人工智能模型可以适当地推膝关节整形外科医生吗?
Colin M Emrich1, Ethan C Gazan1, Alexander J Baur1,2
1Liberty University College of Osteopathic Medicine, Lynchburg, VA, USA.
概括
大型语言模型 (LLM) 可以建议全膝关节整形 (TKA) 外科医生,但准确性各不相同. 虽然66%的建议是适当的,但联系信息的可靠性很低,这凸显了需要进一步研究的需要.
科学领域:
- 医疗保健中的人工智能
- 医疗信息学 医疗信息学
- 手术推系统是指手术推系统.
背景情况:
- 大型语言模型 (LLM) 越来越多地被公众用于健康信息.
- 患者寻求人工智能驱动的医疗查询,包括手术转诊.
研究的目的:
- 评估当前LLM在推全膝关节整形术 (TKA) 外科医生的能力.
- 评估LLM生成的外科医生建议的适当性和可靠性.
主要方法:
- 三位LLM (ChatGPT,Gemini,DeepSeek) 被要求在美国四个城市中确定TKA外科医生.
- 搜索评估了外科医生的适当性 (TKA实践,活跃状态) 和联系方式的准确性.
- 分析了LLM的理由和信息来源.
主要成果:
- 总体而言,66%的LLM对TKA外科医生的建议是适当的.
- 双子座 (87%) 和ChatGPT (74%) 的表现优于DeepSeek (36%).
- 只有23%的提供联系方式是准确的,模型的变化很大.
结论:
- 虽然LLM显示出识别TKA外科医生的潜力,但其准确性却存在差异.
- 外科医生专业知识验证和联系信息的可靠性需要改进.
- 需要进一步的研究来了解外科推的LLM推标准.
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