大型语言模型和手术决策:面部创伤学实践中生成单模人工智能的评估
Simone Benedetti1, Andrea Frosolini1, Lisa Catarzi1
1Maxillofacial Surgery Unit, Department of Medical Biotechnologies, University of Siena, Siena, Italy.
Journal of maxillofacial and oral surgery
|February 6, 2026
概括
大型语言模型 (LLM) 在协助外科决定方面表现有前途,但需要进一步开发以获得临床可靠性. 人类外科医生在准确性和对面创伤病例的整体有用性方面始终优于人工智能.
科学领域:
- 医疗人工智能 医疗人工智能
- 手术决策支持系统 手术决策支持系统
- 面创伤学 面创伤学
背景情况:
- 大型语言模型 (LLM) 对医疗保健专业人员在诊断和治疗决策中具有潜在的好处.
- 然而,将它们纳入临床工作流程引发了人们对其有用性,可靠性和伦理影响的担忧.
研究的目的:
- 评估LLM,特别是ChatGPT-4和谷歌Bard在面创伤学中管理复杂的手术场景的能力.
- 在现实世界的临床病例模拟中,将LLM与人类面外科住院医生的表现进行比较.
主要方法:
- 一项涉及30个现实世界面创伤病例的横截面研究.
- 这些病例被介绍给了ChatGPT-4,Google Bard和外科住院医生.
- 专家外科医生使用AIPI和QAMAI评估工具对表现进行了评估.
主要成果:
- 聊天GPT-4和巴德在考虑患者特征方面表现出类似的能力,但在差异诊断建议方面有所不同.
- 聊天GPT-4在提出额外的检查和治疗计划方面表现优于巴德.
- 面外科住院医生在所有QAMAI参数 (包括准确性,清晰度和整体实用性) 上始终比LLM获得更高的分数.
结论:
- 在面部创伤学中,LLM显示出作为临床决策支持工具的潜力.
- 需要取得重大进展,以确保LLM在实际临床应用中的可靠性.
- AIPI和QAMAI工具对于评估LLM响应非常有价值,强调需要标准化的评估方法.
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