白衣机器人:大型语言模型是患者教育的未来吗? 一个多中心的横截面分析
Ughur Aghamaliyev1, Javad Karimbayli2, Athanasios Zamparas1
1Department of General, Visceral, and Transplant Surgery, Ludwig-Maximilians-University Munich, Munich, Germany.
International journal of surgery (London, England)
|January 29, 2025
概括
像GPT-4o这样的大型语言模型 (LLM) 可以通过为经常问的问题提供准确的音频答案来改善外科患者的教育. 手术后的信息比手术前的指导更准确,更无害.
科学领域:
- 医疗信息学 医疗信息学
- 外科教育的外科教育
- 医疗保健中的人工智能
背景情况:
- 全球发生数百万次手术,手术后死亡率很高.
- 有效的患者教育至关重要,但受到手术前患者压力的挑战.
- 大型语言模型 (LLM) 为增强患者信息传递提供了一个潜在的解决方案.
研究的目的:
- 评估从聊天生成预训练变压器-4o (GPT-4o) 到外科病人的常见问题中的音频响应的准确性,相关性和安全性.
- 为了比较GPT-4o在手术前和手术后查询以及大手术和小手术过程中的性能.
主要方法:
- 一项横截面研究评估了120个基于音频的答案,从GPT-4o到一般和程序特定的常见问题.
- 常见问题涵盖了手术前和后的六种手术程序 (主要和次要).
- 来自6所德国大学医院的11名外科医生评估了答案的准确性,相关性和潜在危害.
主要成果:
- GPT-4o获得了高平均分:准确度为4.12/5,相关性为4.46/5,潜在危害为0.22/5.
- 手术后的反应比手术前的反应更准确,更相关,更无害.
- 对小手术的反应比对大手术的反应更准确.
结论:
- 通过提供准确和相关的信息,GPT-4o显示了增强外科患者教育的巨大潜力.
- 来自GPT-4o的术后指导比手术前指导更可靠,更安全.
- 将GPT-4o集成到医疗保健系统中可以提高患者满意度和知情决策.
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