医疗 AI 终于有了真实场景的语音对话基准——111 小时医患对话数据，覆盖四种疾病，做医疗对话系统的团队可以直接拿来训练和测试模型。Claude Sonnet 4 在句子选择上领先，但所有模型都过度自信，这个发现值得关注。
研究人员发布了 MeDial-Speech，一个包含 111 小时真实医患对话的语音数据集，涵盖路易体痴呆、心力衰竭、肩痛和心绞痛四种疾病。该数据集来自机器人与患者、医生与患者的对话，旨在训练和评估用于医疗咨询的 AI 系统。论文还提出了一个基于句子选择的对话基准，测试了 GPT-5 mini、DeepSeek-V3 和 Claude Sonnet 4 三个大模型。结果显示 Claude Sonnet 4 在句子选择任务中准确率最高（手动转录 71.1%，自动转录 74.7%），但所有模型在概率预测上均过度自信。数据集对非商业用途免费开放。
A Dataset of Robot-Patient and Doctor-Patient Medical Dialogues for Spoken Language Processing Tasks
Large Language Models (LLMs) have brought huge improvements to Artificial Intelligence (AI), which can be applied to general-purpose tasks. However, their application to textual or spoken medical consultations is still an open research problem. This paper proposes MeDial-Speech, a novel speech dataset for training and evaluating Med-AIs that can carry out consultations with patients. It was collected in realistic environments from robot-patient and doctor-patient dialogues, contains 111+ hours of speech data (without data augmentation), and covers four health conditions: Lewy body dementia, heart failure, shoulder pain, and angina. In addition, we propose a dialogue benchmark via sentence selection (with 20 options) to evaluate three state-of-the-art LLMs: GPT-5 mini, DeepSeek-V3, and Claude Sonnet 4. Experimental results reveal that Claude Sonnet 4 is the best in sentence selection, with 71.1% accuracy using manual transcriptions and 74.7% using automatic transcriptions, and that all LLMs are highly overconfident in their probabilistic predictions, regardless of selecting correct or incorrect sentences in medical dialogues. This dataset is free of charge for non-commercial purposes at: https://huggingface.co/datasets/hcuayahu/MeDial-Speech