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. 2024 Nov;6(11):e848-e856.
doi: 10.1016/S2589-7500(24)00143-2. Epub 2024 Sep 17.

Generative artificial intelligence and ethical considerations in health care: a scoping review and ethics checklist

Yilin Ning  1 , Salinelat Teixayavong  1 , Yuqing Shang  1 , Julian Savulescu  2 , Vaishaanth Nagaraj  3 , Di Miao  1 , Mayli Mertens  4 , Daniel Shu Wei Ting  5 , Jasmine Chiat Ling Ong  6 , Mingxuan Liu  1 , Jiuwen Cao  7 , Michael Dunn  8 , Roger Vaughan  9 , Marcus Eng Hock Ong  10 , Joseph Jao-Yiu Sung  11 , Eric J Topol  12 , Nan Liu  13
Affiliations

Generative artificial intelligence and ethical considerations in health care: a scoping review and ethics checklist

Yilin Ning et al. Lancet Digit Health. 2024 Nov.

Erratum in

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.

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Conflict of interest statement

Declaration of interests NL reports funding from the Duke–NUS Signature Research Programme funded by the Ministry of Health, Singapore. JS reports funding from the Wellcome Trust and roles as a Bioethics Committee consultant for Bayer and as an advisory panel member for the Hevolution Foundation. DSWT reports funding from National Medical Research Council, Singapore, Duke–NUS Medical School, and Agency for Science, Technology, and Research; patents on deep learning systems for diabetic retinopathy, glaucoma, and age-related macular degeneration (co-inventor; 2017), a computer-implemented method for training an image classifier using weakly annotated training data (2019), and automatically extracting measurements from an image of a display of a measurement device (2020); has a leadership role (unpaid) as Chair of the AI and Digital Innovation Standing Committee and the Asia–Pacific Academy of Ophthalmology; and serves on the executive committees of the American Academy of Ophthalmology AI Committee, STARD-AI Steering Committee, Imperial College London, DECIDE-AI, and QUANDAS-AI. EJT reports funding from the National Institutes of Health (NIH) Grant and consulting fees as an adviser to Tempus Labs, Pheno.AI, and Abridge. All other authors declare no competing interests.

Figures

Figure 1:
Figure 1:. Study selection
AI=artificial intelligence.
Figure 2:
Figure 2:. Evidence gap map of ethical issues caused or resolved by generative AI methods for different data modalities
Some articles included discussed multiple data modalities and ethical issues caused or resolved by generative AI. An article might discuss both issues caused and resolved by generative AI methods, and might cover multiple ethical issues or data modalities. AI=artificial intelligence.
See this image and copyright information in PMC

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