在莫斯重建中评估人工智能对术后问题的反应
Areeb Shah1, Luke Schwetschenau1, Lisa Velez-Velez2
1Saint Louis University School of Medicine, St. Louis, Missouri.
Facial plastic surgery : FPS
|August 25, 2025
概括
像大型语言模型 (LLM) 这样的人工智能 (AI) 聊天机器人可以在莫斯面部重建后帮助患者的问题. 然而,人工智能反应的质量有所不同,需要医生持续监督以确保准确性和清晰度.
科学领域:
- 皮肤病学
- 人工智能
- 医疗信息学
背景情况:
- 在Mohs面部重建后,
- 大型语言模型 (LLM) 提供了改善患者沟通的潜力.
- 人工智能产生的医疗信息的可变性需要仔细评估.
研究的目的:
- 评估四位领先的LLM对莫斯面部重建后常见的术后问题的质量,准确性,全面性和可读性.
- 在此背景下比较Claude AI,ChatGPT,微软 Copilot 和 Google Gemini 的性能.
主要方法:
- 根据STROBE的指导方针进行了前性观察研究.
- 使用标准化提示函数向四个LLM (Claude AI,ChatGPT,Microsoft Copilot,Google Gemini) 提出了31个常见的术后问题.
- 面部整形外科医生使用经过验证的工具 (EQIP,利克特尺度,可读性公式) 进行了盲目评估.
主要成果:
- 克劳德人工智能在质量 (EQIP: 90.3),准确性 (4.55/5) 和全面性 (4.60/5) 方面表现出卓越的表现.
- 所有评估的LLM产生的反应都超过了6年级的阅读水平.
- 不同的LLM提供的信息质量和深度存在显著差异.
结论:
- 在接受Mohs面部重建的患者中,LLM显示出支持术后沟通的前景.
- 观察到的AI反应质量的变化强调了医生审查和监督的必要性.
- 需要进一步的研究来优化LLM在患者教育中的使用,同时确保医疗准确性.
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