How do I install the Radiology Dataset Guide skill?

Run npx skillmds@latest add aizech/radiology-dataset-guide in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Radiology Dataset Guide safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Radiology Dataset Guide?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Radiology Dataset Guide free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Radiology Dataset Guide?

aizech (@aizech) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Radiology Dataset Guide by aizech — Agent Skill

Name: Radiology Dataset Guide
Author: aizech

Radiology Dataset Guide

Guides researchers and developers through radiology dataset selection, access, and utilization for AI development. Use when user mentions "radiology dataset", "medical imaging data", "RSNA challenge", "MIMIC data access", or needs dataset guidance.

aizech 66687ec 2 files · 5.5 KB Updated Sep 17, 2026

File contents

Radiology Dataset Guide Skill

Triggers

"radiology dataset"
"medical imaging data"
"RSNA challenge"
"MIMIC data access"
"CheXpert download"
"dataset comparison"
"training data preparation"
"public dataset"

Parameters

task_type (required): ML/AI task being solved
- detection - Abnormality/nodule/cancer detection
- segmentation - Organ or lesion segmentation
- classification - Disease or finding classification
- reconstruction - Image reconstruction/enhancement
- quantification - Measurement and feature extraction
anatomy (optional): Body region or organ system
modality (optional): Imaging modality preference
access_requirements (optional): Data use restrictions
commercial_use (optional): Boolean for commercial application intent

Dataset Inventory

Dataset	Modality	Primary Task	Access	Annotations
RSNA Bone Age	X-ray	Regression	Public	Age, quality
RSNA Pneumonia	Chest X-ray	Detection	Public	Bounding boxes
RSNA Brain Hemorrhage	CT	Detection	Public	Bounding boxes, type
NIH ChestX-ray14	Chest X-ray	Classification	Public	Labels
CheXpert	Chest X-ray	Classification	Institutional	Labels
MIMIC-CXR	Chest X-ray	Multi	PhysioNet	Labels, reports
CheXphoto	Chest X-ray	Classification	Public	Synth/real pairs
LUNA16	CT	Detection	Public	Nodule centers
KiTS	CT	Segmentation	Public	Kidney/tumor
BraTS	MRI	Segmentation	Research	Multi-modal seg
PANDA	Histology	Classification	Public	Biopsy grades
OBJ-CXR	Chest X-ray	Detection	Public	Bounding boxes

Output Format

Returns structured JSON with:

Relevant datasets ranked by suitability
Annotation quality and completeness
Access procedure and requirements
Key publications and benchmarks
Preprocessing recommendations
Compliance and ethics considerations

Usage Examples

task_type: detection
anatomy: lung
modality: CT

task_type: classification
anatomy: chest
commercial_use: true

---name: radiology-dataset-guidedescription: Guides researchers and developers through radiology dataset selection, access, and utilization for AI development. Use when user mentions "radiology dataset", "medical imaging data", "RSNA challenge", "MIMIC data access", or needs dataset guidance.---# Radiology Dataset Guide Skill## Triggers- "radiology dataset"- "medical imaging data"- "RSNA challenge"- "MIMIC data access"- "CheXpert download"- "dataset comparison"- "training data preparation"- "public dataset"## Parameters- `task_type` (required): ML/AI task being solved - `detection` - Abnormality/nodule/cancer detection - `segmentation` - Organ or lesion segmentation - `classification` - Disease or finding classification - `reconstruction` - Image reconstruction/enhancement - `quantification` - Measurement and feature extraction- `anatomy` (optional): Body region or organ system- `modality` (optional): Imaging modality preference- `access_requirements` (optional): Data use restrictions- `commercial_use` (optional): Boolean for commercial application intent## Dataset Inventory| Dataset | Modality | Primary Task | Access | Annotations ||---------|----------|---------------|--------|-------------|| RSNA Bone Age | X-ray | Regression | Public | Age, quality || RSNA Pneumonia | Chest X-ray | Detection | Public | Bounding boxes || RSNA Brain Hemorrhage | CT | Detection | Public | Bounding boxes, type || NIH ChestX-ray14 | Chest X-ray | Classification | Public | Labels || CheXpert | Chest X-ray | Classification | Institutional | Labels || MIMIC-CXR | Chest X-ray | Multi | PhysioNet | Labels, reports || CheXphoto | Chest X-ray | Classification | Public | Synth/real pairs || LUNA16 | CT | Detection | Public | Nodule centers || KiTS | CT | Segmentation | Public | Kidney/tumor || BraTS | MRI | Segmentation | Research | Multi-modal seg || PANDA | Histology | Classification | Public | Biopsy grades || OBJ-CXR | Chest X-ray | Detection | Public | Bounding boxes |## Output FormatReturns structured JSON with:- Relevant datasets ranked by suitability- Annotation quality and completeness- Access procedure and requirements- Key publications and benchmarks- Preprocessing recommendations- Compliance and ethics considerations## Usage Examples```task_type: detectionanatomy: lungmodality: CTtask_type: classificationanatomy: chestcommercial_use: true```

Frequently asked questions