datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.mental_health_counseling_conversations
Amod/mental_health_counseling_conversations
This dataset is a compilation of high-quality, real one-on-one mental health counseling conversations between individuals and licensed professionals. Each exchange is structured as a clear question–answer pair, making it directly suitable for fine-tuning or instruction-tuning language models that need to handle sensitive, empathetic, and contextually aware dialogue.
Since its public release in 2023, it has been downloaded over 100,000… See the full description on the dataset page: https://huggingface.co/datasets/Amod/mental_health_counseling_conversations.VL-Health
VL-Health Dataset
Overview
The VL-Health dataset is designed for multi-stage training of unified LVLMs in the medical domain. It consists of two key phases:
Alignment – Focused on training image captioning capabilities and learning representations of input visual information.
Instruct Fine-Tuning – Designed for enhancing the model's ability to handle various vision-language tasks, including both visual comprehension and visual generation tasks.
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/lintw/VL-Health.healthqa-br
HealthQA-BR
Resumo
O HealthQA-BR é o primeiro benchmark de larga escala e abrangência para todo o Sistema Único de Saúde (SUS), projetado para medir o conhecimento clínico de Grandes Modelos de Linguagem (LLMs) frente aos desafios da saúde pública brasileira. Composto por 5.632 questões de múltipla escolha, o conjunto de dados é derivado de provas e concursos de licenciamento profissional e residência de abrangência nacional e de alto impacto no Brasil.
Diferentemente de… See the full description on the dataset page: https://huggingface.co/datasets/Larxel/healthqa-br.healthsearchqa
HealthSearchQA
Dataset of consumer health questions released by Google for the Med-PaLM paper (arXiv preprint).
From the paper:
We curated our own additional dataset consisting of 3,173 commonly searched consumer questions,
referred to as HealthSearchQA. The dataset was curated using seed medical conditions and their
associated symptoms. We used the seed data to retrieve publicly-available commonly searched questions
generated by a search engine, which were displayed to all users… See the full description on the dataset page: https://huggingface.co/datasets/katielink/healthsearchqa.Ethical-Reasoning-in-Mental-Health-v1This repository contains the dataset for the paper EthicsMH: A Pilot Benchmark for Ethical Reasoning in Mental Health AI.
Overview
Ethical-Reasoning-in-Mental-Health-v1 (EthicsMH) is a carefully curated dataset focused on ethical decision-making scenarios in mental health contexts.This dataset captures the complexity of real-world dilemmas faced by therapists, psychiatrists, and AI systems when navigating critical issues such as confidentiality, autonomy, and bias.
Each sample… See the full description on the dataset page: https://huggingface.co/datasets/UVSKKR/Ethical-Reasoning-in-Mental-Health-v1.Health_Benchmarks
LLM Health Benchmarks Dataset by Yesil Science
The LLM Health Benchmarks Dataset is a specialized resource for evaluating large language models (LLMs) in different medical specialties. It provides structured question-answer pairs designed to test the performance of AI models in understanding and generating domain-specific knowledge.
Primary Purpose
This dataset is built to:
Benchmark LLMs in medical specialties and subfields.
Assess the accuracy and contextual… See the full description on the dataset page: https://huggingface.co/datasets/yesilhealth/Health_Benchmarks.HealthChat-11K
HealthChat-11K
This repository contains HealthChat-11K, a curated dataset of approximately 11,000 real-world conversations, composed of 25,000 user messages, where users seek healthcare information from Large Language Models (LLMs). The goal of this work is to provide a high-quality resource for systematically studying and improving health conversations involving humans and AI (e.g., LLMs).
The dataset was presented in the paper: "What's Up, Doc?": Analyzing How Users Seek Health… See the full description on the dataset page: https://huggingface.co/datasets/yahskapar/HealthChat-11K.Nepali-HealthChatevipedia-reviews
Evipedia Evidence Reviews
The full public catalogue of evipedia.ai — a
continuously-updated encyclopedia of evidence reviews on health & longevity
interventions — as one record per review. Each record carries the review's
metadata plus its complete Markdown body.
Homepage / source: https://evipedia.ai
Live file: https://evipedia.ai/evipedia-corpus.jsonl (this dataset mirrors it)
Publisher: Forever Healthy
License: CC BY 4.0
What's inside
One JSON object per… See the full description on the dataset page: https://huggingface.co/datasets/forever-healthy/evipedia-reviews.HealMed
HealMed (Human-verified Evaluation Across Languages for Medical AI) is a multilingual medical dataset featuring expert-verified translations for benchmarking multilingual medical AI systems.
The dataset comprises translations from two complementary sources. A portion is based on the multilingual translations released by the GlobMed project (arXiv: 2601.02186), while the remainder was generated by our team using zero-shot machine translation to expand language coverage. Each translated… See the full description on the dataset page: https://huggingface.co/datasets/li-lab/HealMed.IndustryInstruction_Health-Medicine
IndustryInstruction: Health & Medicine
This repository contains the IndustryInstruction: Health & Medicine domain subset of BAAI/IndustryInstruction.
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 IndustryInstruction:
@misc{shi2024industryinstruction,
title = {IndustryInstruction},
author = {Xiaofeng Shi and Lulu Zhao and Hua Zhou and… See the full description on the dataset page: https://huggingface.co/datasets/BAAI/IndustryInstruction_Health-Medicine.OpenMathReasoning
OpenMathReasoning
OpenMathReasoning is a large-scale math reasoning dataset for training large language models (LLMs).
This dataset contains
306K unique mathematical problems sourced from AoPS forums with:
3.2M long chain-of-thought (CoT) solutions
1.7M long tool-integrated reasoning (TIR) solutions
566K samples that select the most promising solution out of many candidates (GenSelect)
Additional 193K problems sourced from AoPS forums (problems only, no solutions)
We used… See the full description on the dataset page: https://huggingface.co/datasets/healthlifestyle/OpenMathReasoning.HealthCareMagic-DistilledThis dataset undergone:
Comprehensive data augmentation pipeline,
Soft/hard label distillation from MedGemma-27B-Text-IT
Shifaa_Arabic_Mental_Health_Consultations
🏥 Shifaa Arabic Mental Health Consultations 🧠
📌 Overview
Shifaa Arabic Mental Health Consultations is a high-quality dataset designed to advance Arabic medical language models.This dataset provides 35,648 real-world medical consultations, covering a wide range of mental health concerns.
📊 Dataset Summary
Size: 35,648 consultations
Main Specializations: 7
Specific Diagnoses: 123
Languages: Arabic (العربية)
Why This Dataset?
🔹 Lack of… See the full description on the dataset page: https://huggingface.co/datasets/Ahmed-Selem/Shifaa_Arabic_Mental_Health_Consultations.Health-Bench-Eval-OSS-2025-07
Dataset Card for HealthBench
Dataset Summary
HealthBench is a benchmark dataset developed by OpenAI in collaboration with 262 physicians from 60 countries to evaluate AI systems in health-related conversational scenarios. It contains 5,000 multi-turn health conversations in a JSONL file (2025-05-07-06-14-12_oss_eval.jsonl), simulating interactions between AI models and users (laypersons or clinicians). Each conversation includes a user prompt, a candidate model response… See the full description on the dataset page: https://huggingface.co/datasets/Tonic/Health-Bench-Eval-OSS-2025-07.Mental-Health-Conversations
Dataset Card
This dataset consists of around 99k rows of mental health conversations. It is a cleaned version of "jerryjalapeno/nart-100k-synthetic".
Source
jerryjalapeno/nart-100k-synthetic
chatdoctor-healthcaremagic-112k-vi
🩺 ChatDoctor HealthCareMagic 112k (Vietnamese Translated)
Tập dữ liệu hỏi đáp y khoa ChatDoctor HealthCareMagic 112k được dịch sang tiếng Việt chất lượng cao, phục vụ fine-tune các mô hình ngôn ngữ lớn (LLM) trong lĩnh vực y tế, chăm sóc sức khỏe và tư vấn y khoa tổng quát.
📌 Tổng quan dữ liệu
Quy mô: 112,165 cặp hỏi - đáp y tế thực tế giữa bệnh nhân và bác sĩ.
Phân chia:
train: 106,556 mẫu (95%)
validation: 5,609 mẫu (5%)
Định dạng: Chuẩn Alpaca /… See the full description on the dataset page: https://huggingface.co/datasets/NoirHuy/chatdoctor-healthcaremagic-112k-vi.omnimcp_healthtech_medops_teaser
🔬 INSPECT THE DEEPSEEK-R1 REASONING CHAIN LIVE:
Zero hallucinations. Null syntax errors. 100% AST compiler validated.🌐 Live Interactive Reasoning & Code Inspector: https://emgena.com/trainingslager🎁 Claim your Free Starter Kit (Code: STARTER100): https://emgena.com/trainingslager🏷️ Launch Discount: Get 20 € OFF any 500-incident production suite with code LAUNCH20!
📜 Enterprise Compliance: EU AI Act Articles 50 & 53 certified • 100% DSGVO / GDPR clean • Commercial EULA… See the full description on the dataset page: https://huggingface.co/datasets/emgena/omnimcp_healthtech_medops_teaser.vietnamese_health_dataset
Team and Homepage
Official Website: https://aienthusiasm.vn
Hugging Face Organization: https://huggingface.co/ai-enthusiasm-community
Contact
If you encounter any issues with the dataset or have any inquiries, please feel free to reach out to us via email at: aienthusiasm.team@gmail.com
Dataset Structure
The dataset is provided in a flattened tabular format, optimized for the Hugging Face Dataset Viewer and high-speed Parquet processing.… See the full description on the dataset page: https://huggingface.co/datasets/ai-enthusiasm-community/vietnamese_health_dataset.synthetic-mental-health-convos
Synthetic Mental Health SFT Dataset
Dataset Summary
This dataset contains high-fidelity, synthetic patient-therapist dialogues designed for Supervised Fine-Tuning (SFT) of Large Language Models (LLMs) in the domain of mental health.
The primary goal of this dataset is to train AI assistants to transition from "general knowledge" models to empathetic, supportive, and safety-conscious mental health companions. The dialogues cover a wide spectrum of mental health conditions… See the full description on the dataset page: https://huggingface.co/datasets/hllzmz/synthetic-mental-health-convos.HealthCareMagicThis dataset undergone a comprehensive data augmentation pipeline.
omnimcp_healthtech_hipaa_redactor_teaser
🔬 INSPECT THE DEEPSEEK-R1 REASONING CHAIN LIVE:
Zero hallucinations. Null syntax errors. 100% AST compiler validated.🌐 Live Interactive Reasoning & Code Inspector: https://emgena.com/trainingslager🎁 Claim your Free Starter Kit (Code: STARTER100): https://emgena.com/trainingslager🏷️ Launch Discount: Get 20 € OFF any 500-incident production suite with code LAUNCH20!
📜 Enterprise Compliance: EU AI Act Articles 50 & 53 certified • 100% DSGVO / GDPR clean • Commercial EULA… See the full description on the dataset page: https://huggingface.co/datasets/emgena/omnimcp_healthtech_hipaa_redactor_teaser.omnimcp_healthtech_hl7_parser_teaser
🔬 INSPECT THE DEEPSEEK-R1 REASONING CHAIN LIVE:
Zero hallucinations. Null syntax errors. 100% AST compiler validated.🌐 Live Interactive Reasoning & Code Inspector: https://emgena.com/trainingslager🎁 Claim your Free Starter Kit (Code: STARTER100): https://emgena.com/trainingslager🏷️ Launch Discount: Get 20 € OFF any 500-incident production suite with code LAUNCH20!
📜 Enterprise Compliance: EU AI Act Articles 50 & 53 certified • 100% DSGVO / GDPR clean • Commercial EULA… See the full description on the dataset page: https://huggingface.co/datasets/emgena/omnimcp_healthtech_hl7_parser_teaser.public-health-news-DF-qa
Public Health Brasília QA Corpus
Dataset Summary
Public Health Brasília QA Corpus is a dataset for evaluating Retrieval-Augmented Generation (RAG) systems over public health news articles from the Secretaria de Saúde do Distrito Federal (SES-DF), Brazil. It consists of two components: a QA evaluation corpus with location-aware question-answer pairs, and a knowledge base corpus of 1,688 public health news articles that serves as the retrieval source for the RAG… See the full description on the dataset page: https://huggingface.co/datasets/gvic-unb/public-health-news-DF-qa.omnimcp_healthtech_fhir_validator_teaser
🔬 INSPECT THE DEEPSEEK-R1 REASONING CHAIN LIVE:
Zero hallucinations. Null syntax errors. 100% AST compiler validated.🌐 Live Interactive Reasoning & Code Inspector: https://emgena.com/trainingslager🎁 Claim your Free Starter Kit (Code: STARTER100): https://emgena.com/trainingslager🏷️ Launch Discount: Get 20 € OFF any 500-incident production suite with code LAUNCH20!
📜 Enterprise Compliance: EU AI Act Articles 50 & 53 certified • 100% DSGVO / GDPR clean • Commercial EULA… See the full description on the dataset page: https://huggingface.co/datasets/emgena/omnimcp_healthtech_fhir_validator_teaser.omnimcp_healthtech_cohort_query_teaser
🔬 INSPECT THE DEEPSEEK-R1 REASONING CHAIN LIVE:
Zero hallucinations. Null syntax errors. 100% AST compiler validated.🌐 Live Interactive Reasoning & Code Inspector: https://emgena.com/trainingslager🎁 Claim your Free Starter Kit (Code: STARTER100): https://emgena.com/trainingslager🏷️ Launch Discount: Get 20 € OFF any 500-incident production suite with code LAUNCH20!
📜 Enterprise Compliance: EU AI Act Articles 50 & 53 certified • 100% DSGVO / GDPR clean • Commercial EULA… See the full description on the dataset page: https://huggingface.co/datasets/emgena/omnimcp_healthtech_cohort_query_teaser.healthcare-minor-consultation
This Dialogue
Comprised of fictitious examples of dialogues between a doctor and a patient during a minor medical consultation.. Check out the example below:
"id": 1,
"description": "Discussion about a common cold",
"dialogue": "Patient: Doctor, I've been feeling congested and have a runny nose. What can I do to relieve these symptoms?\n\nDoctor: It sounds like you have a common cold. You can try over-the-counter decongestants to relieve congestion and saline nasal sprays to help… See the full description on the dataset page: https://huggingface.co/datasets/FunDialogues/healthcare-minor-consultation.mental_health_counseling_conversations
Amod/mental_health_counseling_conversations
This data is cloned from https://huggingface.co/datasets/Amod/mental_health_counseling_conversations
Dataset Summary
This dataset is a collection of questions and answers sourced from two online counseling and therapy platforms. The questions cover a wide range of mental health topics, and the answers are provided by qualified psychologists. The dataset is intended to be used for fine-tuning language models to improve their… See the full description on the dataset page: https://huggingface.co/datasets/MaggiePai/mental_health_counseling_conversations.autoscientist-healthcare-reasoning
🩺 Adapted Healthcare Clinical-Reasoning (AutoScientist)
Built with Adaptive Data by Adaption.
A grounded, safety-blueprinted clinical-reasoning dataset — and a rigorous,
fully-reproducible study of when data adaptation helps a small model, and when it doesn't.
📈 Adaptive Data quality
Before → After
Overall quality score
7.0 → 9.1 (+30%)
Quality grade
B → A
Completion quality
+37.9%
Message quality
+17.6%
Percentile vs. reference corpus
15.3 → 33.0… See the full description on the dataset page: https://huggingface.co/datasets/hetanshwaghela/autoscientist-healthcare-reasoning.