GMAI-Reasoning10K is a high-quality medical image reasoning dataset containing 10,000 carefully selected samples. The data was collected from 95 medical datasets from sources such as Kaggle, GrandChallenge, and Open-Release, covering 12 imaging modalities including X-ray, CT, and MRI. It was created by General-Medical-AI and last updated on July 21, III.
Use Cases
- Train medical visual question answering models based on the described image reasoning tasks.
- Benchmark multimodal medical AI reasoning capabilities based on the 12 imaging modalities.
- Fine-tune large language models for clinical reasoning based on the dataset's structured medical queries.
- Develop educational tools for medical image interpretation based on the standardized reasoning samples.
Strengths
- Contains 10,000 high-quality, carefully selected samples.
- Aggregated from 95 source datasets, suggesting broad coverage.
- Covers 12 imaging modalities, including X-ray, CT, and MRI.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is known, but specific file formats and data structure details are unavailable.
- Data may reflect source bias inherent to the 95 aggregated datasets from platforms like Kaggle.
Provenance
- Source
- Aggregated from 95 medical datasets from reliable sources such as Kaggle, GrandChallenge, and Open-Release.
- Collection Method
- Collected and preprocessed following standardization methods from SAMed-20M.
- Time Range
- null
- Freshness
- Last updated 2025-07-21 11:31:05; freshness should be verified.
- Geography
- null