评估人工智能产生的对患者关于体生注射问题的反应
Benjamin W King1, Jesse Seilern Und Aspang1, Kyle Hammond1
1Department of Orthopaedics, Emory University School of Medicine, Atlanta, Georgia, USA.
Orthopaedic journal of sports medicine
|February 23, 2026
概括
人工智能 (AI) 大型语言模型 (LLM) 通常提供准确的脊椎生物学信息,但是在患者适当的阅读水平以上写的. 双子座比ChatGPT更准确,但所有模型都需要医生监督患者教育.
科学领域:
- 整形医生 整形医生 整形医生
- 生物技术是生物技术.
- 人工智能的人工智能
背景情况:
- 患者对脊椎生物注射的兴趣正在增加.
- 患者越来越多地使用人工智能大语言模型 (LLM) 来获取健康信息.
- 人工智能为患者生成的脊椎生物学信息的准确性和可读性尚不清楚.
研究的目的:
- 评估3个受欢迎的AI LLM (ChatGPT,Gemini,Grok) 对患者关于脊椎生理注射的常见问题的答案的准确性和可读性.
主要方法:
- 横截面研究评估了20个常见的患者问题,涉及ChatGPT 4o,Gemini 2.5 Flash和Grok 3.
- 四位独立审查员使用ChatGPT响应评分系统 (CRRS) 和AI响应指标 (AIRM) 评估了响应的准确性.
- 使用弗莱什-金凯德等级水平 (FKGL) 测量可读性.
主要成果:
- 答案准确度通常是可以接受的,但25-50%需要澄清.
- 与ChatGPT相比,双子号显示出更高的准确性 (CRRS,P=.04;AIRM,P=.03).与ChatGPT相比,双子号显示出更高的准确性.
- 所有人工智能模型都产生了大学阅读水平或更高的响应,超过了推的患者教育水平.
结论:
- 人工智能LLM通常提供准确的脊椎生物学信息,但缺乏适合患者的可读性.
- 双子座显示了比ChatGPT更好的准确性,但所有模型都表现出清晰度的限制.
- 人工智能应对应该补充,而不是取代医生领导的患者教育.
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