高质量医学数据资源

cMedQA2 is an updated Chinese community medical question answering dataset for question-answer matching and medical QA research. It is useful for training and evaluating Chinese medical retrieval, ranking, and answer selection models.

MIMIC-CXR is a large deidentified chest radiograph dataset with associated free-text radiology reports. It is widely used for chest X-ray classification, report generation, image-text representation learning, radiology retrieval, and medical multimodal foundation model evaluation.

MIMIC-IV-Note provides deidentified clinical notes linked to MIMIC-IV hospital data. It supports clinical NLP tasks such as note representation learning, discharge summary modeling, information extraction, summarization, and multimodal EHR-text modeling.

CMCQA is a large Chinese medical conversational question-answering dataset released with knowledge-grounded medical dialogue research. It supports medical conversation QA, knowledge-grounded response generation, and evaluation of Chinese medical dialogue systems.

HuatuoGPT2-SFT-GPT4-140K is a Chinese medical supervised fine-tuning dataset containing medical instruction-style conversations and GPT-4-assisted responses. It is useful for Chinese medical assistant alignment and medical LLM instruction tuning.

Huatuo-26M is a large-scale Chinese medical question-answering dataset with about 26 million QA pairs collected for medical language modeling and medical dialogue research. It is suitable for Chinese medical LLM pretraining, fine-tuning, and QA system development.

MedQA is a medical examination question answering benchmark with English and Chinese medical licensing-style question sets, including mainland China and Taiwan variants. It is widely used for medical QA and medical reasoning evaluation.

MedDG is an entity-centric Chinese medical consultation dataset with domain entity annotations for medical dialogue generation. It supports entity-aware response generation, medical consultation modeling, and dialogue systems that ground responses in clinical concepts.

CMB is a comprehensive Chinese medical benchmark for evaluating medical large language models on medical exams, reasoning, and clinical knowledge questions. It is suited for Chinese medical QA, LLM evaluation, and instruction-following assessment.

CBLUE is a Chinese biomedical language understanding benchmark covering real-world biomedical NLP tasks such as named entity recognition, relation extraction, term normalization, clinical trial classification, sentence similarity, and medical question answering. It is useful for evaluating Chinese clinical NLP models and medical language models.

Medical LLM benchmark and leaderboard intended to broaden coverage beyond single medical QA datasets.

Benchmark for evaluating health AI model safety, helpfulness, and clinical-relevance judgments with physician-reviewed rubrics.