Assessing large language models as assistive tools in medical consultations for Kawasaki disease.

Journal: Frontiers in artificial intelligence
Published Date: Mar 31, 2025

Abstract

BACKGROUND: Kawasaki disease (KD) presents complex clinical challenges in diagnosis, treatment, and long-term management, requiring a comprehensive understanding by both parents and healthcare providers. With advancements in artificial intelligence (AI), large language models (LLMs) have shown promise in supporting medical practice. This study aims to evaluate and compare the appropriateness and comprehensibility of different LLMs in answering clinically relevant questions about KD and assess the impact of different prompting strategies.

Authors

  • Chunyi Yan
    Department of Pediatric Cardiology, West China Second University Hospital, Sichuan University, Chengdu, China.
  • Zexi Li
    Department of Cardiology, West China Hospital, Sichuan University, Chengdu, China.
  • Yongzhou Liang
    Department of Pediatric Cardiology, West China Second University Hospital, Sichuan University, Chengdu, China.
  • Shuran Shao
    Department of Pediatric Cardiology, West China Second University Hospital, Sichuan University, Chengdu, China.
  • Fan Ma
    Department of Pediatric Cardiology, West China Second University Hospital, Sichuan University, Chengdu, China.
  • Nanjun Zhang
    Department of Pediatric Cardiology, West China Second University Hospital, Sichuan University, Chengdu, China.
  • Bowen Li
    Department of Pediatric Cardiology, West China Second University Hospital, Sichuan University, Chengdu, China.
  • Chuan Wang
    Department of Endocrinology and Metabolism, Qilu Hospital, Shandong University, Jinan, China.
  • Kaiyu Zhou
    Department of Pediatric Cardiology, West China Second University Hospital, Sichuan University, Chengdu, China.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals