菜单
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.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Integrate AI detection into PACS workflow. Also use when setting up, configuring, or optimizing AI detection systems for medical imaging. Also covers Aidoc, Nvidia Clara, Zebra Medical, MaxQ AI, and Qure AI integration.
QA AI outputs, detect false positives/negatives, and validate AI results. Also use when evaluating AI system performance, reviewing AI-assisted findings, or conducting quality assurance on AI detection and reporting tools.
Guidance for AI-assisted structured reporting tools. Also use when the user mentions AI reporting, automated templating, speech-to-report, or wants to configure or optimize AI-assisted radiology reporting systems (RadAI, Abba, DeepRad).
Use LLM APIs for radiology tasks. Also use when integrating medical LLMs (MedPaLM, MedLM, Google Health, Amazon HealthLake) for report analysis, clinical reasoning, or radiology AI workflows.
Assesses medical image quality against clinical standards and identifies optimization opportunities. Use when user mentions "image quality audit", "artifact review", "dose analysis", "protocol deviation", "quality metrics", "diagnostic adequacy", or "technique optimization".
Retrieves and analyzes operational metrics from radiology information systems. Use when user mentions "radiology KPIs", "productivity metrics", "turnaround time analysis", "workload distribution", or needs operational analytics.
| name | radiology-dataset-guide |
| description | 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. |
task_type (required): ML/AI task being solved
detection - Abnormality/nodule/cancer detectionsegmentation - Organ or lesion segmentationclassification - Disease or finding classificationreconstruction - Image reconstruction/enhancementquantification - Measurement and feature extractionanatomy (optional): Body region or organ systemmodality (optional): Imaging modality preferenceaccess_requirements (optional): Data use restrictionscommercial_use (optional): Boolean for commercial application intent| 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 |
Returns structured JSON with:
task_type: detection
anatomy: lung
modality: CT
task_type: classification
anatomy: chest
commercial_use: true