The promise of large language models in health care
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Due to their training on a vast array of internet content, LLMs may inadvertently incorporate biases across the web, posing severe threats in sensitive fields.
“Due to their training on a vast array of internet content, LLMs may inadvertently incorporate biases (Arora and Arora 2023) (e.g., gender bias, racial bias, geopolitical biases, religious bias, nationality bias, sexual orientation bias, and age bias, etc.) across the web, posing severe threats in sensitive fields (Korngiebel and Mooney 2021).”
From Large language models in medical and healthcare fields: applications, advances, and challenges (2024) · cites this paper as Arora and Arora 2023
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Large language models in medicine
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- Authors
- Arora, A., & Arora, A.
- Affiliations
- University of Cambridge (GB)
- Corresponding author
- Arora, Anmol
- ORCID
- Arora, Anmol
- Venue
- The Lancet
- Publisher
- Elsevier BV
- Published
- 2023-02
- Volume
- 401
- Issue
- 10377
- Pages
- 641
- ISSN
- 0099-5355 (ISSN-L)
- 0140-6736
- OpenAlex
- W4321606060
- Cited by
- 153
- References
- 3
- Citations by year
- 2026: 23 · 2025: 49 · 2024: 43 · 2023: 37 · 2022: 1
- FWCI
- 6.07
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The Lancet (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- large language models, generative AI, health care, artificial general intelligence, ChatGPT, GPT-3, BERT, clinical applications of AI
- MeSH terms
- Delivery of Health Care, Health Facilities, Humans, Language
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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