datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
medical-o1-reasoning-SFT
News
[2025/04/22] We split the data and kept only the medical SFT dataset (medical_o1_sft.json). The file medical_o1_sft_mix.json contains a mix of medical and general instruction data.
[2025/02/22] We released the distilled dataset from Deepseek-R1 based on medical verifiable problems. You can use it to initialize your models with the reasoning chain from Deepseek-R1.
[2024/12/25] We open-sourced the medical reasoning dataset for SFT, built on medical verifiable problems and an… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/medical-o1-reasoning-SFT.Medical-Eval-HumanityLastExammedical-qa-datasets
all-processed dataset is a concatenation of of medical-meadow-* and chatdoctor_healthcaremagic datasets
The Chat Doctor term is replaced by the chatbot term in the chatdoctor_healthcaremagic dataset
Similar to the literature the medical_meadow_cord19 dataset is subsampled to 50,000 samples
truthful-qa-* is a benchmark dataset for evaluating the truthfulness of models in text generation, which is used in Llama 2 paper. Within this dataset, there are 55 and 16 questions related to Health and… See the full description on the dataset page: https://huggingface.co/datasets/lavita/medical-qa-datasets.medical_meadow_medqa
Dataset Card for MedQA
Dataset Summary
This is the data and baseline source code for the paper: Jin, Di, et al. "What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams."
From https://github.com/jind11/MedQA:
The data that contains both the QAs and textbooks can be downloaded from this google drive folder. A bit of details of data are explained as below:
For QAs, we have three sources: US, Mainland of China, and… See the full description on the dataset page: https://huggingface.co/datasets/medalpaca/medical_meadow_medqa.medical_meadow_medical_flashcards
Dataset Card for Medical Flashcards
Dataset Summary
Medicine as a whole encompasses a wide range of subjects that medical students and graduates must master
in order to practice effectively. This includes a deep understanding of basic medical sciences, clinical knowledge,
and clinical skills. The Anki Medical Curriculum flashcards are created and updated by medical students and cover the
entirety of this curriculum, addressing subjects such as anatomy, physiology… See the full description on the dataset page: https://huggingface.co/datasets/medalpaca/medical_meadow_medical_flashcards.MedQuad-MedicalQnADataset
Reference:
"A Question-Entailment Approach to Question Answering". Asma Ben Abacha and Dina Demner-Fushman. BMC Bioinformatics, 2019.
lingshu_training_data_medical_domain
Website
🤖 7B Model
🤖 8B Model based on InternVL3
🤖 32B Model
MedEvalKit
Technical Report
Lingshu MCP
Lingshu Medical MLLM Training Data (Medical Domain)
This dataset contains the medical-domain training data used in the multi-stage training of the Lingshu Medical Multimodal Large Language Model (MLLM). General-domain data has been removed; only medical data is included.
The training… See the full description on the dataset page: https://huggingface.co/datasets/cocool/lingshu_training_data_medical_domain.medical_meadow_wikidoc
Dataset Card for WikiDoc
For the dataset containing patient information from wikidoc refer to this dataset
Dataset Summary
This dataset containes medical question-answer pairs extracted from WikiDoc,
a collaborative platform for medical professionals to share and contribute to up-to-date medical knowledge.
The platform has to main subsites, the "Living Textbook" and "Patient Information". The "Living Textbook"
contains chapters for various medical specialties, which we… See the full description on the dataset page: https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc.ai-medical-chatbot
AI Medical Chatbot Dataset
This is an experimental Dataset designed to run a Medical Chatbot
It contains at least 250k dialogues between a Patient and a Doctor.
Playground ChatBot
ruslanmv/AI-Medical-Chatbot
For furter information visit the project here:
https://github.com/ruslanmv/ai-medical-chatbot
aya-mm-exams-spanish-medicalMedical Spanish Exams for the Multimodal Aya Exams Projects.
Questions available in file: data.json
Images stored in: /images
Original data and file available here: link
medical_meadow_wikidoc_patient_information
Dataset Card for WikiDoc
For the dataset containing rephrased content from the living textbook refer to this dataset
Dataset Summary
This dataset containes medical question-answer pairs extracted from WikiDoc,
a collaborative platform for medical professionals to share and contribute to up-to-date medical knowledge.
The platform has to main subsites, the "Living Textbook" and "Patient Information". The "Living Textbook"
contains chapters for various medical specialties… See the full description on the dataset page: https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc_patient_information.SlideChat
Introduction
This repository provides the dataset resources used for training and evaluating SlideChat, a multimodal large language model for whole-slide pathology image understanding.
The dataset includes both instruction-following training data and VQA/Caption evaluation benchmarks across multiple pathology cohorts and tasks.
Contents
Training Instruction Data
SlideInstruct_train_stage1_caption.json: Slide-level caption instruction data used for Stage-1… See the full description on the dataset page: https://huggingface.co/datasets/General-Medical-AI/SlideChat.medicaid-explorer-datalingshu_training_data_medical_domain
Website
🤖 7B Model
🤖 8B Model based on InternVL3
🤖 32B Model
MedEvalKit
Technical Report
Lingshu MCP
Lingshu Medical MLLM Training Data (Medical Domain)
This dataset contains the medical-domain training data used in the multi-stage training of the Lingshu Medical Multimodal Large Language Model (MLLM). General-domain data has been removed; only medical data is included.
The training… See the full description on the dataset page: https://huggingface.co/datasets/lingshu-medical-mllm/lingshu_training_data_medical_domain.II-Medical-Reasoning-SFT
II-Medical-Reasoning-SFT
II-Medical SFT is a curated dataset designed to support the supervised fine-tuning of large language models (LLMs) for medical reasoning tasks. It comprises multi-turn dialogues, clinical case scenarios, and question-answer pairs that reflect the complex reasoning processes encountered in real-world clinical practice.
The dataset is intended to help models develop key competencies such as differential diagnosis, evidence-based decision-making, patient… See the full description on the dataset page: https://huggingface.co/datasets/Intelligent-Internet/II-Medical-Reasoning-SFT.medical_meadow_health_advice
Health Advice
Dataset Summary
This is the dataset use in the paper: Detecting Causal Language Use in Science Findings.
It was cleaned and formated to fit into the alpaca template.
Citation Information
@inproceedings{yu-etal-2019-detecting,
title = "Detecting Causal Language Use in Science Findings",
author = "Yu, Bei and
Li, Yingya and
Wang, Jun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural… See the full description on the dataset page: https://huggingface.co/datasets/medalpaca/medical_meadow_health_advice.GMAI-VL-5.5M
GMAI-VL-5.5M Dataset
GMAI-VL-5.5M is a comprehensive, large-scale medical General Medical AI Vision-Language (GMAI-VL) dataset built specifically for training multimodal foundation models in the medical domain. It contains an extraordinary scale of high-quality instructions encompassing over 5.5 million multimodal question-answering pairs, carefully constructed based on hundreds of medical classification, segmentation, and detection datasets.
This repository… See the full description on the dataset page: https://huggingface.co/datasets/General-Medical-AI/GMAI-VL-5.5M.Medical-Commons
Medical-Commons
Medical-Commons is the largest dataset of medical content under free licenses or open data program collected by Pleias.
It includes three different collection:
International scientific collection of 2M articles from OpenAlex.
French scientific collection of XM articles, reports and PhD theses from French institutional repositories.
Administration collection from health and medical agencies, for now limited to France but with a planned Europe-wide expansion.
The… See the full description on the dataset page: https://huggingface.co/datasets/PleIAs/Medical-Commons.africa-synth-aid-flows-medical-multimodal-fracture-all
Africa Synth Aid Flows Medical Multimodal Fracture All | Africa (Electric Sheep Africa metadata inventory)
Size category: 1K<n<10K - Formats: json - Sector: health - Engineered by Electric Sheep Africa
TL;DR
This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.
What This Dataset Covers
Health… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-aid-flows-medical-multimodal-fracture-all.mmlu_professional_medicinemedical纯文本数据，中文医疗数据集，包含预训练数据的百科数据，指令微调数据和奖励模型数据。medical_meadow_mediqa
MediQA
Dataset Description
MEDIQA is a dataset of manually generated, question-driven summaries of multi and single document answers to consumer health questions.
Homepage: https://osf.io/fyg46/?view_only=
Citation Information
@article{savery2020question,
title={Question-driven summarization of answers to consumer health questions},
author={Savery, Max and Abacha, Asma Ben and Gayen, Soumya and Demner-Fushman, Dina},
journal={Scientific Data}… See the full description on the dataset page: https://huggingface.co/datasets/medalpaca/medical_meadow_mediqa.IndustryCorpus2_medicine_health_psychology_traditional_chinese_medicine
IndustryCorpus2: Health & Medicine
This repository contains the IndustryCorpus2: Health & Medicine domain subset of BAAI/IndustryCorpus2.
Refer to the parent dataset card for data construction, intended use, limitations,
and licensing details.
Citation
If you use this dataset in your work, please cite IndustryCorpus2:
@misc{shi2024industrycorpus2,
title = {IndustryCorpus2},
author = {Xiaofeng Shi and Lulu Zhao and Hua Zhou and Donglin Hao},
year… See the full description on the dataset page: https://huggingface.co/datasets/BAAI/IndustryCorpus2_medicine_health_psychology_traditional_chinese_medicine.mmlu_college_medicineJapanese-Medical-VQA-12m
Japanese Medical VQA 12M
Japanese Medical VQA 12M is a large-scale Japanese medical multimodal dataset built from Open-PMC-18M and released in Parquet and Webdataset format.
This dataset contains outputs from multiple data-construction stages, including:
source captions
Japanese translations of source captions
enriched captions
Japanese translations of enriched captions
question-answering
Current Repository Format
This repository currently stores the dataset in… See the full description on the dataset page: https://huggingface.co/datasets/MIL-UT/Japanese-Medical-VQA-12m.RexCT_npymedical_questions_pairs
Dataset Card for [medical_questions_pairs]
Dataset Summary
This dataset consists of 3048 similar and dissimilar medical question pairs hand-generated and labeled by Curai's doctors. Doctors with a list of 1524 patient-asked questions randomly sampled from the publicly available crawl of HealthTap. Each question results in one similar and one different pair through the following instructions provided to the labelers:
Rewrite the original question in a different way while… See the full description on the dataset page: https://huggingface.co/datasets/curaihealth/medical_questions_pairs.MedicalTextbookMedical-Reasoning-SFT-Mega
Medical-Reasoning-SFT-Mega
The ultimate medical reasoning dataset - combining 7 state-of-the-art AI models with fair distribution deduplication. 1.79 million unique samples with 3.78 billion tokens of medical chain-of-thought reasoning.
Dataset Overview
Metric
Value
Total Samples
1,789,998 (after deduplication)
Total Tokens
~3.78 Billion
Content Tokens
~2.22 Billion
Reasoning Tokens
~1.56 Billion
Samples with Reasoning
1,789,764 (100.0%)
Unique… See the full description on the dataset page: https://huggingface.co/datasets/OpenMed/Medical-Reasoning-SFT-Mega.us_medical_license_exam_textbooks_en