The DOI matches its Crossref record; 89 of the 94 references it cites were found in a registry or an index, and 2 are not checked yet.
Artificial intelligence, ChatGPT, and other large language models for social determinants of health: Current state and future directions
Open accessAbstract
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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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science · doi:10.1126/science.aax2342 · Cited by 7,137 · cited 1×
“75 This will remain a pervasive problem since more than half of the datasets used for clinical AI development originate from either the USA or China.”
BackgroundOn the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445922 · Cited by 6,988 · cited 2×
“52,64 Particular to LMICs, the engagement of low-cost workers to perform annotation of toxic language (including text embedding hate speech, sexual abuse, violence) may contribute to negative psychological health outcomes among these workers.”
BackgroundThe global landscape of AI ethics guidelines
Jobin, A., Ienca, M., & Vayena, E. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0088-2 · Cited by 6,220
Actual causes of death in the United States
McGinnis, J. M. (1993) · JAMA: The Journal of the American Medical Association · doi:10.1001/jama.1993.03510180077038 · Cited by 5,480
Large language models in medicine
Thirunavukarasu, A. J., Ting, D. S. J., Elangovan, K., Gutierrez, L., Tan, T. F., & Ting, D. S. W. (2023) · Nature Medicine · doi:10.1038/s41591-023-02448-8 · Cited by 3,944
Self-citationThe social determinants of health: It’s time to consider the causes of the causes
Braveman, P., & Gottlieb, L. (2014) · Public Health Reports® · doi:10.1177/00333549141291s206 · Cited by 3,861
The COVID-19 pandemic and health inequalities
Bambra, C., Riordan, R., Ford, J., & Matthews, F. (2020) · Journal of Epidemiology and Community Health · doi:10.1136/jech-2020-214401 · Cited by 2,118
Sheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., Colen, R. R., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-69250-1 · Cited by 1,646 · cited 1×
“82,83 Block-chain technology facilitates secure and auditable exchange of sensitive patient data, safeguarding data integrity and immuta-bility.”
BackgroundPrivacy in the age of medical big data
Price, W. N., & Cohen, I. G. (2019) · Nature Medicine · doi:10.1038/s41591-018-0272-7 · Cited by 1,522
Research works printed but not linked: 3
- 2017, Social Determinants of Health (SDOH), NEJM Catalyst
- Zelmer, 2022, Transforming Care and Outcomes with Digital Health Through and Beyond the Pandemic, NEJM Catalyst
- Perrigo, 2023, Exclusive: The $2 Per Hour Workers Who Made ChatGPT Safer, Time
Cited by
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Items 101–112 of 112
Dulal, S. S., Jain, D. K., Sherman, Z., Zhang, M., Kim, R., Subramanian, S. V., & Kim, J. (2026) · The Professional Geographer · doi:10.1080/00330124.2026.2667385
Domain-Specific NLP in High-Stakes Sectors
Khairallah, M. (2026) · International Symposium of Systems, Advanced Technologies and Knowledge (ISSATK) · doi:10.1109/issatk69667.2026.11566857
AI-Driven Employment: Navigating Algorithmic Discrimination and Public Health (Preprint)Preprint
Alexander, A. A. (2026) · doi:10.2196/preprints.104438
Elmosaad, Y. M. (2026) · Healthcare · doi:10.3390/healthcare14142114
Pratiwi, M. D., Yuniarto, P. F., Putri, E. M., Mawarni, O. I., Winartiana, Hidayati, E. N., Dhafin, A. A., & Herman (2026) · ADIMASKA JURNAL PENGABDIAN MASYARAKAT KESEHATAN · doi:10.30737/mavfpf13
Berenson, J., Marten, R., Mao, W., Basu, D., Kosińska, M., Cassels, A., Yamey, G., & Rasanathan, K. (2026) · International Journal for Equity in Health · doi:10.1186/s12939-026-02900-4
O'Leary, J., Dunne, A., & O'Brien, S. (2025) · Archives of Pharmacy Practice · doi:10.51847/sgqoul1mc5
Khosravi, M., Zahrayi, F., Beiky, F., Miri, A., Nakhaei, M., Ghoddoosi-Nejad, D., & Saadatjoo, S. A. (2026) · Digital Health · doi:10.1177/20552076261485226
Dautova, S. B., Abdumannopovna, R. M., Kazimbetova, Z. Z., Баходировна, М. Н., Bayniyazova, G. M., Utebaev, T. T., & Kuchiboyev, S. A. (2026) · Eurasia Journal of Mathematics Science and Technology Education · doi:10.29333/ejmste/19560
Gonçalves, A. R. M., Maia, N. D. S., Maia, M. D. S., Barreto, M. C. A., Bonidia, R. P., & de Carvalho, A. (2026) · Simpósio Brasileiro de Bancos de Dados (SBBD) · doi:10.5753/sbbd_estendido.2026.249582
Alzahrani, M. S., Almarghalani, D. A., Hammad, A. M., Alharbi, A. S., Alhayfani, M. K., AlHarthi, M. S., Momenah, T., Almehmadi, K., Alzahrani, A. H., Alazwari, D. M., et al. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1936664
Aadam, A. A., Daahiye, M. A., Mohamed, A. A., Ali, A. D., & Khalif, I. A. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1942140
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
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