Identification of Online Health Information Using Large Pretrained Language Models: Mixed Methods Study.

Journal: Journal of medical Internet research
PMID: 40367512

Abstract

BACKGROUND: Online health information is widely available, but a substantial portion of it is inaccurate or misleading, including exaggerated, incomplete, or unverified claims. Such misinformation can significantly influence public health decisions and pose serious challenges to health care systems. With advances in artificial intelligence and natural language processing, pretrained large language models (LLMs) have shown promise in identifying and distinguishing misleading health information, although their effectiveness in this area remains underexplored.

Authors

  • Dongmei Tan
    College of Medical Informatics, Chongqing Medical University, Chongqing, China.
  • Yi Huang
    Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
  • Ming Liu
    School of Land Engineering, Chang'an University, Xi'an 710064, China; Xi'an Key Laboratory of Territorial Spatial Information, School of Land Engineering, Chang'an University, Xi'an 710064, China. Electronic address: mingliu@chd.edu.cn.
  • Ziyu Li
    Gastrointestinal Cancer Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China.
  • Xiaoqian Wu
  • Cheng Huang
    James H. Clark Center, Stanford University, Stanford, California, USA.

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