Step-CoT: 70,000 Medical VQA Pairs with Stepwise Clinical Reasoning

Step-CoT is a large-scale medical reasoning dataset containing over 10,000 real clinical chest X-ray cases and 70,000 visual question-answering pairs. It was created by author fl-15o and last updated on March 23,我们发现2026. Each VQA pair is structured into a seven-step diagnostic workflow designed to mirror clinical reasoning.

Use Cases

• Training models for medical visual question answering based on chest X-ray images and associated questions.
• Developing interpretable AI systems based on the structured seven-step diagnostic workflow.
• Benchmarking clinical reasoning capabilities in multimodal AI based on the stepwise chain-of-thought annotations.
• Researching the alignment of AI diagnostic processes with human clinical workflows based on the dataset's design.

Strengths

• Contains over 10,000 real clinical chest X-ray cases, providing a substantial clinical basis.
• Includes 70,000 VQA pairs, offering a large-scale resource for model training.
• Structures reasoning into a seven-step diagnostic workflow, explicitly designed to improve interpretability.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

huggingface

Collection Method

Likely curated from real clinical chest X-ray cases, though specific collection method is not detailed.

Time Range

null

Freshness

Last updated 2026-03-23 10:28:30; freshness should be verified.

Geography

null