Assessing the Utility of ChatGPT Throughout the Entire Clinical Workflow: Development and Usability Study.

Journal: Journal of medical Internet research
PMID: 37606976

Abstract

BACKGROUND: Large language model (LLM)-based artificial intelligence chatbots direct the power of large training data sets toward successive, related tasks as opposed to single-ask tasks, for which artificial intelligence already achieves impressive performance. The capacity of LLMs to assist in the full scope of iterative clinical reasoning via successive prompting, in effect acting as artificial physicians, has not yet been evaluated.

Authors

  • Arya Rao
    Harvard Medical School, Boston, MA, USA.
  • Michael Pang
    Orthopaedic Machine Learning Laboratory, Brigham & Women's Hospital, Boston, Massachusetts, U.S.A.
  • John Kim
    Harvard Medical School, Boston, MA, USA.
  • Meghana Kamineni
    Medically Engineered Solutions in Healthcare Incubator, Innovation in Operations Research Center (MESH IO), Massachusetts General Hospital, Boston, MA, United States.
  • Winston Lie
    Medically Engineered Solutions in Healthcare Incubator, Innovation in Operations Research Center (MESH IO), Massachusetts General Hospital, Boston, MA, United States.
  • Anoop K Prasad
    Medically Engineered Solutions in Healthcare Incubator, Innovation in Operations Research Center (MESH IO), Massachusetts General Hospital, Boston, MA, United States.
  • Adam Landman
    Harvard Medical School, Boston, Massachusetts, USA.
  • Keith Dreyer
    Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts.
  • Marc D Succi
    Harvard Medical School, Boston, MA, USA. msucci@mgh.harvard.edu.

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