搜索医学 AI 论文与资源

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.

AMOS is an abdominal multi-organ segmentation benchmark with CT and MRI cases for evaluating versatile medical image segmentation models. It supports abdominal organ segmentation, modality-general segmentation, and benchmarking of robust 3D segmentation methods.

UK Biobank Imaging provides large-scale imaging phenotypes linked to genetic, lifestyle, and health outcome data. It is used for population-scale medical imaging AI, disease risk prediction, representation learning, multimodal biomedical modeling, and epidemiological AI studies.

The Cancer Genome Atlas is a large cancer genomics resource with molecular, clinical, and pathology-related data across many cancer types. It is a foundation dataset for oncology AI, survival prediction, subtype discovery, multimodal cancer modeling, and translational biomarker research.

ADNI provides longitudinal neuroimaging, biomarker, clinical, and cognitive data for Alzheimer disease research. It supports disease progression modeling, dementia diagnosis, multimodal prediction, biomarker discovery, and clinical translation studies.

MedMNIST v2 is a standardized collection of lightweight biomedical image classification datasets, including 2D and 3D tasks. It is useful for quick benchmarking, AutoML, foundation model sanity checks, and reproducible evaluation across multiple medical imaging domains.

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 is a large deidentified electronic health record dataset from Beth Israel Deaconess Medical Center, covering hospital and ICU data for critical care research. It is a core benchmark source for clinical prediction, temporal EHR modeling, phenotyping, and healthcare AI method development.

SLAKE is a semantically labeled medical visual question answering dataset with bilingual English-Chinese questions, medical images, and knowledge-enhanced annotations. It is useful for medical multimodal learning, image-grounded QA, and radiology VQA evaluation.

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.

Legacy multi-task biomedical image segmentation benchmark retained as a reference; newer segmentation benchmarks are listed above it.

JPG-formatted chest radiographs with labels derived from free-text reports, hosted by PhysioNet.

Deidentified EHR data for ICU and hospital patients at Beth Israel Deaconess Medical Center, distributed through PhysioNet with credentialed access.

Foundation model and toolkit for all-in-one biomedical image parsing across recognition, detection, and segmentation tasks.

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.

Google Health AI Developer Foundations open model resources for medical text and medical image understanding, including MedGemma 1.5 resources.

Interactive medical image segmentation benchmark and baseline from CVPR 2025, covering multiple modalities, organs, and target structures.

Multimodal clinical data foundation and benchmark introduced at ICML 2025 for clinical foundation model research.