# openmedlab/Awesome-Medical-Dataset

Collection of awesome medical dataset resources.

Repository: https://github.com/openmedlab/Awesome-Medical-Dataset
Canonical: https://ross.abutalabs.com/products/awesome-medical-dataset
License Family: other
Last push: 2025-01-23T03:32:22+00:00

## Health v2 (maintenance only)
Score: 27/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 3, release rhythm 35, longevity 68
- inputs: {"age_days": 957, "days_push": 587, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2095, forks 202 (observed 2026-08-28T04:06:13.181598+00:00)

## What it is
A curated awesome-list cataloging publicly available medical datasets, covering medical imaging (CT, MRI, endoscopy, retina, skin, microscopy), image-text pairs, and electronic health record text data, organized by anatomical region. It also links to dataset platforms, evaluation benchmarks, related SOTA methods, and foundation toolbox projects for medical AI research.

## Use cases
- find public medical imaging datasets for training models
- find CT and MRI segmentation datasets with organ annotations
- locate electronic health record datasets for healthcare research
- benchmark datasets for medical image segmentation challenges
- curated list of medical NLP and image-text datasets
- datasets for radiology, pathology, or ophthalmology research
- where to find openly available medical datasets for machine learning

## When to choose
- you want a single curated index of medical datasets browsable by anatomy or modality
- you are surveying available public data before starting a healthcare ML project
- you need links to official dataset websites, release dates, and challenge pages

## When to avoid
- you need a tool to download, host, or manage datasets rather than a link collection
- you need the actual medical data itself, which lives in external repositories
- you need executable code or pipelines for data preprocessing

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation, data-science, machine-learning
- domain: healthcare, awesome-lists, machine-learning, data-science, computer-vision
- platform: -
- tags: awesome-list, medical-imaging, datasets, electronic-health-records, segmentation, open-data, benchmarks, curated-list, natural-language-processing

## Member repositories
- openmedlab/Awesome-Medical-Dataset (main) score 27

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:13.181598+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:55:24.122018+00:00, confidence not recorded.
  - readme: https://github.com/openmedlab/Awesome-Medical-Dataset (fetched 2026-08-28T04:06:13.181598+00:00, sha b8023a581e7b)
- Data as of 2026-08-30T08:39:29.467469+00:00.