MedGemma
MedGemma is Google’s open-weight medical language and vision family, based on Gemma 3. The May 2025 release included 4B multimodal and 27B text instruction-tuned models; a 27B multimodal model was added July 9. Medical visual checkpoints use a SigLIP encoder trained on de-identified clinical images. The multimodal 27B variant also targets medical records and FHIR data. These are developer foundations requiring adaptation and validation; the independently released MedGemma 1.5 update has its own page.
2025-05-20
4B, 27B
Decoder-only Transformer (based on Gemma 3)
Health AI Developer Foundations terms of use
Specifications
- Parameters
- 4B, 27B
- Architecture
- Decoder-only Transformer (based on Gemma 3)
- License
- Health AI Developer Foundations terms of use
- Context Window
- 131,072 tokens
- Max Output
- 8,192 tokens
- Type
- multimodal
- Modalities
- textimage
Benchmark Scores
Advanced Specifications
- Model Family
- Gemma
- Finetuned From
- gemma-3
- API Access
- Available
- Chat Interface
- Not Available
- Multilingual Support
- No
- Variants
- 4B instruction-tuned (medgemma-4b-it)4B pre-trained (medgemma-4b-pt)27B text instruction-tuned (medgemma-27b-text-it)27B multimodal instruction-tuned (medgemma-27b-it; July 9, 2025)
- Hardware Support
- CUDATPUCPU
Capabilities & Limitations
- Capabilities
- medical image classificationmedical image interpretationchest X-ray interpretationdermatology image analysishistopathology analysisophthalmology image analysisfundus image analysisradiology report generationmedical visual question answeringmedical text comprehensionclinical reasoningmedical knowledge retrievalmultimodal medical understandingpatient interviewingmedical triagingclinical decision supportmedical summarizationprompt engineering adaptationfine-tuning capabilityagentic orchestration
- Known Limitations
- Not intended for direct clinical diagnosis or treatment recommendationsDeveloper model that requires validation on intended use caseBaseline performance may need improvement through adaptationNot clinical grade without additional fine-tuningPrimarily evaluated on single-image tasksNot evaluated for multi-turn applicationsMay be more sensitive to specific prompts than base Gemma 3Evaluation primarily in English language
- Notable Use Cases
- medical image classificationmedical image interpretation and reportingradiology image analysisdigital pathology classificationfundus image analysisskin image classificationmedical visual question answeringpatient interviewing systemsmedical triaging applicationsclinical decision support toolsmedical summarizationhealthcare AI application developmentagentic medical systemsFHIR data processingprivate health data parsingmedical education platforms
- Function Calling Support
- Yes
- Tool Use Support
- Yes