Large Language Models in Medicine: The Potentials and Pitfalls-Reference-Cited by-同舟云学术

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Large Language Models in Medicine: The Potentials and Pitfalls

Published:2024-02 Issue:2 Volume:177 Page:210-220
ISSN:0003-4819
Container-title:Annals of Internal Medicine
language:en
Short-container-title:Ann Intern Med

Author:

Omiye Jesutofunmi A.¹,Gui Haiwen²^ORCID,Rezaei Shawheen J.²^ORCID,Zou James³,Daneshjou Roxana¹^ORCID

Affiliation:

1. Department of Dermatology and Department of Biomedical Data Science, Stanford University, Stanford, California (J.A.O., R.D.)

2. Department of Dermatology, Stanford University, Stanford, California (H.G., S.J.R.)

3. Department of Biomedical Data Science, Stanford University, Stanford, California (J.Z.).

Publisher

American College of Physicians

Link

https://www.acpjournals.org/doi/pdf/10.7326/M23-2772

Reference60 articles.

1. Touvron H, Lavril T, Izacard G, et al. LLaMA: open and efficient foundation language models. arXiv. Preprint posted online 27 February 2023. doi:10.48550/arXiv.2302.13971

2. Bommasani R, Hudson DA, Adeli E, et al. On the opportunities and risks of foundation models. arXiv. Preprint posted online 16 August 2021. Revised 12 July 2022. doi:10.48550/arXiv.2108.07258

3. Zhao WX, Zhou K, Li J, et al. A survey of large language models. arXiv. Preprint posted online 31 March 2023. Revised 29 June 2023. doi:10.48550/arXiv.2303.18223

4. Liang P, Bommasani R, Lee T, et al. Holistic evaluation of language models. arXiv. Preprint posted online 16 November 2022. Revised 1 October 2023. doi:10.48550/arXiv.2211.09110

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