基于大型语言模型的聊天机器人作为心脏病发作急救建议的来源
Alexei A Birkun1, Adhish Gautam2
1Department of General Surgery, Anaesthesiology, Resuscitation and Emergency Medicine, Medical Academy named after S.I. Georgievsky of V.I. Vernadsky Crimean Federal University, Simferopol, Russian Federation.
Current problems in cardiology
|August 28, 2023
概括
大型语言模型聊天机器人可以在心脏病发作期间提供实时急救建议. 然而,当前的聊天机器人响应经常遗漏关键指令并含有不准确的信息,这使得它们无法用于紧急指导.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 紧急医疗 紧急医疗
背景情况:
- 大型语言模型 (LLM) 显示出产生类似人类反应的前景.
- 假设,LLM驱动的聊天机器人可以为心血管紧急情况提供实时急救建议.
- 这种人工智能对关键健康信息的可靠性需要彻底评估.
研究的目的:
- 评估聊天机器人对心脏病发作情景的急救反应的质量.
- 将聊天机器人生成的建议与已建立的国际急救指南进行比较.
- 在人工智能驱动的健康建议中识别遗漏,不准确和误导性信息.
主要方法:
- 模拟用户向新的Bing聊天机器人询问,使用来自冈比亚,印度和美国的"心脏病发作怎么做"提示.
- 根据2020年国际急救,复苏和教育指南,评估每个国家的20个聊天机器人响应,使用检查清单.
- 分析响应一致性,遗漏救命指令,并包含多余或不一致的指令.
主要成果:
- 聊天机器人响应提供了一些急救指导,但通常忽略了关键步骤,如鼓励休息或启动心肺复苏 (CPR).
- 与指导方针的平均响应一致性在7.3% (印度) 到16.8% (美国) 之间.
- 很大一部分回复包含了多余或不一致的指令:45%在印度,25%在冈比亚和美国.
结论:
- 目前关于心脏病发作急救的聊天机器人建议由于遗漏,不准确和误导性指示而不可靠.
- 目前,聊天机器人不能被推为紧急急救信息的可靠来源.
- 进一步的研究和组织努力至关重要,以确保人工智能介导的健康咨询的可靠性和安全性.
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