Scientific Evidence for Clinical Text Summarization Using Large Language Models: Scoping Review.

Journal: Journal of medical Internet research
PMID: 40371947

Abstract

BACKGROUND: Information overload in electronic health records requires effective solutions to alleviate clinicians' administrative tasks. Automatically summarizing clinical text has gained significant attention with the rise of large language models. While individual studies show optimism, a structured overview of the research landscape is lacking.

Authors

  • Lydie Bednarczyk
    Division of Medical Information Sciences, University Hospital of Geneva, Geneva, Switzerland.
  • Daniel Reichenpfader
    Bern University of Applied Sciences, Institute for Medical Informatics, Quellgasse 21, Biel/Bienne, 2502, Bern, Switzerland.
  • Christophe Gaudet-Blavignac
    Division of Medical Information Sciences Geneva University Hospitals and University of Geneva.
  • Amon Kenna Ette
    Division of Medical Information Sciences, University Hospital of Geneva, Geneva, Switzerland.
  • Jamil Zaghir
    Division of Medical Information Sciences, University Hospitals of Geneva.
  • Yuanyuan Zheng
    State Key Laboratory of Chemical Biology and Drug Discovery, Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University Hunghom Kowloon Hong Kong P. R. China kwok-yin.wong@polyu.edu.hk.
  • Adel Bensahla
    Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland.
  • Mina Bjelogrlic
    Division of Medical Information Sciences, University Hospitals of Geneva and University of Geneva, Geneva, Switzerland.
  • Christian Lovis
    Division of Medical Information Sciences Geneva University Hospitals and University of Geneva.

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