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科学领域:

  • 牙科诊断 牙科诊断 牙科诊断
  • 医学中的人工智能
  • 牙周内科医院 牙周内科医院 牙周内科医院

背景情况:

  • 生成型大语言模型 (LLM) 越来越多地用于牙科.
  • 基于临床指南的LLMs的诊断准确性和可重复性尚未得到充分证实.
  • 美国内脏病学会 (AAE) 和欧洲内脏病学会 (ESE) 的权威立场声明提供了基于证据的标准来评估内脏病学LLM绩效.

研究的目的:

  • 评估各种生成型大语言模型 (LLM) 的诊断准确性和可重复性.
  • 将LLM的绩效与AAE和ESE的既定内牙指南进行比较.
  • 识别LLM成果的变化及其与内牙科最佳实践的协调.

主要方法:

  • 这项研究遵循了TRIPOD-LLM指南,评估了11个LLM,包括ChatGPT,Gemini,Claude,Perplexity和DeepSeek模型.
  • 从AAE和ESE的立场陈述中提取的60个多选题,在每一个LLM中进行了五轮的管理,产生了3300个答案.
  • 主要结局是完全正确的准确性;次要结局是模型内部的一致性,使用千平方测试和邦费罗尼调整进行分析.

主要成果:

  • 在11个LLM中观察到所有正确度的显著变化 (χ2 = 50.56,df = 10,p < 0.001).
  • 聊天GPT 4o和Claude Opus 4获得了最高准确率的95.0%,而DeepSeek的表现最低,为63.3%.
  • 在大多数模型中,模型内部一致性通常超过90%,尽管DeepSeek的一致性较低,为75.0%.

结论:

  • 大多数评估的LLM在与AAE和ESE内牙指南进行基准比较时,显示出高的诊断准确性和可重复性.
  • 尽管取得了进步,但性能变化和潜在的自信错误输出强调了严格验证的必要性.
  • 专家监督对于LLMs在内牙科的安全和有效临床整合至关重要.