Quality of Answers of Generative Large Language Models Versus Peer Users for Interpreting Laboratory Test Results for Lay Patients: Evaluation Study.

Journal: Journal of medical Internet research
Published Date: Apr 17, 2024

Abstract

BACKGROUND: Although patients have easy access to their electronic health records and laboratory test result data through patient portals, laboratory test results are often confusing and hard to understand. Many patients turn to web-based forums or question-and-answer (Q&A) sites to seek advice from their peers. The quality of answers from social Q&A sites on health-related questions varies significantly, and not all responses are accurate or reliable. Large language models (LLMs) such as ChatGPT have opened a promising avenue for patients to have their questions answered.

Authors

  • Zhe He
    School of Information, Florida State University, Tallahassee, FL, USA.
  • Balu Bhasuran
    DRDO-BU Center for Life Sciences, Bharathiar University Campus, Coimbatore, Tamilnadu, India.
  • Qiao Jin
    National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
  • Shubo Tian
    Department of Statistics, Florida State University, Tallahassee, FL 32306, USA.
  • Karim Hanna
    Morsani College of Medicine, University of South Florida, Tampa, FL, United States.
  • Cindy Shavor
    Morsani College of Medicine, University of South Florida, Tampa, FL, United States.
  • Lisbeth Garcia Arguello
    Morsani College of Medicine, University of South Florida, Tampa, FL, United States.
  • Patrick Murray
    Morsani College of Medicine, University of South Florida, Tampa, FL, United States.
  • Zhiyong Lu
    National Center for Biotechnology Information, Bethesda, MD 20894 USA.

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