OpenMedReason: Medical Visual Question Answering with Structured Reasoning Traces

OpenMedReason is a multimodal dataset for medical visual question answering (VQA) containing structured chain-of-thought reasoning traces. It includes 192,619 training examples and 1,500 test examples. The dataset was created by author 'neginb' and was last updated on the Hugging Face platform in May 2026.

Use Cases

• Training models for medical visual question answering based on the provided image and question columns.
• Developing and benchmarking chain-of-thought reasoning models based on the structured 'reasoning' text traces.
• Evaluating the alignment between model-generated reasoning and final answers based on the provided 'answer' ground truth.

Strengths

• Includes 192,619 training examples, which suggests a substantial scale for model training.
• Provides structured reasoning traces in a specific format (e.g., and <answer>X</answer>), which can support detailed model evaluation.
• Contains a dedicated test split of 1,500 examples for standardized benchmarking.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent beyond basic names and types; field semantics must be inferred after download.
• The description notes 'Train excludes…', indicating potential data exclusions whose impact is unclear without the full dataset page.

Provenance

Source

huggingface

Freshness

Last updated 2026-05-19 20:11:55; freshness should be verified.