---
license: cc-by-4.0
task_categories:
  - visual-question-answering
language:
  - en
tags:
  - medical
  - radiology
  - vqa
  - chain-of-thought
  - reasoning
size_categories:
  - 100K<n<1M
pretty_name: Medical VQA with Reasoning Traces (Anonymous)
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*.parquet
      - split: test
        path: data/test-*.parquet
---

## This data is the Open-PMC derived data part that we discuss in the paper

# Medical VQA with Reasoning Traces (Anonymous)

A multiple-choice medical visual question answering benchmark with
chain-of-thought reasoning traces. Each example consists of a medical image
(radiology, pathology, clinical photograph, etc.), a multiple-choice question
with labeled options, a reasoning trace, and the correct answer letter.

This dataset is released anonymously in support of a peer-reviewed submission.

## Columns

| Column              | Type   | Populated on   | Description                                                                         |
|---------------------|--------|----------------|-------------------------------------------------------------------------------------|
| `image`             | Image  | train, test    | The medical image relevant to the question (JPEG, embedded).                        |
| `question`          | string | train, test    | Question text followed by labeled answer options (e.g. `"A. ..."`).                 |
| `reasoning`         | string | train, test    | Chain-of-thought reasoning trace that supports the correct answer.                  |
| `answer`            | string | train, test    | Single uppercase letter giving the correct option (e.g. `"A"`).                     |
| `Perception`        | string | **test only**  | JSON list of *observation*-axis unit-questions (what the image actually shows).     |
| `Medical knowledge` | string | **test only**  | JSON list of *knowledge*-axis unit-questions (relevant clinical/medical facts).     |
| `Rationale`         | string | **test only**  | JSON list of *inference*-axis unit-questions (reasoning-from-observations claims).  |

`Perception` / `Medical knowledge` / `Rationale` are evaluation aids: each is a
JSON-encoded list of `{topic, claim, presence_question, correctness_question,
source_quote, importance}` items derived from the reference reasoning trace.
They are populated for the test split only and held as `"[]"` strings on the
train split for schema parity (so the dataset viewer works on both splits).


## Loading

```python
from datasets import load_dataset
import json

ds = load_dataset("researcher2026/OpenMedReason")
print(ds)
print(ds["train"][0])

# Eval-time helpers (test split):
ex = ds["test"][0]
perception = json.loads(ex["Perception"])
knowledge  = json.loads(ex["Medical knowledge"])
rationale  = json.loads(ex["Rationale"])
print(f"perception items: {len(perception)}")
```

## Anonymity

The dataset is hosted from an anonymous account and the dataset card omits any
identifying information about the authors, institution, or project.