一个多中心的多功能评估的大型语言模型在纯色音频图解患者的患者
Jun Liang1,2,3,4, Mengyao Xing2, Peng Xiang1,5
1Department of AI and IT, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
NPJ digital medicine
|March 15, 2026
概括
大型语言模型 (LLM) 被评估用于解释听力图报告,帮助患者理解听力损失. 虽然不取代医生,但LLMs显示出作为可访问的听力学信息的工具的希望.
科学领域:
- 听力学和人工智能的人工智能
- 在医疗保健中的自然语言处理.
背景情况:
- 纯色音频图对于听力损失的评估至关重要,但对患者来说技术上是复杂的.
- 缺乏对患者友好的解释,阻碍了对治疗的理解和坚持.
- 对于大语言模型 (LLM) 在解释音频记录报告时没有先前的评估.
研究的目的:
- 评估八个LLM在理解和解释纯色音频记录报告方面的表现.
- 评估LLM的诊断准确性,一般读者的解释性,以及面向患者的建议.
- 探索LLMs作为听力学患者沟通中的辅助工具的潜力.
主要方法:
- 一个盲目的多中心研究,涉及140个音频记录.
- 在诊断,解释和推任务中评估了八种不同的LLM.
- 临床专家和非专业人士进行了评估,以衡量不同的观点.
主要成果:
- 对于听力损失的严重程度 (67.00%) 和类型 (54.00%) ,DeepSeek-V3显示出最高的诊断准确度.
- 对于广大受众来说,R1获得了最好的可读性 (弗莱什-金凯德分级:6.41).
- 在所有评估的LLM中,患者报告了理解和情绪支持的显著改善,双子座2.0闪光/思考表现良好.
结论:
- 目前的通用LLM不能取代医生在听力学中的诊断能力.
- 在翻译复杂的音频录像数据成患者友好的格式方面,LLM显示出作为有价值的辅助工具的潜力.
- 这些人工智能工具可以提高听力医疗信息的可访问性,特别是对于服务不足的人群.
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