Generative Artificial Intelligence and Ethical Considerations in Health Care: a Scoping Review and Ethics Checklist

Overview

Journal Lancet Digit Health

Specialties Biomedical Engineering
Medical Informatics
Public Health

Date 2024 Sep 18

PMID 39294061

Authors

Yilin Ning

Salinelat Teixayavong

Yuqing Shang

Julian Savulescu

Vaishaanth Nagaraj

Di Miao

Mayli Mertens

Daniel Shu Wei Ting

Jasmine Chiat Ling Ong

Mingxuan Liu

Jiuwen Cao

Michael Dunn

Roger Vaughan

Marcus Eng Hock Ong

Joseph Jao-Yiu Sung

Eric J Topol

Nan Liu

Affiliations

Soon will be listed here.

Abstract

The widespread use of Chat Generative Pre-trained Transformer (known as ChatGPT) and other emerging technology that is powered by generative artificial intelligence (GenAI) has drawn attention to the potential ethical issues they can cause, especially in high-stakes applications such as health care, but ethical discussions have not yet been translated into operationalisable solutions. Furthermore, ongoing ethical discussions often neglect other types of GenAI that have been used to synthesise data (eg, images) for research and practical purposes, which resolve some ethical issues and expose others. We did a scoping review of the ethical discussions on GenAI in health care to comprehensively analyse gaps in the research. To reduce the gaps, we have developed a checklist for comprehensive assessment and evaluation of ethical discussions in GenAI research. The checklist can be integrated into peer review and publication systems to enhance GenAI research and might be useful for ethics-related disclosures for GenAI-powered products and health-care applications of such products and beyond.

Citing Articles

The integration of AI in nursing: addressing current applications, challenges, and future directions.

Wei Q, Pan S, Liu X, Hong M, Nong C, Zhang W Front Med (Lausanne). 2025; 12:1545420.

PMID: 40007584 PMC: 11850350. DOI: 10.3389/fmed.2025.1545420.

Evaluating the Efficacy of Large Language Models in Guiding Treatment Decisions for Pediatric Refractive Error.

Kang D, Wu H, Yuan L, Shen W, Feng J, Zhan J Ophthalmol Ther. 2025; .

PMID: 39985747 DOI: 10.1007/s40123-025-01105-2.

Application of Generative Artificial Intelligence in Dyslipidemia Care.

Ahn J, Kim B J Lipid Atheroscler. 2025; 14(1):77-93.

PMID: 39911966 PMC: 11791424. DOI: 10.12997/jla.2025.14.1.77.

AI for IMPACTS Framework for Evaluating the Long-Term Real-World Impacts of AI-Powered Clinician Tools: Systematic Review and Narrative Synthesis.

Jacob C, Brasier N, Laurenzi E, Heuss S, Mougiakakou S, Coltekin A J Med Internet Res. 2025; 27:e67485.

PMID: 39909417 PMC: 11840377. DOI: 10.2196/67485.

Convergence of evolving artificial intelligence and machine learning techniques in precision oncology.

Fountzilas E, Pearce T, Baysal M, Chakraborty A, Tsimberidou A NPJ Digit Med. 2025; 8(1):75.

PMID: 39890986 PMC: 11785769. DOI: 10.1038/s41746-025-01471-y.

References

Wornow M, Xu Y, Thapa R, Patel B, Steinberg E, Fleming S . The shaky foundations of large language models and foundation models for electronic health records. NPJ Digit Med. 2023; 6(1):135. PMC: 10387101. DOI: 10.1038/s41746-023-00879-8. View

Rahimzadeh V, Kostick-Quenet K, Blumenthal Barby J, McGuire A . Ethics Education for Healthcare Professionals in the Era of ChatGPT and Other Large Language Models: Do We Still Need It?. Am J Bioeth. 2023; 23(10):17-27. DOI: 10.1080/15265161.2023.2233358. View

Morley J, Machado C, Burr C, Cowls J, Joshi I, Taddeo M . The ethics of AI in health care: A mapping review. Soc Sci Med. 2020; 260:113172. DOI: 10.1016/j.socscimed.2020.113172. View

Park C, Jeong H, Henao R, Kheterpal M . Current Landscape of Generative Adversarial Networks for Facial Deidentification in Dermatology: Systematic Review and Evaluation. JMIR Dermatol. 2024; 5(2):e35497. PMC: 10334885. DOI: 10.2196/35497. View

Catchpole K, Russ S . The problem with checklists. BMJ Qual Saf. 2015; 24(9):545-9. DOI: 10.1136/bmjqs-2015-004431. View

Marks M, Haupt C . AI Chatbots, Health Privacy, and Challenges to HIPAA Compliance. JAMA. 2023; 330(4):309-310. DOI: 10.1001/jama.2023.9458. View

Murphy K, Di Ruggiero E, Upshur R, Willison D, Malhotra N, Cai J . Artificial intelligence for good health: a scoping review of the ethics literature. BMC Med Ethics. 2021; 22(1):14. PMC: 7885243. DOI: 10.1186/s12910-021-00577-8. View

Nikolentzos G, Vazirgiannis M, Xypolopoulos C, Lingman M, Brandt E . Synthetic electronic health records generated with variational graph autoencoders. NPJ Digit Med. 2023; 6(1):83. PMC: 10148837. DOI: 10.1038/s41746-023-00822-x. View

Fournier-Tombs E, McHardy J . A Medical Ethics Framework for Conversational Artificial Intelligence. J Med Internet Res. 2023; 25:e43068. PMC: 10373921. DOI: 10.2196/43068. View

10.

Riggare S . Patient researchers - the missing link?. Nat Med. 2020; 26(10):1507. DOI: 10.1038/s41591-020-1080-4. View

11.

Oniani D, Hilsman J, Peng Y, Poropatich R, Pamplin J, Legault G . Adopting and expanding ethical principles for generative artificial intelligence from military to healthcare. NPJ Digit Med. 2023; 6(1):225. PMC: 10693640. DOI: 10.1038/s41746-023-00965-x. View

12.

Liu M, Ning Y, Teixayavong S, Mertens M, Xu J, Ting D . A translational perspective towards clinical AI fairness. NPJ Digit Med. 2023; 6(1):172. PMC: 10502051. DOI: 10.1038/s41746-023-00918-4. View

13.

Nov O, Singh N, Mann D . Putting ChatGPT's Medical Advice to the (Turing) Test: Survey Study. JMIR Med Educ. 2023; 9:e46939. PMC: 10366957. DOI: 10.2196/46939. View

14.

Kuo N, Garcia F, Sonnerborg A, Bohm M, Kaiser R, Zazzi M . Generating synthetic clinical data that capture class imbalanced distributions with generative adversarial networks: Example using antiretroviral therapy for HIV. J Biomed Inform. 2023; 144:104436. DOI: 10.1016/j.jbi.2023.104436. View

15.

Vidal C . Neurotechnologies under the Eye of Bioethics. eNeuro. 2022; 9(3). PMC: 9215686. DOI: 10.1523/ENEURO.0072-22.2022. View

16.

Dado T, Gucluturk Y, Ambrogioni L, Ras G, Bosch S, van Gerven M . Hyperrealistic neural decoding for reconstructing faces from fMRI activations via the GAN latent space. Sci Rep. 2022; 12(1):141. PMC: 8741893. DOI: 10.1038/s41598-021-03938-w. View

17.

Tang L, Sun Z, Idnay B, Nestor J, Soroush A, Elias P . Evaluating large language models on medical evidence summarization. NPJ Digit Med. 2023; 6(1):158. PMC: 10449915. DOI: 10.1038/s41746-023-00896-7. View

18.

Collins G, Reitsma J, Altman D, Moons K . Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ. 2015; 350:g7594. DOI: 10.1136/bmj.g7594. View

19.

Mesko B, Topol E . The imperative for regulatory oversight of large language models (or generative AI) in healthcare. NPJ Digit Med. 2023; 6(1):120. PMC: 10326069. DOI: 10.1038/s41746-023-00873-0. View

20.

Tang J, LeBel A, Jain S, Huth A . Semantic reconstruction of continuous language from non-invasive brain recordings. Nat Neurosci. 2023; 26(5):858-866. PMC: 11304553. DOI: 10.1038/s41593-023-01304-9. View