41746-022-00609-6","article-title":"Paying for artificial intelligence in medicine.","volume":"5","author":"Parikh","year":"2022","journal-title":"NPJ Digit Med"},{"issue":"6","key":"jsc250012r28","doi-asserted-by":"publisher","first-page":"1525","DOI":"10.1038\/s41591-024-02870-6","article-title":"Off-label use of artificial intelligence models in healthcare.","volume":"30","author":"Krishnamoorthy","year":"2024","journal-title":"Nat Med"},{"issue":"1","key":"jsc250012r29","doi-asserted-by":"publisher","DOI":"10.1016\/j.hlpt.2022.100602","article-title":"Organizational, professional, and patient characteristics associated with artificial intelligence adoption in healthcare: a systematic review.","volume":"11","author":"Khanijahani","year":"2022","journal-title":"Health Policy Technol"},{"issue":"23","key":"jsc250012r30","doi-asserted-by":"publisher","first-page":"2275","DOI":"10.1001\/jama.2023.22295","article-title":"Measuring the impact of AI in the diagnosis of hospitalized patients: a randomized clinical vignette survey study.","volume":"330","author":"Jabbour","year":"2023","journal-title":"JAMA"},{"issue":"23","key":"jsc250012r31","doi-asserted-by":"publisher","first-page":"2255","DOI":"10.1001\/jama.2023.22557","article-title":"Automation bias and assistive AI: risk of harm from AI-driven clinical decision support.","volume":"330","author":"Khera","year":"2023","journal-title":"JAMA"},{"issue":"1","key":"jsc250012r32","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1038\/s41746-025-01586-2","article-title":"Opportunities and risks of artificial intelligence in patient portal messaging in primary care.","volume":"8","author":"Biro","year":"2025","journal-title":"NPJ Digit Med"},{"issue":"19","key":"jsc250012r33","doi-asserted-by":"publisher","first-page":"1667","DOI":"10.1001\/jama.2025.0946","article-title":"How AI could reshape health care-rise in direct-to-consumer models.","volume":"333","author":"Mandl","year":"2025","journal-title":"JAMA"},{"issue":"5","key":"jsc250012r34","doi-asserted-by":"publisher","first-page":"1269","DOI":"10.1038\/s41591-024-02943-6","article-title":"The health risks of generative AI-based wellness apps.","volume":"30","author":"De Freitas","year":"2024","journal-title":"Nat Med"},{"issue":"6","key":"jsc250012r36","doi-asserted-by":"publisher","first-page":"646","DOI":"10.1001\/jamadermatol.2024.0468","article-title":"Current state of dermatology mobile applications with artificial intelligence features.","volume":"160","author":"Wongvibulsin","year":"2024","journal-title":"JAMA Dermatol"},{"issue":"1","key":"jsc250012r37","doi-asserted-by":"publisher","DOI":"10.2196\/44838","article-title":"An overview of chatbot-based mobile mental health apps: insights from app description and user reviews.","volume":"11","author":"Haque","year":"2023","journal-title":"JMIR Mhealth Uhealth"},{"key":"jsc250012r38","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2024.105560","article-title":"Smartwatch interventions in healthcare: a systematic review of the literature.","volume":"190","author":"Triantafyllidis","year":"2024","journal-title":"Int J Med Inform"},{"key":"jsc250012r39","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/j.jbi.2016.09.001","article-title":"Health at hand: a systematic review of smart watch uses for health and wellness.","volume":"63","author":"Reeder","year":"2016","journal-title":"J Biomed Inform"},{"issue":"20","key":"jsc250012r41","doi-asserted-by":"publisher","first-page":"1769","DOI":"10.1001\/jama.2025.3308","article-title":"Regulation of health and health care artificial intelligence.","volume":"333","author":"Mello","year":"2025","journal-title":"JAMA"},{"issue":"2","key":"jsc250012r42","doi-asserted-by":"publisher","DOI":"10.1093\/jlb\/lsac015","article-title":"Skating the line between general wellness products and regulated devices: strategies and implications.","volume":"9","author":"Simon","year":"2022","journal-title":"J Law Biosci"},{"issue":"6","key":"jsc250012r43","doi-asserted-by":"publisher","first-page":"800","DOI":"10.1177\/15598276241266784","article-title":"Health & wellness coaching services: making the case for reimbursement.","volume":"19","author":"Abu Dabrh","year":"2024","journal-title":"Am J Lifestyle Med"},{"issue":"1","key":"jsc250012r44","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1038\/s41746-019-0212-z","article-title":"Beyond validation: getting health apps into clinical practice.","volume":"3","author":"Gordon","year":"2020","journal-title":"NPJ Digit Med"},{"issue":"8","key":"jsc250012r45","doi-asserted-by":"publisher","DOI":"10.2196\/14724","article-title":"Reimbursement of apps for mental health: findings from interviews.","volume":"6","author":"Powell","year":"2019","journal-title":"JMIR Ment Health"},{"key":"jsc250012r46","doi-asserted-by":"publisher","DOI":"10.2196\/50342","article-title":"Existing barriers faced by and future design recommendations for direct-to-consumer health care artificial intelligence apps: scoping review.","volume":"25","author":"He","year":"2023","journal-title":"J Med Internet Res"},{"key":"jsc250012r47","doi-asserted-by":"publisher","DOI":"10.2196\/43808","article-title":"Problems and barriers related to the use of digital health applications: scoping review.","volume":"25","author":"Giebel","year":"2023","journal-title":"J Med Internet Res"},{"issue":"1","key":"jsc250012r48","first-page":"43","article-title":"Disruptive technologies: Catching the wave.","volume":"73","author":"Bower","year":"1995","journal-title":"Harv Bus Rev"},{"issue":"11","key":"jsc250012r51","doi-asserted-by":"publisher","DOI":"10.1056\/AIcs2400420","article-title":"Large language models for more efficient reporting of hospital quality measures.","volume":"1","author":"Boussina","year":"2024","journal-title":"NEJM AI"},{"key":"jsc250012r52","doi-asserted-by":"publisher","DOI":"10.1016\/j.health.2023.100245","article-title":"A forecasting approach for hospital bed capacity planning using machine learning and deep learning with application to public hospitals.","volume":"4","author":"Mahmoudian","year":"2023","journal-title":"Healthc Anal (N Y)"},{"issue":"03","key":"jsc250012r53","doi-asserted-by":"publisher","first-page":"141","DOI":"10.37547\/tajet\/Volume07Issue03-14","article-title":"Optimizing revenue cycle management in healthcare: AI and IT solutions for business process automation.","volume":"7","author":"Jalil","year":"2025","journal-title":"The American Journal of Engineering and Technology."},{"issue":"4","key":"jsc250012r54","doi-asserted-by":"publisher","DOI":"10.1016\/j.hlpt.2023.100824","article-title":"Artificial intelligence for patient scheduling in the real-world health care setting: a metanarrative review.","volume":"12","author":"Knight","year":"2023","journal-title":"Health Policy Technol"},{"issue":"3","key":"jsc250012r55","doi-asserted-by":"publisher","first-page":"862","DOI":"10.1016\/j.radi.2024.03.014","article-title":"\u2018You make the call\u2019: improving radiology staff scheduling with AI-generated self-rostering in a medical imaging department.","volume":"30","author":"O\u2019Callahan","year":"2024","journal-title":"Radiography (Lond)"},{"key":"jsc250012r56","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-023-08906-2","article-title":"Effective use of artificial intelligence in healthcare supply chain resilience using fuzzy decision-making model.","author":"Deveci","year":"2023","journal-title":"Soft Comput"},{"key":"jsc250012r57","doi-asserted-by":"publisher","DOI":"10.32996\/jcsts.2024.6.5.8","article-title":"AI in healthcare supply chain management: enhancing efficiency and reducing costs with predictive analytics.","author":"Khan","year":"2024","journal-title":"J Comput Sci Technol Stud"},{"issue":"1","key":"jsc250012r58","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1001\/jamaneurol.2023.4324","article-title":"Streamlining prior authorization to improve care.","volume":"81","author":"Busis","year":"2024","journal-title":"JAMA Neurol"},{"issue":"3","key":"jsc250012r59","doi-asserted-by":"publisher","first-page":"35","DOI":"10.63282\/3050-9262.IJAIDSML-V3I3P104","article-title":"Automating prior authorization decisions using machine learning and health claim data.","volume":"3","author":"Anand","year":"2022","journal-title":"Int J Artif Intel, Data Sci, Machine Learning"},{"issue":"3","key":"jsc250012r61","doi-asserted-by":"publisher","first-page":"85","DOI":"10.9734\/jerr\/2023\/v25i3893","article-title":"Rise of artificial intelligence in business and industry.","volume":"25","author":"Bharadiya","year":"2023","journal-title":"J Eng Res Rep"},{"issue":"12","key":"jsc250012r62","doi-asserted-by":"publisher","DOI":"10.1056\/AIoa2400659","article-title":"Does AI-powered clinical documentation enhance clinician efficiency? a longitudinal study.","volume":"1","author":"Liu","year":"2024","journal-title":"NEJM AI"},{"issue":"3","key":"jsc250012r63","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocae295","article-title":"Ambient artificial intelligence scribes to alleviate the burden of clinical documentation.","volume":"5","author":"Tierney","year":"2024","journal-title":"NEJM Catalyst"},{"key":"jsc250012r64","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.jdin.2024.02.009","article-title":"Artificial intelligence-driven digital scribes in clinical documentation: pilot study assessing the impact on dermatologist workflow and patient encounters.","volume":"15","author":"Cao","year":"2024","journal-title":"JAAD Int"},{"key":"jsc250012r65","doi-asserted-by":"publisher","DOI":"10.1056\/CAT.25.0040","article-title":"Ambient Artificial Intelligence Scribes: Learnings after 1 Year and over 2.5 Million Uses.","author":"Tierney","year":"2025","journal-title":"NEJM Catalys"},{"issue":"2","key":"jsc250012r66","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1001\/jama.2024.4088","article-title":"The integration of clinical trials with the practice of medicine: repairing a house divided.","volume":"332","author":"Angus","year":"2024","journal-title":"JAMA"},{"issue":"7","key":"jsc250012r67","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1136\/bmjqs-2025-018559","article-title":"Understanding the evidence for artificial intelligence in healthcare.","volume":"34","author":"Jackson","year":"2025","journal-title":"BMJ Qual Saf"},{"issue":"8","key":"jsc250012r68","doi-asserted-by":"publisher","first-page":"1040","DOI":"10.1001\/jamainternmed.2021.3333","article-title":"The epic sepsis model falls short-the importance of external validation.","volume":"181","author":"Habib","year":"2021","journal-title":"JAMA Intern Med"},{"issue":"1","key":"jsc250012r69","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1038\/s41746-024-01066-z","article-title":"Integrating artificial intelligence into healthcare systems: more than just the algorithm.","volume":"7","author":"Kwong","year":"2024","journal-title":"NPJ Digit Med"},{"key":"jsc250012r70","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-publhealth-052020-103738","article-title":"A review of the quality and impact of mobile health apps.","author":"Grundy","year":"2022","journal-title":"Ann Rev Public Health"},{"issue":"6223","key":"jsc250012r78","doi-asserted-by":"publisher","first-page":"720","DOI":"10.1126\/science.aaa2362","article-title":"Health care policy. Randomize evaluations to improve health care delivery.","volume":"347","author":"Finkelstein","year":"2015","journal-title":"Science"},{"issue":"12","key":"jsc250012r79","doi-asserted-by":"publisher","first-page":"1121","DOI":"10.1038\/s42256-022-00563-8","article-title":"Generalizability of an acute kidney injury prediction model across health systems.","volume":"4","author":"Cao","year":"2022","journal-title":"Nat Mach Intell"},{"issue":"9","key":"jsc250012r80","doi-asserted-by":"publisher","first-page":"759","DOI":"10.1001\/jama.2024.25818","article-title":"Do sepsis alerts help?","volume":"333","author":"Angus","year":"2025","journal-title":"JAMA"},{"issue":"11","key":"jsc250012r81","doi-asserted-by":"publisher","first-page":"e2135286","DOI":"10.1001\/jamanetworkopen.2021.35286","article-title":"Quantification of sepsis model alerts in 24 us hospitals before and during the COVID-19 pandemic.","volume":"4","author":"Wong","year":"2021","journal-title":"JAMA Netw Open"},{"issue":"1","key":"jsc250012r82","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1038\/s41746-020-00318-y","article-title":"Developing a delivery science for artificial intelligence in healthcare.","volume":"3","author":"Li","year":"2020","journal-title":"NPJ Digit Med"},{"issue":"14","key":"jsc250012r83","doi-asserted-by":"publisher","first-page":"1351","DOI":"10.1001\/jama.2019.10306","article-title":"Making machine learning models clinically useful.","volume":"322","author":"Shah","year":"2019","journal-title":"JAMA"},{"issue":"11","key":"jsc250012r84","doi-asserted-by":"publisher","first-page":"e745","DOI":"10.1016\/S2589-7500(21)00208-9","article-title":"The false hope of current approaches to explainable artificial intelligence in health care.","volume":"3","author":"Ghassemi","year":"2021","journal-title":"Lancet Digit Health"},{"issue":"7957","key":"jsc250012r85","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1038\/s41586-023-05947-3","article-title":"Blinded, randomized trial of sonographer versus AI cardiac function assessment.","volume":"616","author":"He","year":"2023","journal-title":"Nature"},{"issue":"20","key":"jsc250012r86","doi-asserted-by":"publisher","first-page":"1909","DOI":"10.1056\/NEJMoa1901183","article-title":"Large-scale assessment of a smartwatch to identify atrial fibrillation.","volume":"381","author":"Perez","year":"2019","journal-title":"N Engl J Med"},{"issue":"5","key":"jsc250012r87","doi-asserted-by":"publisher","first-page":"e367","DOI":"10.1016\/S2589-7500(24)00047-5","article-title":"Randomised controlled trials evaluating artificial intelligence in clinical practice: a scoping review.","volume":"6","author":"Han","year":"2024","journal-title":"Lancet Digit Health"},{"issue":"9","key":"jsc250012r88","doi-asserted-by":"publisher","first-page":"1318","DOI":"10.1038\/s41591-020-1042-x","article-title":"Welcoming new guidelines for AI clinical research.","volume":"26","author":"Topol","year":"2020","journal-title":"Nat Med"},{"issue":"1","key":"jsc250012r89","doi-asserted-by":"publisher","first-page":"1619","DOI":"10.1038\/s41467-024-45355-3","article-title":"Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines.","volume":"15","author":"Martindale","year":"2024","journal-title":"Nat Commun"},{"issue":"11","key":"jsc250012r90","doi-asserted-by":"publisher","first-page":"1043","DOI":"10.1001\/jama.2020.1039","article-title":"Randomized clinical trials of artificial intelligence.","volume":"323","author":"Angus","year":"2020","journal-title":"JAMA"},{"issue":"4","key":"jsc250012r91","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1038\/s41591-021-01312-x","article-title":"How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals.","volume":"27","author":"Wu","year":"2021","journal-title":"Nat Med"},{"key":"jsc250012r92","doi-asserted-by":"publisher","DOI":"10.3389\/frhs.2022.1053496","article-title":"Reflections on 10 years of effectiveness-implementation hybrid studies.","volume":"2","author":"Curran","year":"2022","journal-title":"Front Health Serv"},{"issue":"10","key":"jsc250012r93","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1038\/s41573-019-0034-3","article-title":"Adaptive platform trials: definition, design, conduct and reporting considerations.","volume":"18","author":"Angus","year":"2019","journal-title":"Nat Rev Drug Discov"},{"issue":"16","key":"jsc250012r94","doi-asserted-by":"publisher","first-page":"1619","DOI":"10.1001\/jama.2015.2316","article-title":"The platform trial: an efficient strategy for evaluating multiple treatments.","volume":"313","author":"Berry","year":"2015","journal-title":"JAMA"},{"issue":"1","key":"jsc250012r95","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1056\/NEJMoa1513750","article-title":"Adaptive randomization of neratinib in early breast cancer.","volume":"375","author":"Park","year":"2016","journal-title":"N Engl J Med"},{"issue":"1","key":"jsc250012r96","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1186\/s13063-020-04997-6","article-title":"Implementation of the randomized embedded multifactorial adaptive platform for COVID-19 (REMAP-COVID) trial in a US health system: lessons learned and recommendations.","volume":"22","author":"Huang","year":"2021","journal-title":"Trials"},{"key":"jsc250012r97","doi-asserted-by":"publisher","DOI":"10.1016\/j.cct.2022.106822","article-title":"The comparative effectiveness of COVID-19 monoclonal antibodies: a learning health system randomized clinical trial.","volume":"119","author":"McCreary","year":"2022","journal-title":"Contemp Clin Trials"},{"issue":"1","key":"jsc250012r98","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1186\/s13063-021-05316-3","article-title":"The UPMC OPTIMISE-C19 (Optimizing Treatment and Impact of Monoclonal Antibodies Through Evaluation for COVID-19) trial: a structured summary of a study protocol for an open-label, pragmatic, comparative effectiveness platform trial with response-adaptive randomization.","volume":"22","author":"Huang","year":"2021","journal-title":"Trials"},{"issue":"2","key":"jsc250012r99","doi-asserted-by":"publisher","DOI":"10.1093\/jamiaopen\/ooae023","article-title":"Integrating clinical research into electronic health record workflows to support a learning health system.","volume":"7","author":"Goldhaber","year":"2024","journal-title":"JAMIA Open"},{"issue":"12","key":"jsc250012r100","doi-asserted-by":"publisher","first-page":"1175","DOI":"10.1056\/NEJMsb1900856","article-title":"Creating a learning health system through rapid-cycle, randomized testing.","volume":"381","author":"Horwitz","year":"2019","journal-title":"N Engl J Med"},{"issue":"5","key":"jsc250012r103","first-page":"74","article-title":"The surprising power of online experiments.","volume":"95","author":"Kohavi","year":"2017","journal-title":"Harv Bus Rev"},{"issue":"22","key":"jsc250012r105","doi-asserted-by":"publisher","first-page":"10723","DOI":"10.1073\/pnas.1820701116","article-title":"Objecting to experiments that compare two unobjectionable policies or treatments.","volume":"116","author":"Meyer","year":"2019","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"21","key":"jsc250012r106","doi-asserted-by":"publisher","first-page":"1845","DOI":"10.1001\/jama.2024.7741","article-title":"Causal inference about the effects of interventions from observational studies in medical journals.","volume":"331","author":"Dahabreh","year":"2024","journal-title":"JAMA"},{"issue":"1","key":"jsc250012r107","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1038\/s41746-024-01270-x","article-title":"A scoping review of reporting gaps in FDA-approved AI medical devices.","volume":"7","author":"Muralidharan","year":"2024","journal-title":"NPJ Digit Med"},{"issue":"7","key":"jsc250012r108","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1080\/17483107.2019.1701103","article-title":"Mobile health app usability and quality rating scales: a systematic review.","volume":"16","author":"Azad-Khaneghah","year":"2021","journal-title":"Disabil Rehabil Assist Technol"},{"key":"jsc250012r110","doi-asserted-by":"publisher","DOI":"10.1056\/CAT.24.0131","article-title":"Standing on FURM ground: a framework for evaluating fair, useful, and reliable AI models in health care systems.","author":"Callahan","year":"2024","journal-title":"NEJM Catalyst"},{"issue":"8","key":"jsc250012r111","doi-asserted-by":"publisher","DOI":"10.1056\/AIp2400223","article-title":"A call for artificial intelligence implementation science centers to evaluate clinical effectiveness.","volume":"1","author":"Longhurst","year":"2024","journal-title":"NEJM"},{"issue":"9","key":"jsc250012r112","doi-asserted-by":"publisher","first-page":"1631","DOI":"10.1093\/jamia\/ocac078","article-title":"A framework for the oversight and local deployment of safe and high-quality prediction models.","volume":"29","author":"Bedoya","year":"2022","journal-title":"J Am Med Inform Assoc"},{"issue":"1","key":"jsc250012r113","doi-asserted-by":"publisher","DOI":"10.1136\/bmjhci-2021-100444","article-title":"Evaluation framework to guide implementation of AI systems into healthcare settings.","volume":"28","author":"Reddy","year":"2021","journal-title":"BMJ Health Care Inform"},{"key":"jsc250012r114","doi-asserted-by":"publisher","DOI":"10.3389\/fdgth.2023.1004130","article-title":"Determinants for scalable adoption of autonomous AI in the detection of diabetic eye disease in diverse practice types: key best practices learned through collection of real-world data.","volume":"5","author":"Goldstein","year":"2023","journal-title":"Front Digit Health"},{"key":"jsc250012r115","doi-asserted-by":"publisher","DOI":"10.2337\/db24-57-OR","article-title":"Enhancing diabetic eye disease detection through autonomous artificial intelligence implementation in a federally qualified health center.","volume":"73","author":"Castro","year":"2024","journal-title":"Diabetes"},{"issue":"1","key":"jsc250012r116","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1136\/medhum-2021-012318","article-title":"(De)troubling transparency: artificial intelligence (AI) for clinical applications.","volume":"49","author":"Winter","year":"2023","journal-title":"Med Humanit"},{"issue":"1","key":"jsc250012r117","doi-asserted-by":"publisher","first-page":"1163","DOI":"10.1186\/s12913-023-10098-2","article-title":"Exploring patient perspectives on how they can and should be engaged in the development of artificial intelligence (AI) applications in health care.","volume":"23","author":"Adus","year":"2023","journal-title":"BMC Health Serv Res"},{"key":"jsc250012r118","first-page":"25","article-title":"The EU AI Act: a summary of its significance and scope.","volume":"1","author":"Edwards","year":"2021","journal-title":"Artif Intell"},{"issue":"9","key":"jsc250012r122","doi-asserted-by":"publisher","DOI":"10.1016\/j.labinv.2024.102111","article-title":"Implementation of digital pathology and artificial intelligence in routine pathology practice.","volume":"104","author":"Zhang","year":"2024","journal-title":"Lab Invest"},{"issue":"9","key":"jsc250012r124","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1001\/jama.2023.15481","article-title":"AI in medicine: JAMA\u2019s focus on clinical outcomes, patient-centered care, quality, and equity.","volume":"330","author":"Khera","year":"2023","journal-title":"JAMA"},{"issue":"14","key":"jsc250012r125","doi-asserted-by":"publisher","first-page":"1397","DOI":"10.1001\/jama.2020.9371","article-title":"The case for algorithmic stewardship for artificial intelligence and machine learning technologies.","volume":"324","author":"Eaneff","year":"2020","journal-title":"JAMA"},{"issue":"13","key":"jsc250012r126","doi-asserted-by":"publisher","first-page":"1171","DOI":"10.1056\/NEJMp2406135","article-title":"Avoiding financial toxicity for patients from clinicians\u2019 use of AI.","volume":"391","author":"Jain","year":"2024","journal-title":"N Engl J Med"},{"issue":"11","key":"jsc250012r127","doi-asserted-by":"publisher","first-page":"2686","DOI":"10.1038\/s41591-023-02540-z","article-title":"External validation of AI models in health should be replaced with recurring local validation.","volume":"29","author":"Youssef","year":"2023","journal-title":"Nat Med"},{"issue":"12","key":"jsc250012r128","doi-asserted-by":"publisher","first-page":"2011","DOI":"10.1093\/jamia\/ocaa088","article-title":"MINIMAR (minimum information for medical ai reporting): developing reporting standards for artificial intelligence in health care.","volume":"27","author":"Hernandez-Boussard","year":"2020","journal-title":"J Am Med Inform Assoc"},{"issue":"1","key":"jsc250012r129","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1038\/s41746-025-01506-4","article-title":"Clinical trials informed framework for real world clinical implementation and deployment of artificial intelligence applications.","volume":"8","author":"You","year":"2025","journal-title":"NPJ Digit Med"},{"issue":"11","key":"jsc250012r131","doi-asserted-by":"publisher","first-page":"2730","DOI":"10.1093\/jamia\/ocae209","article-title":"Toward a responsible future: recommendations for AI-enabled clinical decision support.","volume":"31","author":"Labkoff","year":"2024","journal-title":"J Am Med Inform Assoc"},{"issue":"1","key":"jsc250012r132","doi-asserted-by":"publisher","first-page":"28","DOI":"10.3390\/ai4010003","article-title":"Ethics & AI: a systematic review on ethical concerns and related strategies for designing with AI in healthcare.","volume":"4","author":"Li","year":"2022","journal-title":"AI"},{"issue":"2","key":"jsc250012r134","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1353\/hpu.2021.0065","article-title":"A proposed framework on integrating health equity and racial justice into the artificial intelligence development lifecycle.","volume":"32","author":"Dankwa-Mullan","year":"2021","journal-title":"J Health Care Poor Underserved"},{"issue":"3","key":"jsc250012r135","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1001\/jama.2023.26930","article-title":"A nationwide network of health AI assurance laboratories.","volume":"331","author":"Shah","year":"2024","journal-title":"JAMA"},{"issue":"17","key":"jsc250012r136","doi-asserted-by":"publisher","first-page":"1481","DOI":"10.1001\/jama.2025.1331","article-title":"Launching the trustworthy and responsible AI network (TRAIN): a consortium to facilitate safe and effective AI adoption.","volume":"333","author":"Emb\u00ed","year":"2025","journal-title":"JAMA"},{"issue":"2","key":"jsc250012r139","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1377\/hlthaff.2024.01003","article-title":"Artificial intelligence in health and health care: priorities for action.","volume":"44","author":"Matheny","year":"2025","journal-title":"Health Aff (Millwood)"},{"issue":"11","key":"jsc250012r141","doi-asserted-by":"publisher","first-page":"2247","DOI":"10.1038\/s41591-022-01993-y","article-title":"The AI life cycle: a holistic approach to creating ethical AI for health decisions.","volume":"28","author":"Ng","year":"2022","journal-title":"Nat Med"},{"issue":"9","key":"jsc250012r142","doi-asserted-by":"publisher","first-page":"1181","DOI":"10.1038\/s41416-021-01454-2","article-title":"Fair shares: building and benefiting from healthcare AI with mutually beneficial structures and development partnerships.","volume":"125","author":"Sidebottom","year":"2021","journal-title":"Br J Cancer"},{"issue":"6","key":"jsc250012r144","doi-asserted-by":"publisher","first-page":"1733","DOI":"10.1038\/s41591-025-03680-0","article-title":"Promoting transparency in AI for biomedical and behavioral research.","volume":"31","author":"Hernandez-Boussard","year":"2025","journal-title":"Nat Med"},{"issue":"8","key":"jsc250012r145","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2022.27779","article-title":"Assessment of adherence to reporting guidelines by commonly used clinical prediction models from a single vendor: a systematic review.","volume":"5","author":"Lu","year":"2022","journal-title":"JAMA Netw Open"},{"issue":"9","key":"jsc250012r146","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2023.35377","article-title":"APPRAISE-AI tool for quantitative evaluation of AI studies for clinical decision support.","volume":"6","author":"Kwong","year":"2023","journal-title":"JAMA Netw Open"},{"issue":"10","key":"jsc250012r147","doi-asserted-by":"publisher","first-page":"e537","DOI":"10.1016\/S2589-7500(20)30218-1","article-title":"Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension.","volume":"2","author":"Liu","year":"2020","journal-title":"Lancet Digit Health"},{"issue":"5","key":"jsc250012r148","doi-asserted-by":"publisher","first-page":"924","DOI":"10.1038\/s41591-022-01772-9","article-title":"Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI.","volume":"377","author":"Vasey","year":"2022","journal-title":"BMJ"},{"issue":"10","key":"jsc250012r149","doi-asserted-by":"publisher","first-page":"e549","DOI":"10.1016\/S2589-7500(20)30219-3","article-title":"Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension.","volume":"2","author":"Cruz Rivera","year":"2020","journal-title":"Lancet Digit Health"},{"key":"jsc250012r150","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods.","volume":"385","author":"Collins","year":"2024","journal-title":"BMJ"},{"issue":"3","key":"jsc250012r152","article-title":"Nutritional labels for data and models.","volume":"42","author":"Stoyanovich","year":"2019","journal-title":"Q Bull Comput Soc IEEE Tech Comm Data Eng"},{"issue":"4","key":"jsc250012r154","doi-asserted-by":"publisher","first-page":"e214622","DOI":"10.1001\/jamanetworkopen.2021.4622","article-title":"Algorithmovigilance: advancing methods to analyze and monitor artificial intelligence-driven health care for effectiveness and equity.","volume":"4","author":"Embi","year":"2021","journal-title":"JAMA Netw Open"},{"issue":"6","key":"jsc250012r156","doi-asserted-by":"publisher","first-page":"e241369","DOI":"10.1001\/jamahealthforum.2024.1369","article-title":"Artificial intelligence can be regulated using current patient safety procedures and infrastructure in hospitals.","volume":"5","author":"Fleisher","year":"2024","journal-title":"JAMA Health Forum"},{"issue":"1","key":"jsc250012r157","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1038\/s41746-021-00542-0","article-title":"Broadening the reach of the FDA Sentinel system: a roadmap for integrating electronic health record data in a causal analysis framework.","volume":"4","author":"Desai","year":"2021","journal-title":"NPJ Digit Med"},{"issue":"3","key":"jsc250012r158","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1038\/s41591-023-02783-w","article-title":"Integration of AI in healthcare requires an interoperable digital data ecosystem.","volume":"30","author":"Mandl","year":"2024","journal-title":"Nat Med"},{"issue":"9","key":"jsc250012r159","doi-asserted-by":"publisher","first-page":"866","DOI":"10.1001\/jama.2023.14217","article-title":"Creation and adoption of large language models in medicine.","volume":"330","author":"Shah","year":"2023","journal-title":"JAMA"},{"issue":"22","key":"jsc250012r161","doi-asserted-by":"publisher","first-page":"2091","DOI":"10.1056\/NEJMp1809643","article-title":"The FDA Sentinel Initiative\u2014an evolving national resource.","volume":"379","author":"Platt","year":"2018","journal-title":"N Engl J Med"},{"issue":"5","key":"jsc250012r162","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1093\/jamia\/ocaa028","article-title":"Using and improving distributed data networks to generate actionable evidence: the case of real-world outcomes in the Food and Drug Administration\u2019s Sentinel system.","volume":"27","author":"Brown","year":"2020","journal-title":"J Am Med Inform Assoc"},{"issue":"3","key":"jsc250012r163","doi-asserted-by":"publisher","DOI":"10.1002\/lrh2.10351","article-title":"Developing a highly-reliable learning health system.","volume":"7","author":"El-Kareh","year":"2022","journal-title":"Learn Health Syst"},{"key":"jsc250012r164","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2023-076175","article-title":"Data to knowledge to improvement: creating the learning health system.","volume":"384","author":"McDonald","year":"2024","journal-title":"BMJ"},{"issue":"16","key":"jsc250012r165","doi-asserted-by":"publisher","first-page":"1378","DOI":"10.1001\/jama.2024.0268","article-title":"Modernizing the data infrastructure for clinical research to meet evolving demands for evidence.","volume":"332","author":"Franklin","year":"2024","journal-title":"JAMA"},{"issue":"4","key":"jsc250012r166","doi-asserted-by":"publisher","first-page":"762","DOI":"10.1111\/jrh.12836","article-title":"Bridging the rural-urban divide: an implementation plan for leveraging technology and artificial intelligence to improve health and economic outcomes in rural America.","volume":"40","author":"Weeks","year":"2024","journal-title":"J Rural Health"},{"issue":"12","key":"jsc250012r167","doi-asserted-by":"publisher","first-page":"1074","DOI":"10.1001\/jama.2025.0068","article-title":"A unified approach to health data exchange: a report from the US DHHS.","volume":"333","author":"Abbasi","year":"2025","journal-title":"JAMA"},{"issue":"6551","key":"jsc250012r168","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1126\/science.abj8547","article-title":"ARPA-H: accelerating biomedical breakthroughs.","volume":"373","author":"Collins","year":"2021","journal-title":"Science"},{"issue":"3","key":"jsc250012r171","doi-asserted-by":"publisher","DOI":"10.1056\/AIp2400922","article-title":"Unseen commercial forces could undermine artificial intelligence decision support.","volume":"2","author":"Mandl","year":"2025","journal-title":"NEJM"},{"issue":"10","key":"jsc250012r172","doi-asserted-by":"publisher","first-page":"907","DOI":"10.1056\/NEJMp1709851","article-title":"The HITECH era in retrospect.","volume":"377","author":"Halamka","year":"2017","journal-title":"N Engl J Med"},{"issue":"9","key":"jsc250012r173","doi-asserted-by":"publisher","first-page":"753","DOI":"10.1001\/jama.2024.27365","article-title":"Advocating for a master of digital health degree.","volume":"333","author":"Car","year":"2025","journal-title":"JAMA"},{"issue":"1","key":"jsc250012r174","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1186\/s12913-022-08215-8","article-title":"Challenges to implementing artificial intelligence in healthcare: a qualitative interview study with healthcare leaders in Sweden.","volume":"22","author":"Petersson","year":"2022","journal-title":"BMC Health Serv Res"},{"issue":"12","key":"jsc250012r175","doi-asserted-by":"publisher","DOI":"10.1016\/j.xcrm.2022.100824","article-title":"Teaching artificial intelligence as a fundamental toolset of medicine.","volume":"3","author":"\u00d6tles","year":"2022","journal-title":"Cell Rep Med"},{"issue":"3","key":"jsc250012r176","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1097\/ACM.0000000000004963","article-title":"Competencies for the use of artificial intelligence\u2013based tools by health care professionals.","volume":"98","author":"Russell","year":"2023","journal-title":"Acad Med"},{"key":"jsc250012r177","first-page":"1097","article-title":"Integrating generative artificial intelligence into medical education: curriculum, policy, and governance strategies.","volume":"10","author":"Triola","year":"2024","journal-title":"Acad Med"},{"issue":"1","key":"jsc250012r178","doi-asserted-by":"publisher","first-page":"e2453131","DOI":"10.1001\/jamanetworkopen.2024.53131","article-title":"The digital health competencies in medical education framework: an international consensus statement based on a Delphi study.","volume":"8","author":"Car","year":"2025","journal-title":"JAMA Netw Open"},{"issue":"2","key":"jsc250012r179","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1016\/j.anclin.2022.01.001","article-title":"The wicked problem of physician well-being.","volume":"40","author":"Sinskey","year":"2022","journal-title":"Anesthesiol Clin"},{"issue":"8","key":"jsc250012r180","doi-asserted-by":"publisher","first-page":"1900","DOI":"10.1038\/s41591-023-02355-y","article-title":"HIPAA is a misunderstood and inadequate tool for protecting medical data.","volume":"29","author":"Shachar","year":"2023","journal-title":"Nat Med"},{"issue":"2","key":"jsc250012r181","doi-asserted-by":"publisher","DOI":"10.1093\/jlb\/lsab023","article-title":"Patient data ownership: who owns your health?","volume":"8","author":"Liddell","year":"2021","journal-title":"J Law Biosci"},{"issue":"15","key":"jsc250012r182","doi-asserted-by":"publisher","first-page":"1433","DOI":"10.1001\/jama.2017.12145","article-title":"The pathway to patient data ownership and better health.","volume":"318","author":"Mikk","year":"2017","journal-title":"JAMA"},{"issue":"2","key":"jsc250012r184","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1097\/HMR.0000000000000103","article-title":"Geography of community health information organization activity in the United States: implications for the effectiveness of health information exchange.","volume":"42","author":"Vest","year":"2017","journal-title":"Health Care Manage Rev"},{"issue":"23","key":"jsc250012r185","doi-asserted-by":"publisher","first-page":"2171","DOI":"10.1056\/NEJMp2102616","article-title":"HIPAA and the leak of \u201cdeidentified\u201d EHR data.","volume":"384","author":"Mandl","year":"2021","journal-title":"N Engl J Med"},{"issue":"2","key":"jsc250012r186","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1001\/jama.2019.9523","article-title":"Risk, benefit, and fairness in a big data world.","volume":"322","author":"Cassel","year":"2019","journal-title":"JAMA"},{"issue":"1","key":"jsc250012r188","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1001\/jama.2023.25051","article-title":"President Biden\u2019s executive order on artificial intelligence: implications for health care organizations.","volume":"331","author":"Mello","year":"2024","journal-title":"JAMA"},{"issue":"4","key":"jsc250012r189","doi-asserted-by":"publisher","first-page":"1061","DOI":"10.1007\/s11606-020-06394-w","article-title":"From code to bedside: implementing artificial intelligence using quality improvement methods.","volume":"36","author":"Smith","year":"2021","journal-title":"J Gen Intern Med"},{"issue":"1","key":"jsc250012r190","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1038\/s41746-022-00611-y","article-title":"Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare.","volume":"5","author":"Feng","year":"2022","journal-title":"NPJ Digit Med"},{"issue":"2","key":"jsc250012r191","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1177\/1556264615571558","article-title":"The views of quality improvement professionals and comparative effectiveness researchers on ethics, IRBs, and oversight.","volume":"10","author":"Whicher","year":"2015","journal-title":"J Empir Res Hum Res Ethics"},{"issue":"Spec No","key":"jsc250012r192","doi-asserted-by":"publisher","first-page":"S16","DOI":"10.1002\/hast.134","article-title":"An ethics framework for a learning health care system: a departure from traditional research ethics and clinical ethics.","author":"Faden","year":"2013","journal-title":"Hastings Cent Rep"},{"issue":"1","key":"jsc250012r193","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1001\/jama.2024.0269","article-title":"Making the ethical oversight of all clinical trials fit for purpose.","volume":"333","author":"Kass","year":"2025","journal-title":"JAMA"},{"issue":"4","key":"jsc250012r194","doi-asserted-by":"publisher","DOI":"10.1002\/lrh2.10066","article-title":"Ethics and learning health care: the essential roles of engagement, transparency, and accountability.","volume":"2","author":"Kass","year":"2018","journal-title":"Learn Health Syst"},{"issue":"10","key":"jsc250012r195","doi-asserted-by":"publisher","first-page":"e845","DOI":"10.1002\/mp.12707","article-title":"Big data, ethics, and regulations: Implications for consent in the learning health system.","volume":"45","author":"Spector-Bagdady","year":"2018","journal-title":"Med Phys"},{"issue":"1","key":"jsc250012r196","doi-asserted-by":"publisher","DOI":"10.1002\/lrh2.10048","article-title":"The regulation of clinical research: what\u2019s love got to do with it?","volume":"2","author":"Lantos","year":"2017","journal-title":"Learn Health Syst"},{"issue":"1","key":"jsc250012r197","doi-asserted-by":"publisher","DOI":"10.1001\/jamahealthforum.2024.5031","article-title":"Pursuing equity with artificial intelligence in health care.","volume":"6","author":"Johnson","year":"2025","journal-title":"JAMA Health Forum"},{"issue":"18","key":"jsc250012r198","doi-asserted-by":"publisher","first-page":"1765","DOI":"10.1001\/jama.2019.15064","article-title":"Potential liability for physicians using artificial intelligence.","volume":"322","author":"Price","year":"2019","journal-title":"JAMA"},{"key":"jsc250012r199","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMhle2308901","article-title":"Understanding liability risk from using health care artificial intelligence tools.","author":"Mello","year":"2024","journal-title":"N Engl J Med"},{"issue":"3","key":"jsc250012r200","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1056\/NEJMhle2308901","article-title":"Understanding liability risk from using health care artificial intelligence tools.","volume":"390","author":"Mello","year":"2024","journal-title":"N Engl J Med"},{"key":"jsc250012r2","author":"Canada\u2019s Drug Agency","year":"2025"},{"key":"jsc250012r40","doi-asserted-by":"crossref","DOI":"10.51731\/cjht.2022.269","volume-title":"An Overview of Smartphone Apps: CADTH Horizon Scan","author":"Wells","year":"2022"},{"key":"jsc250012r50","first-page":"10","volume-title":"Artificial Intelligence in Healthcare: An Essential Guide for Health Leaders","author":"Chen","year":"2020"},{"key":"jsc250012r60","volume-title":"AI and Health Insurance Prior Authorization: Regulators Need to Step Up Oversight","author":"Shachar","year":"2024"},{"key":"jsc250012r76","author":"Olsen","year":"2008"},{"key":"jsc250012r77","volume-title":"Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary","author":"McGinnis","year":"2011"},{"key":"jsc250012r109","volume-title":"Artificial intelligence (AI) and its Opportunity in Healthcare Organizations Revenue Cycle Management","author":"Pennington","year":"2023"},{"key":"jsc250012r123","volume-title":"CMS Finalizes New Electronic Prior Authorization Requirements For Payers And Providers. Mondaq Business Briefing","author":"Moundas","year":"2024"},{"key":"jsc250012r130","volume-title":"Ethics and Governance of Artificial Intelligence for Health","author":"World Health Organization","year":"2021"},{"key":"jsc250012r133","volume-title":"ACCESS AI: A New Framework for Advancing Health Equity in Health Care AI","author":"Garba-Sani","year":"2024"},{"key":"jsc250012r137","doi-asserted-by":"crossref","first-page":"10","DOI":"10.17226\/27111","volume-title":"Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril","author":"Matheny","year":"2019"},{"key":"jsc250012r151","author":"Mitchell","year":"2019"},{"key":"jsc250012r20","unstructured":"US Food and Drug Administration. Artificial intelligence in software as a medical device. March 25, 2025. Accessed September 30, 2025. https:\/\/www.fda.gov\/medical-devices\/software-medical-device-samd\/artificial-intelligence-software-medical-device"},{"key":"jsc250012r24","unstructured":"The American Medical Association. AMA augmented intelligence research: physician sentiments around the use of AI in health care: motivations, opportunities, risks, and use cases: shifts from 2023 to 2024. Published February 2025. Accessed September 23, 2025. https:\/\/www.ama-assn.org\/system\/files\/physician-ai-sentiment-report.pdf"},{"key":"jsc250012r25","unstructured":"Tahir? D. Health care AI, intended to save money, turns out to require a lot of expensive humans. CBS News. December 23, 2024. Accessed June 27, 2025. https:\/\/www.cbsnews.com\/news\/health-care-ai-cost-humans\/"},{"key":"jsc250012r35","unstructured":"Grand View Research. mHealth market size, share & trends analysis report (2024-2030). Accessed June 27, 2025. https:\/\/www.grandviewresearch.com\/industry-analysis\/mhealth-market"},{"key":"jsc250012r49","unstructured":"Adner? R, Weinstein? J. GenAI could transform how health care works. Harvard Business Review. November 27, 2023. Accessed September 23, 2025. https:\/\/hbr.org\/2023\/11\/genai-could-transform-how-health-care-works"},{"key":"jsc250012r71","unstructured":"Lubell? J. How AI is leading to more prior authorization denials. AMA News Wire. March 10, 2025. Accessed September 26, 2025. https:\/\/www.ama-assn.org\/practice-management\/prior-authorization\/how-ai-leading-more-prior-authorization-denials"},{"key":"jsc250012r72","unstructured":"American Medical Association. 2024 AMA prior authorization physician survey. Accessed June 27, 2025. https:\/\/www.ama-assn.org\/system\/files\/prior-authorization-survey.pdf"},{"key":"jsc250012r73","unstructured":"Pearce? K. Blue Cross Blue Shield of Massachusetts uses artificial intelligence to speed review time, automate authorizations, and eliminate administrative costs. PRNewswire. October 12, 2022. Accessed June 27, 2025. https:\/\/newsroom.bluecrossma.com\/2022-10-12-BLUE-CROSS-BLUE-SHIELD-OF-MASSACHUSETTS-USES-ARTIFICIAL-INTELLIGENCE-TO-SPEED-REVIEW-TIME,-AUTOMATE-AUTHORIZATIONS-ELIMINATE-ADMINISTRATIVE-COSTS"},{"key":"jsc250012r74","unstructured":"Palantir. Palantir for hospitals. Accessed June 27, 2025. https:\/\/www.palantir.com\/offerings\/palantir-for-hospitals\/"},{"key":"jsc250012r75","unstructured":"DataBricks Data Solutions. Case studies: explore our case studies to learn how we empower healthcare and life sciences organizations with Data-Driven AI Solutions. Accessed June 27, 2025. https:\/\/dnamic.ai\/data-case-studies\/"},{"key":"jsc250012r101","unstructured":"Gallo? A. A refresher on A\/B testing. Harvard Business Review. June 28, 2017. Accessed January 21, 2024. https:\/\/hbr.org\/2017\/06\/a-refresher-on-ab-testing"},{"key":"jsc250012r102","unstructured":"Sawant? N, Namballa? CB, Sadagopan? N, Nassif? H. Contextual multi-armed bandits for causal marketing.? arXiv. Preprint posted October 2, 2018. doi:10.48550\/arXiv.1810.01859"},{"key":"jsc250012r104","unstructured":"Solomon? J. A\/B testing in the insurance industry: what top firms are doing. Kameleoon. Accessed June 29, 2025. https:\/\/www.kameleoon.com\/blog\/ab-testing-insurance-industry#:~:text=A\/B"},{"key":"jsc250012r119","unstructured":"European Parliament. EU AI Act: first regulation on artificial intelligence. Accessed June 29, 2025. https:\/\/www-europarl-europa-eu.pitt.idm.oclc.org\/topics\/en\/article\/20230601STO93804\/eu-ai-act-first-regulation-on-artificial-intelligence"},{"key":"jsc250012r120","unstructured":"US Food and Drug Administration. Artificial intelligence and machine learning in software as a medical device. Accessed June 27, 2025. https:\/\/www.fda.gov\/medical-devices\/software-medical-device-samd\/artificial-intelligence-and-machine-learning-software-medical-device"},{"key":"jsc250012r121","unstructured":"US Dept of Health and Human Services. Health data, technology, and interoperability: certification program updates, algorithm transparency, and information sharing (HTI-1) final rule. Accessed June 27, 2025. https:\/\/www.federalregister.gov\/documents\/2024\/01\/09\/2023-28857\/health-data-technology-and-interoperability-certification-program-updates-algorithm-transparency-and"},{"key":"jsc250012r138","unstructured":"Health AI. Health AI partnership is a multi-stakeholder collaborative. Accessed June 29, 2025. https:\/\/healthaipartnership.org\/about-ai-health-partnership"},{"key":"jsc250012r140","unstructured":"National Nurses United. National Nurses United survey finds A.I. technology degrades and undermines patient safety. Accessed June 29, 2025. https:\/\/www.nationalnursesunited.org\/press\/national-nurses-united-survey-finds-ai-technology-undermines-patient-safety"},{"key":"jsc250012r143","unstructured":"US Food and Drug Administration. Total product lifecycle considerations for generative AI enabled devices. Accessed June 29, 2025. https:\/\/www.fda.gov\/media\/182871\/download"},{"key":"jsc250012r153","unstructured":"Fox? A. Epic leads new effort to democratize health AI validation. May 28, 2024. Accessed June 29, 2025. https:\/\/www.healthcareitnews.com\/news\/epic-leads-new-effort-democratize-health-ai-validation"},{"key":"jsc250012r155","unstructured":"The Joint Commission. The Joint Commission and Coalition for Health AI join forces to scale the responsible use of AI in delivering better healthcare. June 11, 2025. Accessed June 29, 2025. https:\/\/www.jointcommission.org\/resources\/news-and-multimedia\/news\/2025\/06\/the-joint-commission-and-coalition-for-health-ai-join-forces\/"},{"key":"jsc250012r160","unstructured":"Beavins? E. CHAI embarks on post-deployment monitoring for AI as FDA oversight lags. Accessed July 15, 2025. https:\/\/www.fiercehealthcare.com\/ai-and-machine-learning\/chai-embarks-post-deployment-monitoring-ai"},{"key":"jsc250012r169","unstructured":"ARPA. ARPA-H announces site selections by launching nationwide health innovation network. September, 2023. Accessed June 29, 2025. https:\/\/arpa-h.gov\/news-and-events\/arpa-h-launches-nationwide-health-innovation-network"},{"key":"jsc250012r170","unstructured":"ARPA. PRECISE-AI: performance and reliability evaluation for continuous modifications and useability of artificial intelligence. Accessed June 29, 2025. https:\/\/arpa-h.gov\/explore-funding\/programs\/precise-ai"},{"key":"jsc250012r183","unstructured":"Assistant Secretary for Technology Policy. HIPAA versus state laws. Accessed June 29, 2025. https:\/\/www.healthit.gov\/topic\/hipaa-versus-state-laws"},{"key":"jsc250012r187","unstructured":"European Parliament. What is GDPR, the EU\u2019s new data protection law? Accessed June 29, 2025. https:\/\/gdpr.eu\/what-is-gdpr\/"}],"container-title":["JAMA"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/jamanetwork.com\/journals\/jama\/articlepdf\/2840175\/jama_angus_2025_sc_250012_1761928737.84917.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T16:00:49Z","timestamp":1762876849000},"score":1,"resource":{"primary":{"URL":"https:\/\/jamanetwork.com\/journals\/jama\/fullarticle\/2840175"}},"subtitle":["The JAMA Summit Report on Artificial Intelligence"],"short-title":[],"issued":{"date-parts":[[2025,11,11]]},"references-count":200,"journal-issue":{"issue":"18","published-print":{"date-parts":[[2025,11,11]]}},"URL":"https:\/\/doi.org\/10.1001\/jama.2025.18490","relation":{},"ISSN":["0098-7484"],"issn-type":[{"value":"0098-7484","type":"print"}],"subject":[],"published":{"date-parts":[[2025,11,11]]}}}