Clinical Management of Wasp Stings Using Large Language Models: Cross-Sectional Evaluation Study.

Journal: Journal of medical Internet research
Published Date: Jun 4, 2025

Abstract

BACKGROUND: Wasp stings are a significant public health concern in many parts of the world, particularly in tropical and subtropical regions. The venom of wasps contains a variety of bioactive compounds that can lead to a wide range of clinical effects, from mild localized pain and swelling to severe, life-threatening allergic reactions, such as anaphylaxis. With the rapid development of artificial intelligence (AI) technologies, large language models (LLMs) are increasingly being used in health care, including emergency medicine and toxicology. These models have the potential to assist health care professionals in making fast and informed clinical decisions. This study aimed to assess the performance of 4 leading LLMs-ERNIE Bot 3.5 (Baidu), ERNIE Bot 4.0 (Baidu), Claude Pro (Anthropic), and ChatGPT 4.0-in managing wasp sting cases, with a focus on their accuracy, comprehensiveness, and decision-making abilities.

Authors

  • Wei Pan
  • Shuman Zhang
    Department of Emergency Medicine, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, China.
  • Yonghong Wang
    State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, P.O. Box 329, Shanghai, 20037, China. yhwang@ecust.edu.cn.
  • Zhenglin Quan
    The Intensive Care Unit, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan, Guangdong, China.
  • Yanxia Zhu
    Cardiopulmonary Rehabilitation Center, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, China.
  • Zhicheng Fang
    Department of Emergency Medicine, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, China.
  • Xianyi Yang
    Department of Emergency Medicine, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, China.

Keywords

External Resources

Popular Topics
Recent Journals