在内牙疾病中使用高级大型语言模型的临床决策支持
Jiahe Li1,2, Xian He1,3, Yong Wang4
1State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China College of Stomatology, Sichuan University, Chengdu, China.
Journal of dental sciences
|January 26, 2026
概括
大型语言模型 (LLM) 显示出对内牙决定支持的前景. 克劳德3.5显示出最高的准确性,尽管所有模型都需要精细化,以应用于复杂的临床应用.
科学领域:
- 人工智能在牙科中的应用
- 临床决策支持系统 临床决策支持系统
- 牙周内科研究 牙周内科研究
背景情况:
- 大型语言模型 (LLM) 提供了临床决策支持的潜力.
- 它们在内牙科疾病诊断和治疗规划中的应用在很大程度上是未被探索的.
- 在专门的内牙科环境中评估LLM能力至关重要.
研究的目的:
- 评估三位高级LLM (GPT-4o,Claude 3.5,Grok2) 在牙周内科的决策表现.
- 评估各种内牙科主题的LLM准确性,包括理论,程序和复杂的案例.
- 为了确定目前的内牙实践LLMs的优点和局限性.
主要方法:
- 开发了一个包括421个多项选择题的综合问题库,涵盖27个内牙科主题.
- 问题包括理论知识,程序步骤和35个复杂的临床病例场景.
- 三个LLM (GPT-4o,Claude 3.5,Grok2) 使用标准化提示进行了测试,通过主题分层准确度衡量性能.
主要成果:
- 克劳德3.5获得了最高的整体准确率73.39%,其次是Grok2 (66.27%) 和GPT-4o (46.32%).
- 格罗克2在分析复杂的内牙病例方面表现出卓越的表现,准确率为69.57%.
- 模型在药理学和结构功能等理论领域表现良好,但在复杂的场景和程序技术方面遇到了困难.
结论:
- 作为内牙科决策支持工具,LLM显示出显著的希望.
- 为了有效的临床整合,需要对特定领域进行进一步的细化和验证.
- 在复杂的情况中,LLM可以增强,但不能取代专家内学判断.
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