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Artificial intelligence, ChatGPT, and other large language models for social determinants of health: Current state and future directions
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This perspective highlights the importance of addressing social determinants of health (SDOH) in patient health outcomes and health inequity, a global problem exacerbated by the COVID-19 pandemic. We provide a broad discussion on current developments in digital health and artificial intelligence (AI), including large language models (LLMs), as transformative tools in addressing SDOH factors, offering new capabilities for disease surveillance and patient care. Simultaneously, we bring attention to challenges, such as data standardization, infrastructure limitations, digital literacy, and algorithmic bias, that could hinder equitable access to AI benefits. For LLMs, we highlight potential unique challenges and risks including environmental impact, unfair labor practices, inadvertent disinformation or "hallucinations," proliferation of bias, and infringement of copyrights. We propose the need for a multitiered approach to digital inclusion as an SDOH and the development of ethical and responsible AI practice frameworks globally and provide suggestions on bridging the gap from development to implementation of equitable AI technologies.
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“Recent studies have utilized the use of large language models (LLMs) for the identification of SDoH factors19,24,45,46.”
From Social determinants of health extraction from clinical notes across institutions using large language models (2025) · cites this paper as [19]
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“Firstly, many studies, even the recent ones19–25 have focused on a limited set of SDoH factors (up to 8 factors).”
From Social determinants of health extraction from clinical notes across institutions using large language models (2025) · cites this paper as [19]
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Metadata
- Authors
- Ong, J. C. L., Seng, B. J. J., Law, J. Z. F., Low, L. L., Kwa, A. L. H., Giacomini, K. M., & Ting, D. S. W.
- Affiliations
- SingHealth (SG)
- National University of Singapore (SG)
- University of California, San Francisco (US)
- Corresponding author
- Ting, Daniel Shu Wei
- ORCID
- Venue
- Cell Reports Medicine
- Publisher
- Elsevier BV
- Published
- 2024-01
- Volume
- 5
- Issue
- 1
- Pages
- 101356
- ISSN
- 2666-3791 (ISSN-L)
- PMC
- PMC10829781
- OpenAlex
- W4390921272
- Cited by
- 111
- References
- 94
- Citations by year
- 2026: 32 · 2025: 51 · 2024: 28
- FWCI
- 2.53
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cell Reports Medicine (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- Agency for Science, Technology and Research (A20H4g2141, H20C6a0032, MOH-CIAINV21nov-001)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- social determinants of health, large language models, ChatGPT, artificial intelligence in healthcare, health disparities, digital health, disease surveillance, algorithmic bias, digital literacy, digital inclusion, environmental impact of AI, AI hallucination, copyright infringement, AI ethics framework, COVID-19 pandemic
- MeSH terms
- COVID-19, Artificial Intelligence, Humans, Language, Pandemics, Social Determinants of Health
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 ›
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