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.
MedGemma 4B IT from Google currently leads the VQA-Rad leaderboard with a score of 0.499 across 1 evaluated AI models.
MedGemma 4B IT leads with 49.9%.
Progress Over Time
Interactive timeline showing model performance evolution on VQA-Rad
VQA-Rad Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | Google | 4B | — | — |
FAQ
Common questions about VQA-Rad.
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