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image to text benchmark

VQA-Rad

VQA-RAD (Visual Question Answering in Radiology) is the first manually constructed dataset of medical visual question answering containing 3,515 clinically generated visual questions and answers about radiology images. The dataset includes questions created by clinical trainees on 315 radiology images from MedPix covering head, chest, and abdominal scans, designed to support AI development for medical image analysis and improve patient care.

Updated Aug 11, 2026

Models1
Model coverage1
MetricScore
EvidenceB

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VQA-Rad Ranking

Higher score ranks better on this benchmark.

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01GOMedGemma 4B ITGoogle49.9%100.0%1CAug 11, 2026

VQA-Rad Highlights

The leading models and scores on this benchmark.

Rank #1MedGemma 4B IT49.9%

What is VQA-Rad?

What VQA-Rad measures and how its scores work.

VQA-RAD (Visual Question Answering in Radiology) is the first manually constructed dataset of medical visual question answering containing 3,515 clinically generated visual questions and answers about radiology images. The dataset includes questions created by clinical trainees on 315 radiology images from MedPix covering head, chest, and abdominal scans, designed to support AI development for medical image analysis and improve patient care.

Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.

Family
VQA-Rad
Modality
multimodal
Primary category
image to text
Score direction
higher
LLMBoard eligible
No
Evaluation key
vqa-rad|llm-stats-current

Benchmark scores retain their original unit. Overall score eligibility is shown separately.

FAQ

Common questions about VQA-Rad.

Which model scores highest on VQA-Rad?

MedGemma 4B IT is currently ranked first with 49.9%.

What does VQA-Rad measure?

VQA-RAD (Visual Question Answering in Radiology) is the first manually constructed dataset of medical visual question answering containing 3,515 clinically generated visual questions and answers about radiology images. The dataset includes questions created by clinical trainees on 315 radiology images from MedPix covering head, chest, and abdominal scans, designed to support AI development for medical image analysis and improve patient care.

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

1 model results are currently shown.

Does this benchmark affect the overall score?

No. This benchmark is shown for reference but does not contribute to the overall score.