MedGemma 4B IT
MedGemma is a collection of Gemma 3 variants that are trained for performance on medical text and image comprehension. MedGemma 4B utilizes a SigLIP image encoder that has been specifically pre-trained on a variety of de-identified medical data, including chest X-rays, dermatology images, ophthalmology images, and histopathology slides. Its LLM component is trained on a diverse set of medical data, including radiology images, histopathology patches, ophthalmology images, and dermatology images. MedGemma is a multimodal model primarily evaluated on single-image tasks. It has not been evaluated for multi-turn applications and may be more sensitive to specific prompts than its predecessor, Gemma 3. Developers should consider bias in validation data and data contamination concerns when using MedGemma.
Benchmark results
| Benchmark | Score | Tags | Source |
|---|---|---|---|
| CheXpert CXR | 48.1% | self-reported llm-stats | link → |
| DermMCQA | 71.8% | self-reported llm-stats | link → |
| MedXpertQA | 18.8% | self-reported llm-stats | link → |
| MIMIC CXR | 88.9% | self-reported llm-stats | link → |
| PathMCQA | 69.8% | self-reported llm-stats | link → |
| SlakeVQA | 62.3% | self-reported llm-stats | link → |
| VQA-Rad | 49.9% | self-reported llm-stats | link → |