评估大型语言模型在口腔疾病自我诊断方面的能力
Shiyang Zhuang1,2,3, Yuanhao Zeng4, Shaojunjie Lin3
1Department of Stomatology, the First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China.
iScience
|January 6, 2025
概括
大型语言模型 (LLM) 在牙诊断中显示出有前途的情况,如牙和口腔癌. 虽然性能因模型和语言而异,但随着进一步发展,LLM可以成为初级牙科护理中宝贵的工具.
科学领域:
- 牙科 牙科是指牙科的专业.
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 大型语言模型 (LLM) 越来越多地被用于医疗保健应用.
- 它们在初级牙科护理中的诊断实用性仍在调查中.
研究的目的:
- 评估各种LLM常见口腔疾病的诊断能力.
- 评估语言对LLM诊断绩效的影响.
主要方法:
- 对多个LLM进行比较分析 (ChatGPT 3.5,ChatGPT 4.0,克劳德 2,拉玛 2,克劳德 3.5 索内特).
- 对各种疾病的诊断准确性进行评估,包括关节疾病,牙周病,牙损伤,缺陷,胸膜炎,牙周炎和口腔癌.
- 基于提示结构和语言 (英语与中文) 的性能变化评估.
主要成果:
- 所有评估的LLM都显示出对关节疾病,牙周病,牙损伤和缺陷的诊断能力.
- 当使用中文 (0%至61.7%) 时,ChatGPT 3.5对脉的性能显著改善.
- 中国语的ChatGPT 4.0在诊断脉 (0%至92%) 和心膜炎 (8%至72%) 中显著改善.
- 克劳德2在脉炎 (36%) 中取得了最高的准确性,ChatGPT 4.0在腹膜炎中,Llama 2/克劳德 3.5在口腔癌中取得了最高的准确性.
结论:
- 在初级牙科护理中,LLM显示出作为诊断各种口腔疾病的补充工具的潜力.
- 性能受特定的LLM,被诊断的条件和使用的语言的影响.
- 需要进一步改进和更新,以优化LLM在日常牙科实践中的实用性.
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