Leveraging generative AI for clinical evidence synthesis needs to ensure trustworthiness.

Journal: Journal of biomedical informatics
Published Date: Apr 10, 2024

Abstract

Evidence-based medicine promises to improve the quality of healthcare by empowering medical decisions and practices with the best available evidence. The rapid growth of medical evidence, which can be obtained from various sources, poses a challenge in collecting, appraising, and synthesizing the evidential information. Recent advancements in generative AI, exemplified by large language models, hold promise in facilitating the arduous task. However, developing accountable, fair, and inclusive models remains a complicated undertaking. In this perspective, we discuss the trustworthiness of generative AI in the context of automated summarization of medical evidence.

Authors

  • Gongbo Zhang
    Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, United States.
  • Qiao Jin
    National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
  • Denis Jered McInerney
    Northeastern University, the Khoury College of Computer Sciences, Boston 02115, USA.
  • Yong Chen
    Department of Urology, Chongqing University Fuling Hospital, Chongqing, China.
  • Fei Wang
    Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, United States.
  • Curtis L Cole
    Information Technologies and Services Department, Weill Cornell Medicine, New York, New York, United States.
  • Qian Yang
    Center for Advanced Scientific Instrumentation, University of Wyoming, Laramie, WY, United States.
  • Yanshan Wang
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
  • Bradley A Malin
    Vanderbilt University, Nashville, TN.
  • Mor Peleg
    Department of Information Systems, University of Haifa, Rabin Bldg., 3498838 Haifa, Israel.
  • Byron C Wallace
    School of Information, University of Texas at Austin, Austin, Texas, USA.
  • Zhiyong Lu
    National Center for Biotechnology Information, Bethesda, MD 20894 USA.
  • Chunhua Weng
    Department of Biomedical Informatics, Columbia University.
  • Yifan Peng
    Department of Population Health Sciences, Weill Cornell Medicine, New York, USA.

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