CoChat
Sign Up
Cited by 41Artificial Intelligence in Healthcare and EducationArtificial Intelligence ApplicationsEthics and Social Impacts of AI

Advancing the democratization of generative artificial intelligence in healthcare: a narrative review

Open access
Chen, Anjun, Liu, Lei, Zhu, Tongyu·2024·Journal of Hospital Management and Health Policy
DOI 10.21037/jhmhp-24-54
Open this paper in CoChat — free account

Abstract

Background and Objective: The emergence of ChatGPT-like generative artificial intelligence (GenAI) has dramatically transformed the healthcare landscape, bringing new hope for the democratization of artificial intelligence (AI) in healthcare—a topic that has not been comprehensively reviewed. This review aims to analyze the reasons propelling the democratization of healthcare GenAI, outline the initial evidence in the literature, and propose future directions to advance GenAI democratization. Methods: We conducted a deep literature search for GenAI studies using Google Scholar, PubMed, ChatGPT, Journal of the American Medical Association (JAMA), Nature, Springer Link, and Journal of Medical Internet Research (JMIR). We performed an abstraction analysis on the nature of GenAI versus traditional AI and the applications of GenAI in medical education and clinical care. Key Content and Findings: (I) A detailed comparison of traditional and GenAI in healthcare reveals that large language model (LLM)-based GenAI’s unprecedent general-purpose capabilities and natural language interaction ability, coupled with its free public availability, make GenAI ideal for democratization in healthcare. (II) We have identified plenty of initial evidence for GenAI democratization in medical education and clinical care, marking the start of the emerging trend of GenAI democratization in a host of impactful applications. Applications in medical education include medical exam preparation, medical teaching and training, and simulation. Applications in clinical care include diagnosis assistance, disease risk prediction, new generalist chatbots, treatment decision support, surgery support, medical image analysis, patient communication, physician communication, documentation automation, clinical trial automation, informatics tasks automation, and specialized or custom LLMs. (III) Responsible AI is essential for the future of healthcare GenAI. National initiatives and regulatory efforts are working to ensure safety, efficacy, accountability, equity, security and privacy are built into healthcare GenAI. Responsible GenAI requires a human-machine collaboration approach, where AI augments human expertise rather than replaces it. Conclusions: The democratization of GenAI in healthcare has just begun, driven by the nature of GenAI and guided by the principle of human-machine collaboration. To further advance GenAI democratization, we propose three key future directions: integrating GenAI in medical education curricula, democratizing GenAI clinical evaluation research, and building learning health systems (LHS) with GenAI for system-level enforcement of democratization. Democratizing GenAI in healthcare will revolutionize medicine and significantly impact care delivery and health policies.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.21037/jhmhp-24-54

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Unlike traditional AI applications for clinical decision support, GenAI tools generate original content in real time, enabling adaptive simulations, dialogue-based learning, and automated feedback.

    “Unlike traditional AI applications for clinical decision support (Khosravi et al., 2024), GenAI tools generate original content in real time, enabling adaptive simulations, dialogue-based learning, and automated feedback (Chen et al., 2024).”

    From Patterns, advances, and gaps in using ChatGPT and similar technologies in nursing education: A PAGER scoping review (2025) · cites this paper as Chen et al., 2024

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

The publisher hasn’t deposited this paper’s reference list.

Research works printed but not linked: 107

  1. Matheny ME, Whicher D, Thadaney Israni S. Artificial Intelligence in Health Care: A Report From the National Academy of Medicine. JAMA 2020;323:509-10. [Crossref] [PubMed]
  2. Dzau VJ, Laitner MH, Temple A, et al. Achieving the promise of artificial intelligence in health and medicine: Building a foundation for the future. PNAS Nexus 2023;2:pgad410. [Crossref]
  3. Khera R, Butte AJ, Berkwits M, et al. AI in Medicine-JAMA's Focus on Clinical Outcomes, Patient-Centered Care, Quality, and Equity. JAMA 2023;330:818-20. [Crossref] [PubMed]
  4. Rajkomar A, Dean J, Kohane I. Machine Learning in Medicine. N Engl J Med 2019;380:1347-58. [Crossref] [PubMed]
  5. Deo RC. Machine Learning in Medicine. Circulation 2015;132:1920-30. [Crossref] [PubMed]
  6. Cohen JP, Cao T, Viviano JD, et al. Problems in the deployment of machine-learned models in health care. CMAJ 2021;193:E1391-4. [Crossref] [PubMed]
  7. Wilkinson J, Arnold KF, Murray EJ, et al. Time to reality check the promises of machine learning-powered precision medicine. Lancet Digit Health 2020;2:e677-80. [Crossref] [PubMed]
  8. Chen PC, Liu Y, Peng L. How to develop machine learning models for healthcare. Nat Mater 2019;18:410-4. [Crossref] [PubMed]
  9. Seastedt KP, Schwab P, O'Brien Z, et al. Global healthcare fairness: We should be sharing more, not less, data. PLOS Digit Health 2022;1:e0000102. [Crossref] [PubMed]
  10. Egger J, Gsaxner C, Pepe A, et al. Medical deep learning-A systematic meta-review. Comput Methods Programs Biomed 2022;221:106874. [Crossref] [PubMed]
  11. Kelly CJ, Karthikesalingam A, Suleyman M, et al. Key challenges for delivering clinical impact with artificial intelligence. BMC Med 2019;17:195. [Crossref] [PubMed]
  12. Jindal JA, Lungren MP, Shah NH. Ensuring useful adoption of generative artificial intelligence in healthcare. J Am Med Inform Assoc 2024;31:1441-4. [Crossref] [PubMed]
  13. Committee on Diagnostic Error in Health Care; Board on Health Care Services; Institute of Medicine; et al. Improving Diagnosis in Health Care. Balogh EP, Miller BT, Ball JR, editors. Washington (DC): National Academies Press (US); 2015 Dec 29.
  14. Wachter RM, Brynjolfsson E. Will Generative Artificial Intelligence Deliver on Its Promise in Health Care? JAMA 2024;331:65-9. [Crossref] [PubMed]
  15. Howell MD, Corrado GS, DeSalvo KB. Three Epochs of Artificial Intelligence in Health Care. JAMA 2024;331:242-4. [Crossref] [PubMed]
  16. Ferruz N, Zitnik M, Oudeyer PY, et al. Anniversary AI reflections. Nature Machine Intelligence 2024;6:6-12. [Crossref]
  17. National Academies of Sciences, Engineering, and Medicine. Artificial Intelligence in Health Professions Education: Proceedings of a Workshop. Washington, DC: The National Academies Press. 2023. Available online: https://doi.org/
  18. Chang BS. Transformation of Undergraduate Medical Education in 2023. JAMA 2023;330:1521-2. [Crossref] [PubMed]
  19. Gilson A, Safranek CW, Huang T, et al. How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge Assessment. JMIR Med Educ 2023;9:e45312. Erratum in: JMIR Med Educ 2024;10:e57594. [Crossref] [PubMed]
  20. Kung TH, Cheatham M, Medenilla A, et al. Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLOS Digit Health 2023;2:e0000198. [Crossref] [PubMed]
  21. Strong E, DiGiammarino A, Weng Y, et al. Chatbot vs Medical Student Performance on Free-Response Clinical Reasoning Examinations. JAMA Intern Med 2023;183:1028-30. [Crossref] [PubMed]
  22. Mihalache A, Popovic MM, Muni RH. Performance of an Artificial Intelligence Chatbot in Ophthalmic Knowledge Assessment. JAMA Ophthalmol 2023;141:589-97. [Crossref] [PubMed]
  23. Schubert MC, Wick W, Venkataramani V. Performance of Large Language Models on a Neurology Board-Style Examination. JAMA Netw Open 2023;6:e2346721. [Crossref] [PubMed]
  24. Abdullahi T, Singh R, Eickhoff C. Learning to Make Rare and Complex Diagnoses With Generative AI Assistance: Qualitative Study of Popular Large Language Models. JMIR Med Educ 2024;10:e51391. [Crossref] [PubMed]
  25. Chen A, Chen DO, Tian L. Benchmarking the symptom-checking capabilities of ChatGPT for a broad range of diseases. J Am Med Inform Assoc 2023;ocad245. [Crossref] [PubMed]
  26. Chen A, Chen W, Liu Y. Impact of Democratizing Artificial Intelligence: Using ChatGPT in Medical Education and Training. Acad Med 2024;99:589. [Crossref] [PubMed]
  27. Preiksaitis C, Rose C. Opportunities, Challenges, and Future Directions of Generative Artificial Intelligence in Medical Education: Scoping Review. JMIR Med Educ 2023;9:e48785. [Crossref] [PubMed]
  28. Boscardin CK, Gin B, Golde PB, et al. ChatGPT and Generative Artificial Intelligence for Medical Education: Potential Impact and Opportunity. Acad Med 2024;99:22-7. [Crossref] [PubMed]
  29. Karabacak M, Ozkara BB, Margetis K, et al. The Advent of Generative Language Models in Medical Education. JMIR Med Educ 2023;9:e48163. [Crossref] [PubMed]
  30. Smith M, Saunders R, Stuckhardt L, et al. Best Care at Lower Cost: The Path to Continuously Learning Health Care in America. Washington (DC): National Academies Press (US); May 10, 2013.
  31. Chen A, Chen DO. Accuracy of Chatbots in Citing Journal Articles. JAMA Netw Open 2023;6:e2327647. [Crossref] [PubMed]
  32. Holderried F, Stegemann-Philipps C, Herschbach L, et al. A Generative Pretrained Transformer (GPT)-Powered Chatbot as a Simulated Patient to Practice History Taking: Prospective, Mixed Methods Study. JMIR Med Educ 2024;10:e53961. [Crossref] [PubMed]
  33. Liu X, Wu C, Lai R, et al. ChatGPT: when the artificial intelligence meets standardized patients in clinical training. J Transl Med 2023;21:447. [Crossref] [PubMed]
  34. Grigorian A, Shipley J, Nahmias J, et al. Implications of Using Chatbots for Future Surgical Education. JAMA Surg 2023;158:1220-2. [Crossref] [PubMed]
  35. Haug CJ, Drazen JM. Artificial Intelligence and Machine Learning in Clinical Medicine, 2023. N Engl J Med 2023;388:1201-8. [Crossref] [PubMed]
  36. Liu J, Wang C, Liu S. Utility of ChatGPT in Clinical Practice. J Med Internet Res 2023;25:e48568. [Crossref] [PubMed]
  37. Lee P, Bubeck S, Petro J. Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine. N Engl J Med 2023;388:1233-9. [Crossref] [PubMed]
  38. Shah NH, Halamka JD, Saria S, et al. A Nationwide Network of Health AI Assurance Laboratories. JAMA 2024;331:245-9. [Crossref] [PubMed]
  39. Eriksen A, Möller S, Ryg J. Use of GPT-4 to Diagnose Complex Clinical Cases. NEJM AI 2023;1:AIp2300031. [Crossref]
  40. Kanjee Z, Crowe B, Rodman A. Accuracy of a Generative Artificial Intelligence Model in a Complex Diagnostic Challenge. JAMA 2023;330:78-80. [Crossref] [PubMed]
  41. Barile J, Margolis A, Cason G, et al. Diagnostic Accuracy of a Large Language Model in Pediatric Case Studies. JAMA Pediatr 2024;178:313-5. [Crossref] [PubMed]
  42. Sandmann S, Riepenhausen S, Plagwitz L, et al. Systematic analysis of ChatGPT, Google search and Llama 2 for clinical decision support tasks. Nat Commun 2024;15:2050. [Crossref] [PubMed]
  43. Savage T, Nayak A, Gallo R, et al. Diagnostic reasoning prompts reveal the potential for large language model interpretability in medicine. NPJ Digit Med 2024;7:20. [Crossref] [PubMed]
  44. Rao A, Pang M, Kim J, et al. Assessing the Utility of ChatGPT Throughout the Entire Clinical Workflow: Development and Usability Study. J Med Internet Res 2023;25:e48659. [Crossref] [PubMed]
  45. Shea YF, Lee CMY, Ip WCT, et al. Use of GPT-4 to Analyze Medical Records of Patients With Extensive Investigations and Delayed Diagnosis. JAMA Netw Open 2023;6:e2325000. [Crossref] [PubMed]
  46. Kulkarni PA, Singh H. Artificial Intelligence in Clinical Diagnosis: Opportunities, Challenges, and Hype. JAMA 2023;330:317-8. [Crossref] [PubMed]
  47. Sarraju A, Bruemmer D, Van Iterson E, et al. Appropriateness of Cardiovascular Disease Prevention Recommendations Obtained From a Popular Online Chat-Based Artificial Intelligence Model. JAMA 2023;329:842-4. [Crossref] [PubMed]
  48. Han C, Kim DW, Kim S, et al. Evaluation of GPT-4 for 10-year cardiovascular risk prediction: Insights from the UK Biobank and KoGES data. iScience 2024;27:109022. [Crossref] [PubMed]
  49. Liu S, Wright AP, Patterson BL, et al. Using AI-generated suggestions from ChatGPT to optimize clinical decision support. J Am Med Inform Assoc 2023;30:1237-45. [Crossref] [PubMed]
  50. Moor M, Banerjee O, Abad ZSH, et al. Foundation models for generalist medical artificial intelligence. Nature 2023;616:259-65. [Crossref] [PubMed]
  51. Huang RS, Lu KJQ, Meaney C, et al. Assessment of Resident and AI Chatbot Performance on the University of Toronto Family Medicine Residency Progress Test: Comparative Study. JMIR Med Educ 2023;9:e50514. [Crossref] [PubMed]
  52. Iannantuono GM, Bracken-Clarke D, Floudas CS, et al. Applications of large language models in cancer care: current evidence and future perspectives. Front Oncol 2023;13:1268915. [Crossref] [PubMed]
  53. Zhu L, Mou W, Chen R. Can the ChatGPT and other large language models with internet-connected database solve the questions and concerns of patient with prostate cancer and help democratize medical knowledge? J Transl Med 2023;21:269. [Crossref] [PubMed]
  54. Sorin V, Klang E, Sklair-Levy M, et al. Large language model (ChatGPT) as a support tool for breast tumor board. NPJ Breast Cancer 2023;9:44. [Crossref] [PubMed]
  55. Benary M, Wang XD, Schmidt M, et al. Leveraging Large Language Models for Decision Support in Personalized Oncology. JAMA Netw Open 2023;6:e2343689. [Crossref] [PubMed]
  56. Chen S, Kann BH, Foote MB, et al. Use of Artificial Intelligence Chatbots for Cancer Treatment Information. JAMA Oncol 2023;9:1459-62. [Crossref] [PubMed]
  57. Rengers TA, Thiels CA, Salehinejad H. Academic Surgery in the Era of Large Language Models: A Review. JAMA Surg 2024;159:445-50. [Crossref] [PubMed]
  58. Ayoub NF, Lee YJ, Grimm D, et al. Comparison Between ChatGPT and Google Search as Sources of Postoperative Patient Instructions. JAMA Otolaryngol Head Neck Surg 2023;149:556-8. [Crossref] [PubMed]
  59. Koohi-Moghadam M, Bae KT. Generative AI in Medical Imaging: Applications, Challenges, and Ethics. J Med Syst 2023;47:94. [Crossref] [PubMed]
  60. Huang J, Neill L, Wittbrodt M, et al. Generative Artificial Intelligence for Chest Radiograph Interpretation in the Emergency Department. JAMA Netw Open 2023;6:e2336100. [Crossref] [PubMed]
  61. Kottlors J, Bratke G, Rauen P, et al. Feasibility of Differential Diagnosis Based on Imaging Patterns Using a Large Language Model. Radiology 2023;308:e231167. [Crossref] [PubMed]
  62. Mihalache A, Huang RS, Popovic MM, et al. Accuracy of an Artificial Intelligence Chatbot's Interpretation of Clinical Ophthalmic Images. JAMA Ophthalmol 2024;142:321-6. [Crossref] [PubMed]
  63. Ayers JW, Poliak A, Dredze M, et al. Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum. JAMA Intern Med 2023;183:589-96. [Crossref] [PubMed]
  64. Ayers JW, Zhu Z, Poliak A, et al. Evaluating Artificial Intelligence Responses to Public Health Questions. JAMA Netw Open 2023;6:e2317517. [Crossref] [PubMed]
  65. Bernstein IA, Zhang YV, Govil D, et al. Comparison of Ophthalmologist and Large Language Model Chatbot Responses to Online Patient Eye Care Questions. JAMA Netw Open 2023;6:e2330320. [Crossref] [PubMed]
  66. Huang AS, Hirabayashi K, Barna L, et al. Assessment of a Large Language Model's Responses to Questions and Cases About Glaucoma and Retina Management. JAMA Ophthalmol 2024;142:371-5. Erratum in: JAMA Ophthalmol 2024;142:393. [Crossref] [PubMed]
  67. Ferreira AL, Chu B, Grant-Kels JM, et al. Evaluation of ChatGPT Dermatology Responses to Common Patient Queries. JMIR Dermatol 2023;6:e49280. [Crossref] [PubMed]
  68. Harris E. Large Language Models Answer Medical Questions Accurately, but Can't Match Clinicians' Knowledge. JAMA 2023;330:792-4. [Crossref] [PubMed]
  69. Goodman RS, Patrinely JR, Stone CA Jr, et al. Accuracy and Reliability of Chatbot Responses to Physician Questions. JAMA Netw Open 2023;6:e2336483. [Crossref] [PubMed]
  70. Wang L, Chen X, Deng X, et al. Prompt engineering in consistency and reliability with the evidence-based guideline for LLMs. NPJ Digit Med 2024;7:41. [Crossref] [PubMed]
  71. Decker H, Trang K, Ramirez J, et al. Large Language Model-Based Chatbot vs Surgeon-Generated Informed Consent Documentation for Common Procedures. JAMA Netw Open 2023;6:e2336997. [Crossref] [PubMed]
  72. Nayak A, Alkaitis MS, Nayak K, et al. Comparison of History of Present Illness Summaries Generated by a Chatbot and Senior Internal Medicine Residents. JAMA Intern Med 2023;183:1026-7. [Crossref] [PubMed]
  73. Goodman KE, Yi PH, Morgan DJ. AI-Generated Clinical Summaries Require More Than Accuracy. JAMA 2024;331:637-8. [Crossref] [PubMed]
  74. Tang L, Sun Z, Idnay B, et al. Evaluating large language models on medical evidence summarization. NPJ Digit Med 2023;6:158. [Crossref] [PubMed]
  75. Tayebi Arasteh S, Han T, Lotfinia M, et al. Large language models streamline automated machine learning for clinical studies. Nat Commun 2024;15:1603. [Crossref] [PubMed]
  76. Hu Y, Chen Q, Du J, et al. Improving large language models for clinical named entity recognition via prompt engineering. J Am Med Inform Assoc 2024;ocad259. [Crossref] [PubMed]
  77. Kresevic S, Giuffrè M, Ajcevic M, et al. Optimization of hepatological clinical guidelines interpretation by large language models: a retrieval augmented generation-based framework. NPJ Digit Med 2024;7:102. [Crossref] [PubMed]
  78. Wornow M, Xu Y, Thapa R, et al. The shaky foundations of large language models and foundation models for electronic health records. NPJ Digit Med 2023;6:135. [Crossref] [PubMed]
  79. Clusmann J, Kolbinger FR, Muti HS, et al. The future landscape of large language models in medicine. Commun Med (Lond) 2023;3:141. [Crossref] [PubMed]
  80. Singhal K, Azizi S, Tu T, et al. Large language models encode clinical knowledge. Nature 2023;620:172-80. [Crossref] [PubMed]
  81. Singhal K, Tu T, Gottweis J, et al. Towards Expert-Level Medical Question Answering with Large Language Models. arXiv 2023. arXiv:2305.09617. · arXiv
  82. McDuff D, Schaekermann M, Tu T, et al. Towards accurate differential diagnosis with large language models. arXiv 2023. arXiv:2312.00164. · arXiv
  83. Tu T, Palepu A, Schaekermann M, et al. Towards conversational diagnostic AI. arXiv 2024. arXiv:2401.05654. · arXiv
  84. Mehandru N, Miao BY, Almaraz ER, et al. Evaluating large language models as agents in the clinic. NPJ Digit Med 2024;7:84. [Crossref] [PubMed]
  85. Stade EC, Stirman SW, Ungar LH, et al. Large language models could change the future of behavioral healthcare: a proposal for responsible development and evaluation. Npj Ment Health Res 2024;3:12. [Crossref] [PubMed]
  86. Yang X, Chen A, PourNejatian N, et al. A large language model for electronic health records. NPJ Digit Med 2022;5:194. [Crossref] [PubMed]
  87. Jiang LY, Liu XC, Nejatian NP, et al. Health system-scale language models are all-purpose prediction engines. Nature 2023;619:357-62. [Crossref] [PubMed]
  88. Wang H, Gao C, Dantona C, et al. DRG-LLaMA: tuning LLaMA model to predict diagnosis-related group for hospitalized patients. NPJ Digit Med 2024;7:16. [Crossref] [PubMed]
  89. Ktena I, Wiles O, Albuquerque I, et al. Generative models improve fairness of medical classifiers under distribution shifts. Nat Med 2024;30:1166-73. [Crossref] [PubMed]
  90. Kim HK, Ryu IH, Choi JY, et al. A feasibility study on the adoption of a generative denoising diffusion model for the synthesis of fundus photographs using a small dataset. Discov Appl Sci 2024;6:188. [Crossref]
  91. Goldberg CB, Adams L, Blumenthal D, et al. To do no harm - and the most good - with AI in health care. NEJM AI 2024;1:AIp2400036. [Crossref]
  92. Badal K, Lee CM, Esserman LJ. Guiding principles for the responsible development of artificial intelligence tools for healthcare. Commun Med (Lond) 2023;3:47. [Crossref] [PubMed]
  93. Shah NH, Entwistle D, Pfeffer MA. Creation and Adoption of Large Language Models in Medicine. JAMA 2023;330:866-9. [Crossref] [PubMed]
  94. Meskó B, Topol EJ. The imperative for regulatory oversight of large language models (or generative AI) in healthcare. NPJ Digit Med 2023;6:120. [Crossref] [PubMed]
  95. Oniani D, Hilsman J, Peng Y, et al. Adopting and expanding ethical principles for generative artificial intelligence from military to healthcare. NPJ Digit Med 2023;6:225. [Crossref] [PubMed]
  96. Mello MM, Guha N. ChatGPT and Physicians' Malpractice Risk. JAMA Health Forum 2023;4:e231938. [Crossref] [PubMed]
  97. Haupt CE, Marks M. AI-Generated Medical Advice-GPT and Beyond. JAMA 2023;329:1349-50. [Crossref] [PubMed]
  98. Gottlieb S, Silvis L. How to Safely Integrate Large Language Models Into Health Care. JAMA Health Forum 2023;4:e233909. [Crossref] [PubMed]
  99. Ito N, Kadomatsu S, Fujisawa M, et al. The Accuracy and Potential Racial and Ethnic Biases of GPT-4 in the Diagnosis and Triage of Health Conditions: Evaluation Study. JMIR Med Educ 2023;9:e47532. [Crossref] [PubMed]
  100. Kim J, Cai ZR, Chen ML, et al. Assessing Biases in Medical Decisions via Clinician and AI Chatbot Responses to Patient Vignettes. JAMA Netw Open 2023;6:e2338050. [Crossref] [PubMed]
  101. Omiye JA, Lester JC, Spichak S, et al. Large language models propagate race-based medicine. NPJ Digit Med 2023;6:195. [Crossref] [PubMed]
  102. Chen A, Wu E, Huang R, et al. Development of inclusive and practical machine learning risk prediction models from electronic medical records for lung cancer screening. JMIR AI 2024; [Crossref]
  103. Hswen Y, Voelker R, New AI. Tools Must Have Health Equity in Their DNA. JAMA 2023;330:1604-7. [Crossref] [PubMed]
  104. Dorr DA, Adams L, Embí P. Harnessing the Promise of Artificial Intelligence Responsibly. JAMA 2023;329:1347-8. [Crossref] [PubMed]
  105. Mello MM, Shah NH, Char DS. President Biden's Executive Order on Artificial Intelligence-Implications for Health Care Organizations. JAMA 2024;331:17-8. [Crossref] [PubMed]
  106. Abràmoff MD, Tarver ME, Loyo-Berrios N, et al. Considerations for addressing bias in artificial intelligence for health equity. NPJ Digit Med 2023;6:170. [Crossref] [PubMed]
  107. Chen A, Chen DO. Simulation of a machine learning enabled learning health system for risk prediction using synthetic patient data. Sci Rep 2022;12:17917. [Crossref] [PubMed] doi: 10.21037/jhmhp-24-54 Cite this article as: Chen A, Liu L, Zhu T. Advancing the democratization of generative artificial intelligence in healthcare: a narrative review. J Hosp Manag Health Policy 2024;8:12.

Cited by

Sorted by citations

Related

Authors
Chen, A., Liu, L., & Zhu, T.
Affiliations
Shanghai Medical College of Fudan University (CN)
Corresponding author
Chen, Anjun
ORCID
Liu, Lei
Venue
Journal of Hospital Management and Health Policy
Publisher
AME Publishing Company
Published
2024-06
Volume
8
Pages
12-12
ISSN
2523-2533 (ISSN-L)
DOI
10.21037/jhmhp-24-54
OpenAlex
W4400158748
Cited by
40
References
0
Citations by year
2026: 21 · 2025: 16 · 2024: 3
FWCI
0.92
Type
journal article
Language
en
Open access
Licence
cc-by-nc-nd
Locations
Journal of Hospital Management and Health Policy (publishedVersion, cc-by-nc-nd)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
Keywords
generative AI, democratization of AI, health care, large language models, medical education, clinical care, responsible AI, human-machine collaboration, learning health systems, clinical decision support, healthcare chatbots, AI regulation, AI equity

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review