Harnessing an Artificial Intelligence-Based Large Language Model With Personal Health Record Capability for Personalized Information Support in Postsurgery Myocardial Infarction: Descriptive Qualitative Study.

Journal: Journal of medical Internet research
PMID: 40305084

Abstract

BACKGROUND: Myocardial infarction (MI) remains a leading cause of morbidity and mortality worldwide. Although postsurgical cardiac interventions have improved survival rates, effective management during recovery remains challenging. Traditional informational support systems often provide generic guidance that does not account for individualized medical histories or psychosocial factors. Recently, artificial intelligence (AI)-based large language models (LLM) tools have emerged as promising interventions to deliver personalized health information to post-MI patients.

Authors

  • Ting-Ting Yang
    Department of Dermatology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan.
  • Hong-Xia Zheng
    Department of Cardiology, West China Hospital, Sichuan University, Chengdu, China.
  • Sha Cao
    Computational Systems Biology Lab, Department of Biochemistry and Molecular Biology, and Institute of Bioinformatics, University of Georgia, GA 30602, USA.
  • Mei-Ling Jing
    Department of Cardiology, West China Hospital, Sichuan University, Chengdu, China.
  • Ju Hu
    Department of Cardiology, West China Hospital, Sichuan University, Chengdu, China.
  • Yan Zuo
    Department of Gynecology and Obstetrics Nursing, West China Second University Hospital, Sichuan University, Chengdu, China.
  • Qing-Yong Chen
    Department of Cardiology, West China Hospital, Sichuan University, Chengdu, China.
  • Jian-Jun Zhang
    Department of Gynecology and Obstetrics, West China Second University Hospital, Sichuan University, Chengdu, China.

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