3个对话生成型人工智能模型的性能,用于计算局部麻醉剂的最大安全剂量:比较分析
Mélanie Suppan1,2, Pietro Elias Fubini1,2, Alexandra Stefani1,2
1Division of Anaesthesiology, Department of Acute Care Medicine, Geneva University Hospitals, Rue Gabrielle-Perret-Gentil 4, Geneva, 1211, Switzerland.
像ChatGPT,Copilot和Gemini这样的生成人工智能模型尚未可靠地计算局部麻醉剂 (LA) 的最大安全剂量. 目前的AI工具显示出严重的不准确性和风险,强调需要继续依赖临床专业知识,以确保安全的LA管理.
科学领域:
- 医疗信息学 医疗信息学
- 麻醉学 麻醉学
- 人工智能在医学中的应用
背景情况:
- 生成型人工智能 (AI) 显示出优化医疗决策的潜力.
- 准确计算最大安全局部麻醉剂 (LA) 剂量在麻醉学中至关重要,以防止局部麻醉系统毒性 (LAST).
- 目前的LA剂量依赖于经验指南和临床医生的经验,导致变化和错误.
研究的目的:
- 评估三个生成AI模型 (ChatGPT,Copilot,Gemini) 在计算最大安全LA剂量的有效性和安全性.
- 确定这些AI模型对LA剂量计算的潜在临床实用性.
主要方法:
- 一项比较分析使用了10个模拟的临床细节中的51项问卷.
- 人工智能生成的LA剂量计算与科学验证的参考剂量进行了比较.
- 进行了量化剂量比较和独立审查员的定性评估.
主要成果:
- 所有的人工智能模型都产生了与LA计算原则一致的响应,但安全性差异很大.
- 双子座经常推迟专家或提供剂量超过安全限值140% (SD103%) 的混合物.
- 聊天GPT (90%不安全,+198%超限) 和Copilot (67%不安全,+217%超限) 的表现不佳,质量评分为"差".
结论:
- 目前的生成AI模型 (Gemini,ChatGPT,Copilot) 缺乏安全的LA剂量计算所需的准确性和可靠性.
- 这些AI模型不应该被用作LA剂量的决策工具.
- 临床医生必须继续依赖专业知识和患者评估,以确保安全的LA给药,直到得到验证的AI解决方案出现.
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