Effectiveness of large language models in preoperative and discharge education: a systematic review based on an evaluation framework

心理干预 可信赖性 系统回顾 梅德林 医学 心理学 患者满意度 焦虑 计算机科学 应用心理学 循证医学 临床疗效 医学物理学 灰色文学 英语 项目评估 质量(理念) 结果(博弈论)
作者
Mengting Wang,Haoming Ma,Meihua Piao
出处
期刊:npj digital medicine [Nature Portfolio]
卷期号:9 (1): 122-122
标识
DOI:10.1038/s41746-025-02302-w
摘要

Large language models (LLMs) are increasingly incorporated into preoperative and discharge education, yet their effectiveness and the ways in which they are evaluated remain inconsistent. This systematic review assessed the effectiveness of LLM-based interventions and identified evidence gaps relevant to understanding how model characteristics may influence patient outcomes. We searched five databases from inception to April 18, 2025, ultimately including twenty studies. Outcomes were narratively synthesized, and interventions were evaluated using a published four-dimension framework, with reporting patterns visualized through a heatmap. Many studies reported benefits for anxiety reduction and selected satisfaction domains, whereas findings for pain, recovery, and other satisfaction elements showed no significant differences from conventional materials. Reporting of evaluation sub-dimensions was uneven, with trustworthiness and performance rarely documented alongside clinical endpoints. These gaps highlight the need for future research that integrates model-centric and patient-centric evaluations to support responsible clinical deployment.
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