Clinical Reasoning Vqa Eval

---
name: clinical-reasoning-vqa-eval
description: Evaluates multimodal clinical reasoning and medical knowledge by testing models on standardized medical exams, text-based QA benchmarks, and medical imaging visual question-answering tasks. Use when the user wants to benchmark on USMLE, MedQA, MMLU, MedXpertQA, VQA-RAD, BraTS, PathVQA, Blood Cell VQA, BreaKHis, EMBED, InBreast, CMMD, CBIS-DDS, or asks about evaluating this task. Reports percentage of correct answers.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2603.04763
  bibtex_key: [REDACTED]
  confidence: high
---

# clinical-reasoning-vqa-eval

> Evaluating GPT-5 as a Multimodal Clinical Reasoner: A Landscape Commentary — Florea et al. (2026) (arXiv:2603.04763, 2026)

## What this evaluates

Evaluates multimodal clinical reasoning and medical knowledge by testing models on standardized medical exams, text-based QA benchmarks, and medical imaging visual question-answering tasks.

## Datasets

- **USMLE** — total ?; splits: test (-1)
- **MedQA** — total ?; splits: test (-1)
- **MMLU** — total ?; splits: test (-1)
- **MedXpertQA** — total ?; splits: test (-1)
- **VQA-RAD** — total ?; splits: test (-1)
- **BraTS** — total ?; splits: test (-1)
- **PathVQA** — total ?; splits: test (-1)
- **Blood Cell VQA** — total ?; splits: test (-1)
- **BreaKHis** — total ?; splits: test (-1)
- **EMBED** — total ?; splits: test (-1)
- **InBreast** — total ?; splits: test (-1)
- **CMMD** — total ?; splits: test (-1)
- **CBIS-DDS** — total ?; splits: test (-1)

## Metrics

- `percentage of correct answers` **(primary)** — range: percent
  - Calculated as the number of correct predictions divided by the total number of questions, multiplied by 100. Reported as absolute percentage-point differences compared to baselines.
- `Macro-Average` — range: percent
  - An unweighted mean of accuracy scores across multiple datasets or subtasks.
- `Weighted Accuracy` — range: percent
  - A size-proportional mean of accuracy scores across different data sources or subsets.

## Input / output format

**Input**: Multiple-choice medical exam questions, text-based clinical QA prompts, or medical images (MRI, pathology slides, mammograms) paired with clinical questions.

**Output**: Model-generated multiple-choice selections or free-text diagnostic reasoning and answers.

## Scoring recipe

```python
def calculate_accuracy(predictions, gold):
    correct = sum(1 for p, g in zip(predictions, gold) if p == g)
    return (correct / len(gold)) * 100

def calculate_macro_average(dataset_accuracies):
    return sum(dataset_accuracies) / len(dataset_accuracies)

def calculate_weighted_accuracy(source_accuracies, source_weights):
    return sum(a * w for a, w in zip(source_accuracies, source_weights))
```

## Common pitfalls

- Confusing macro-average (unweighted mean across datasets) with weighted accuracy (size-proportional mean across sources).
- Overinterpreting accuracy gains on small-scale VQA benchmarks (e.g., VQA-RAD) without considering benchmark-specific variability or answer calibration differences.
- Assuming generalist model accuracy translates to clinical deployment readiness without comparing against domain-specific fine-tuned baselines.

## Evidence (verbatim from paper)

> All results are reported in Table 1 as the percentage of correct answers, unless otherwise stated. All comparisons are made using absolute percentage-point differences.

## Citation

```bibtex
@misc{florea2026gpt5clinical,
  title={Evaluating GPT-5 as a Multimodal Clinical Reasoner: A Landscape Commentary},
  author={Florea et al. (2026)},
  year={2026},
  note={arXiv:2603.04763}
}
```

- arXiv: 2603.04763