大型语言模型聊天机器人用于结石患者教育:范围审查
Reda Goudrar1, Othmane Zekraoui1, Ibrahim Moussa1
1Faculty of Medicine, University of Montreal, Montreal, QC, Canada.
World journal of urology
|October 29, 2025
概括
大型语言模型 (LLM) 聊天机器人对结石疾病 (KSD) 患者教育有希望,但需要提高可读性和遵守指南. 量身定制的泌尿器科工具和患者的意见可以提高其有效性.
科学领域:
- 医疗保健中的人工智能
- 医疗信息学 医疗信息学
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
背景情况:
- 人工智能 (AI) 聊天机器人对健康信息的使用越来越多,特别是在年轻人中.
- 越来越多的兴趣在于利用人工智能在慢性病患者教育中,如结石病 (KSD).
研究的目的:
- 进行关于大型语言模型 (LLM) 聊天机器人在KSD患者教育方面的表现的文献范围审查.
- 评估LLM产生的内容对KSD患者的准确性,可读性,可理解性和可操作性.
主要方法:
- 从2015年到2025年2月,对多个数据库 (Ovid MEDLINE,Embase,CENTRAL,Web of Science,CINAHL,Google Scholar) 进行系统的搜索.
- 坚持乔安娜·布里格斯研究所的审查范围的方法.
- 由两名审查员进行独立审查和数据提取,第三名审查员负责冲突解决.
主要成果:
- 包括17项研究,评估了用于KSD教育的LLM聊天机器人.
- 可读性往往超过了推水平,尽管提示提高了它.
- 准确性通常是足够的到非常好的,有些与传统来源相比.
- 可理解性,可操作性和准则遵守性都显示出局限性.
- 患者的感知是混合的,但总的来说是有利的;注意到一些指导方针的偏差.
结论:
- LLM聊天机器人有可能为KSD患者教育和减少医生工作量提供帮助.
- 目前的限制包括可读性,遵循指南,可理解性和可操作性.
- 开发泌尿器科专用工具,验证内容和患者参与可以减轻这些局限性.
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