A Survey of Large Language Models for Healthcare: From Data, Technology, and Applications to Accountability and Ethics
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CNNs, when trained on publicly available chest x-ray data sets, may show a tendency to underdiagnose specific populations, such as individuals from marginalized communities (eg, Black and Hispanic patients), women, and Medicaid recipients.
“One study showed that CNNs, when trained on publicly available chest x-ray data sets, may show a tendency to underdiagnose specific populations, such as individuals from marginalized communities (eg, Black and Hispanic patients), women, and Medicaid recipients [181].”
From Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook. (2024) · cites this paper as [181]
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References 321 · Cited by 69
- Authors
- He, K., Mao, R., Lin, Q., Ruan, Y., Lan, X., Feng, M., & Cambria, E.
- Affiliations
- National University of Singapore (SG)
- Soochow University (CN)
- Corresponding author
- Feng, Mengling
- Venue
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Published
- 2024
- ISSN
- 1556-5068 (ISSN-L)
- OpenAlex
- W4395703766
- Cited by
- 69
- References
- 321
- Citations by year
- 2026: 9 · 2025: 28 · 2024: 31 · 2023: 1
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- SSRN Electronic Journal (acceptedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- large language models, medical applications, pre-trained language models, generative AI, fairness, accountability, transparency, AI ethics, open source resources, LLM benchmark
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.
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