评估患者与医疗信息的大型语言模型的互动
Nicolas Carl1,2, Sarah Haggenmüller2, Christoph Wies3,2
1Department of Urology, University Medical Center Mannheim, Ruprecht-Karls University of Heidelberg, Mannheim, Germany.
BJU international
|February 19, 2025
概括
患者发现大型语言模型 (LLM) 对医疗信息有用,在可理解性和用户友好性方面评价它们很高. 虽然泌尿科医生更喜欢复杂的问题,但LLMs在临床环境中显示出患者教育的潜力.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 患者参与 患者参与
背景情况:
- 大型语言模型 (LLM) 越来越多地被探索其在医疗保健中的潜力.
- 在临床环境中理解患者与LLM驱动的聊天机器人的互动对于采用至关重要.
- 评估LLM提供的医疗信息的质量和实用性是一个新兴的研究领域.
研究的目的:
- 为了调查现实世界患者的互动,使用LLM驱动的聊天机器人进行泌尿器科咨询.
- 评估LLM生成的医疗信息的感知效用,完整性,可理解性和用户友好性.
- 为了比较患者对信息来源的偏好:LLMs,泌尿科医生或搜索引擎.
主要方法:
- 300名寻求泌尿器科咨询的患者与GPT-4聊天机器人进行了互动.
- 互动后的调查评估了感知到的信息质量和用户友好性.
- 患者比较了LLMs,泌尿科医生和搜索引擎来回答他们的问题.
主要成果:
- 大多数292名完成者发现聊天机器人有用,完整,易于理解和易于使用.
- 人类泌尿科医生在可理解性方面被评为高于LLMs.
- 53%的患者更喜欢LLMs而不是搜索引擎来回答问题.
结论:
- 在临床环境中,LLM显示了提高患者教育和沟通的巨大潜力.
- 患者非常重视LLM的用户友好性和全面性,以获得医疗信息.
- 通过解决患者的初步查询,LLM可以帮助减轻医疗保健提供者的时间限制.
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