Unveiling the Potential of Large Language Models in Transforming Chronic Disease Management: Mixed Methods Systematic Review

作者：Caixia Li, Yina Zhao, Yang Bai, Baoquan Zhao, Yetunde Oluwafunmilayo Tola, Carmen W.H. Chan, Meifen Zhang, Xia Fu · 发表于：Journal of Medical Internet Research · 年份：2025 · DOI：10.2196/70535 · 被引用次数：19 · 研究领域：Artificial Intelligence in Healthcare and Education、Machine Learning in Healthcare、Chronic Disease Management Strategies

Background Chronic diseases are a major global health burden, accounting for nearly three-quarters of the deaths worldwide. Large language models (LLMs) are advanced artificial intelligence systems with transformative potential to optimize chronic disease management; however, robust evidence is lacking. Objective This review aims to synthesize evidence on the feasibility, opportunities, and challenges of LLMs across the disease management spectrum, from prevention to screening, diagnosis, treatment, and long-term care. Methods Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines, 11 databases (Cochrane Central Register of Controlled Trials, CINAHL, Embase, IEEE Xplore, MEDLINE via Ovid, ProQuest Health & Medicine Collection, ScienceDirect, Scopus, Web of Science Core Collection, China National Knowledge Internet, and SinoMed) were searched on April 17, 2024. Intervention and simulation studies that examined LLMs in the management of chronic diseases were included. The methodological quality of the included studies was evaluated using a rating rubric designed for simulation-based research and the risk of bias in nonrandomized studies of interventions tool for quasi-experimental studies. Narrative analysis with descriptive figures was used to synthesize the study findings. Random-effects meta-analyses were conducted to assess the pooled effect estimates of the feasibility of LLMs in chronic disease management. Results A total ...