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Editorial
. 2024 Sep 1;31(9):1801-1811.
doi: 10.1093/jamia/ocae202.

Large language models in biomedicine and health: current research landscape and future directions

Zhiyong Lu  1 , Yifan Peng  2 , Trevor Cohen  3 , Marzyeh Ghassemi  4 , Chunhua Weng  5 , Shubo Tian  1
Affiliations
Editorial

Large language models in biomedicine and health: current research landscape and future directions

Zhiyong Lu et al. J Am Med Inform Assoc. .
No abstract available

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Conflict of interest statement

All authors declare no conflict of interests.

Figures

Figure 1.
Figure 1.
Breakdown of submissions and accepted articles by continent (A) and number and breakdown of accepted articles by content type (B).
Figure 2.
Figure 2.
LLMs used in the accepted papers (A) and evaluation methods used in the accepted papers (B).
See this image and copyright information in PMC

References

    1. Tian S, Jin Q, Yeganova L, et al. Opportunities and challenges for ChatGPT and large language models in biomedicine and health. Brief Bioinform. 2024;25(1):bbad493. 10.1093/bib/bbad493 - DOI - PMC - PubMed
    1. Jin Q, Leaman R, Lu Z. PubMed and beyond: biomedical literature search in the age of artificial intelligence. eBioMedicine. 2024;100:104988. 10.1016/j.ebiom.2024.104988 - DOI - PMC - PubMed
    1. Raiaan MAK, Mukta M, Fatema K, et al. A review on large language models: architectures, applications, taxonomies, open issues and challenges. IEEE Access. 2024;12:26839-26874. 10.1109/ACCESS.2024.3365742 - DOI
    1. Liu J, Yang M, Yu Y, et al. 2024. Large language models in bioinformatics: applications and perspectives. arXiv, arXiv:2401.04155, preprint: not peer reviewed.
    1. Peng Y, Rousseau JF, Shortliffe EH, et al. AI-generated text may have a role in evidence-based medicine. Nat Med. 2023;29(7):1593-1594. 10.1038/s41591-023-02366-9 - DOI - PMC - PubMed

Publication types