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Yang","year":"2023","journal-title":"medRxiv"},{"issue":"8067","key":"pdig.0001453.ref067","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1038\/s41586-025-08869-4","article-title":"Towards accurate differential diagnosis with large language models","volume":"642","author":"D McDuff","year":"2025","journal-title":"Nature"},{"issue":"4","key":"pdig.0001453.ref068","doi-asserted-by":"crossref","first-page":"498","DOI":"10.7326\/ANNALS-24-03283","article-title":"Comparison of initial Artificial Intelligence (AI) and final physician recommendations in AI-assisted virtual urgent care visits","volume":"178","author":"D Zeltzer","year":"2025","journal-title":"Ann Intern Med"},{"key":"pdig.0001453.ref069","doi-asserted-by":"crossref","DOI":"10.2196\/51603","article-title":"How can the clinical aptitude of AI assistants be assayed?","volume":"25","author":"AJ Thirunavukarasu","year":"2023","journal-title":"J Med Internet 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Wang","year":"2024","journal-title":"arXiv"},{"key":"pdig.0001453.ref074","article-title":"Teach multimodal LLMs to comprehend electrocardiographic images","author":"R Liu","year":"2024","journal-title":"arXiv"},{"key":"pdig.0001453.ref075","article-title":"Specialist vision-language models for clinical ophthalmology","author":"R Holland","year":"2024","journal-title":"arXiv"},{"issue":"6","key":"pdig.0001453.ref076","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1001\/jamaophthalmol.2024.1165","article-title":"Vision-language models for feature detection of macular diseases on optical coherence tomography","volume":"142","author":"F Antaki","year":"2024","journal-title":"JAMA Ophthalmol"},{"key":"pdig.0001453.ref077","first-page":"175","article-title":"Current and future use of artificial intelligence in electrocardiography","volume":"10","author":"M Mart\u00ednez-Sell\u00e9s","year":"2023","journal-title":"J Cardiovasc Dev 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tools?","author":"KP Ober","year":"2015","journal-title":"The Pharos of Alpha Omega Alpha-Honor Medical Society"},{"issue":"5","key":"pdig.0001453.ref100","article-title":"Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study","volume":"2","author":"Y Xie","year":"2020","journal-title":"Lancet Digit Health"},{"issue":"2","key":"pdig.0001453.ref101","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.jval.2016.11.021","article-title":"Developing a value framework: the need to reflect the opportunity costs of funding decisions","volume":"20","author":"M Sculpher","year":"2017","journal-title":"Value Health"},{"issue":"1","key":"pdig.0001453.ref102","article-title":"Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations","volume":"7","author":"JE Alderman","year":"2025","journal-title":"Lancet Digit Health"},{"key":"pdig.0001453.ref103","doi-asserted-by":"crossref","DOI":"10.1136\/bmj-2024-081554","article-title":"FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare","volume":"388","author":"K Lekadir","year":"2025","journal-title":"BMJ"},{"issue":"11","key":"pdig.0001453.ref104","doi-asserted-by":"crossref","first-page":"3038","DOI":"10.1038\/s41591-024-03310-1","article-title":"An ethics assessment tool for artificial intelligence implementation in healthcare: CARE-AI","volume":"30","author":"Y Ning","year":"2024","journal-title":"Nat Med"},{"issue":"8","key":"pdig.0001453.ref105","doi-asserted-by":"crossref","first-page":"900","DOI":"10.1016\/j.amjmed.2019.02.005","article-title":"The case for being a medical conservative","volume":"132","author":"J Mandrola","year":"2019","journal-title":"Am J Med"},{"key":"pdig.0001453.ref106","doi-asserted-by":"crossref","DOI":"10.1136\/bmj-2022-070904","article-title":"Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI","volume":"377","author":"B Vasey","year":"2022","journal-title":"BMJ"},{"issue":"1","key":"pdig.0001453.ref107","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1038\/s41591-024-03425-5","article-title":"The TRIPOD-LLM reporting guideline for studies using large language models","volume":"31","author":"J Gallifant","year":"2025","journal-title":"Nat Med"},{"key":"pdig.0001453.ref108","article-title":"Reporting guidelines for chatbot health advice studies: explanation and elaboration for the Chatbot Assessment Reporting Tool (CHART)","volume":"390","author":"CHART Collaborative","year":"2025","journal-title":"BMJ"},{"issue":"12","key":"pdig.0001453.ref109","doi-asserted-by":"crossref","first-page":"2176","DOI":"10.1038\/s41591-021-01595-0","article-title":"Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations","volume":"27","author":"L Seyyed-Kalantari","year":"2021","journal-title":"Nat Med"},{"issue":"2","key":"pdig.0001453.ref110","first-page":"75","article-title":"Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects","volume":"31","author":"B Ko\u00e7ak","year":"2025","journal-title":"Diagn Interv Radiol"},{"issue":"13","key":"pdig.0001453.ref111","doi-asserted-by":"crossref","DOI":"10.1126\/sciadv.adq0305","article-title":"Demographic bias of expert-level vision-language foundation models in medical imaging","volume":"11","author":"Y Yang","year":"2025","journal-title":"Sci Adv"},{"key":"pdig.0001453.ref112","first-page":"232","article-title":"CheXclusion: fairness gaps in deep chest X-ray classifiers","volume":"26","author":"L Seyyed-Kalantari","year":"2021","journal-title":"Pac Symp Biocomput"},{"key":"pdig.0001453.ref113","article-title":"MedHalu: hallucinations in responses to healthcare queries by large language models","author":"V Agarwal","year":"2024","journal-title":"arXiv"},{"key":"pdig.0001453.ref114","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1016\/j.jacr.2023.06.025","article-title":"Shortcuts causing bias in radiology artificial intelligence: causes, evaluation, and mitigation","volume":"20","author":"I Banerjee","year":"2023","journal-title":"J Am Coll Radiol"},{"key":"pdig.0001453.ref115","doi-asserted-by":"crossref","DOI":"10.2214\/AJR.24.31493","article-title":"Pitfalls in interpretive applications of artificial intelligence in radiology","volume":"223","author":"S Behzad","year":"2024","journal-title":"Am J Roentgenol"},{"issue":"5","key":"pdig.0001453.ref116","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","article-title":"Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead","volume":"1","author":"C Rudin","year":"2019","journal-title":"Nat Mach Intell"},{"issue":"1","key":"pdig.0001453.ref117","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1038\/s41746-025-01476-7","article-title":"Implementing large language models in healthcare while balancing control, collaboration, costs and security","volume":"8","author":"F Dennst\u00e4dt","year":"2025","journal-title":"NPJ Digit Med"},{"issue":"1","key":"pdig.0001453.ref118","doi-asserted-by":"crossref","first-page":"100007","DOI":"10.1016\/j.fhj.2024.100007","article-title":"Clinicians risk becoming \u201cliability sinks\u201d for artificial intelligence","volume":"11","author":"T Lawton","year":"2024","journal-title":"Future Healthc J"},{"issue":"1","key":"pdig.0001453.ref119","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1038\/s41591-018-0272-7","article-title":"Privacy in the age of medical big data","volume":"25","author":"WN Price","year":"2019","journal-title":"Nat Med"},{"key":"pdig.0001453.ref120","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1186\/s12910-020-00519-w","article-title":"In Defence of informed consent for health record research - why arguments from \u2018easy rescue\u2019, \u2018no harm\u2019 and \u2018consent bias\u2019 fail","volume":"21","author":"T Ploug","year":"2020","journal-title":"BMC Med Ethics"},{"key":"pdig.0001453.ref121","doi-asserted-by":"crossref","DOI":"10.2196\/52180","article-title":"Ethical, legal, and practical concerns surrounding the implemention of new forms of consent for health data research: qualitative interview study","volume":"26","author":"S Wiertz","year":"2024","journal-title":"J Med Internet Res"},{"issue":"1","key":"pdig.0001453.ref122","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1186\/s12910-022-00871-z","article-title":"Is there a civic duty to support medical AI development by sharing electronic health records?","volume":"23","author":"S M\u00fcller","year":"2022","journal-title":"BMC Med Ethics"},{"key":"pdig.0001453.ref123","doi-asserted-by":"crossref","DOI":"10.2196\/42131","article-title":"Opt-in and opt-out consent procedures for the reuse of routinely recorded health data in scientific research and their consequences for consent rate and consent bias: systematic review","volume":"25","author":"Y de Man","year":"2023","journal-title":"J Med Internet Res"},{"issue":"1","key":"pdig.0001453.ref124","doi-asserted-by":"crossref","DOI":"10.1136\/bmjhci-2021-100450","article-title":"Exploring stakeholder attitudes towards AI in clinical practice","volume":"28","author":"IA Scott","year":"2021","journal-title":"BMJ Health Care Inform"},{"key":"pdig.0001453.ref125","article-title":"Stakeholder perspectives of clinical artificial intelligence implementation: systematic review of qualitative evidence","volume":"25","author":"HDJ Hogg","year":"2023","journal-title":"J Med Internet Res"},{"issue":"1","key":"pdig.0001453.ref126","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1111\/medu.12371","article-title":"Avoiding the trap of overtreatment","volume":"48","author":"WA Rogers","year":"2014","journal-title":"Med Educ"},{"issue":"5","key":"pdig.0001453.ref127","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1177\/01410768231173123","article-title":"Large language models will not replace healthcare professionals: curbing popular fears and hype","volume":"116","author":"AJ Thirunavukarasu","year":"2023","journal-title":"J R Soc Med"},{"issue":"7","key":"pdig.0001453.ref128","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1038\/s44220-024-00257-7","article-title":"The potential and perils of generative artificial intelligence in psychiatry and psychology","volume":"2","author":"AJ Thirunavukarasu","year":"2024","journal-title":"Nat Mental Health"},{"issue":"2","key":"pdig.0001453.ref129","doi-asserted-by":"crossref","DOI":"10.3399\/bjgpopen17X100833","article-title":"The effect of Dr Google on doctor-patient encounters in primary care: a quantitative, observational, cross-sectional study","volume":"1","author":"N Van Riel","year":"2017","journal-title":"BJGP Open"},{"issue":"2","key":"pdig.0001453.ref130","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1515\/dx-2016-0045","article-title":"\u201cDr. Google\u201d and his predecessors","volume":"4","author":"A Jutel","year":"2017","journal-title":"Diagnosis (Berl)"},{"key":"pdig.0001453.ref131","author":"International Medical Device Regulators Forum","year":"2014"},{"issue":"2","key":"pdig.0001453.ref132","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1038\/s41591-025-04068-w","article-title":"Building a code of conduct for AI-driven clinical consultations","volume":"32","author":"E Lim","year":"2026","journal-title":"Nat Med"},{"issue":"1","key":"pdig.0001453.ref133","doi-asserted-by":"crossref","first-page":"10104","DOI":"10.1038\/s41467-024-51465-9","article-title":"In-context learning enables multimodal large language models to classify cancer pathology images","volume":"15","author":"D Ferber","year":"2024","journal-title":"Nat Commun"},{"key":"pdig.0001453.ref134","author":"International Organization for Standardization","year":"2021"},{"key":"pdig.0001453.ref135","doi-asserted-by":"crossref","unstructured":"Fuller JG. Run-time assurance: a rising technology. 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