multimodal benchmark
VQA-Rad Leaderboard
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 17, 2026
Models1
Model coverage1
MetricScore
EvidenceB
VQA-Rad Ranking
Higher score ranks better on this benchmark.
Sort by
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelGO | Score49.9% | Percentile100.0% | Participants1 | EvidenceC | Evaluated |
VQA-Rad Highlights
The leading models and scores on this benchmark.
The Top AI Models for VQA-Rad
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis vqa-rad AI model leaderboard uses descending score in the benchmark's original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
- 01GOMedGemma 4B ITGoogle
- Score
- 49.9%
Strengths
- Ranks #1 of 1 compared models
- 100th percentile on this benchmark
- C evidence result
Considerations
- This result measures VQA-Rad, not total model capability
Selection summary
Best AI Models for VQA-Rad
MedGemma 4B IT currently leads VQA-Rad with 49.9%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.
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
- multimodal
- 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.