---
language:
- en
task_categories:
- question-answering
tags:
- condition-gated-reasoning
- clinical-reasoning
- multi-hop-qa
- medical-qa
license: mit
size_categories:
- n<1K
configs:
- config_name: default
  data_files:
  - split: test
    path: condmedqa.csv
dataset_info:
- config_name: default
  features:
  - name: id
    dtype: int64
  - name: wiki1
    dtype: string
  - name: wiki2
    dtype: string
  - name: q
    dtype: string
  - name: a
    dtype: string
---

# CondMedQA

A diagnostic benchmark of **100 condition-dependent biomedical questions** where the correct answer changes based on patient-specific factors such as comorbidities, allergies, pregnancy status, and contraindications.

## Dataset Description

Real-world clinical reasoning is inherently conditional: treatment decisions depend on patient-specific factors such as comorbidities, drug allergies, pregnancy, and organ function. Existing biomedical QA benchmarks largely ignore this conditionality, testing only context-free factual recall.

**CondMedQA** fills this gap with 100 expert-curated questions that require reasoning over two Wikipedia source articles to arrive at a condition-appropriate answer. Each question presents a clinical scenario where the standard-of-care answer is modified by a specific patient condition.

### Key Properties

- **Domain**: Biomedical / clinical pharmacology / diagnostic imaging
- **Question type**: Short-answer, condition-dependent
- **Multi-hop**: Every question requires evidence from 2 source articles
- **Total questions**: 100
- **Answer length**: Typically 1–5 words
- **Split**: Test only (diagnostic benchmark)

## Dataset Format

A single CSV file (`condmedqa.csv`) with the following columns:

| Column | Description |
|--------|-------------|
| `id` | Question identifier (1–100) |
| `wiki1` | URL of the first Wikipedia source article (primary condition/disease) |
| `wiki2` | URL of the second Wikipedia source article (drug/test/modifier) |
| `q` | The clinical question with patient-specific conditions |
| `a` | The condition-appropriate answer |

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("jashparekh/CondMedQA")
```

Or directly:

```python
import pandas as pd

df = pd.read_csv("condmedqa.csv")
```

## Intended Use

- Evaluating LLMs on **condition-dependent clinical reasoning**
- Benchmarking retrieval-augmented generation (RAG) pipelines on context-sensitive medical QA
- Testing whether models can integrate patient-specific constraints with medical knowledge
- Diagnostic evaluation (100 questions — not intended as a training set)

## Citation

```bibtex
@article{parekh2026conditiongated,
  title={Condition-Gated Reasoning for Context-Dependent Biomedical Question Answering},
  author={Parekh, Jash Rajesh and Kweon, Wonbin and Chan, Joey and Islamaj, Rezarta and Leaman, Robert and Jiang, Pengcheng and Wei, Chih-Hsuan and Wang, Zhizheng and Lu, Zhiyong and Han, Jiawei},
  journal={arXiv preprint arXiv:2602.17911},
  year={2026}
}
```

## License

MIT