MedGemma for Clinical AI Model Selection and Deployment

HAI-DEF
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We recently explored using MedGemma within a multi-agent clinical AI system to help with both model selection and deployment.

In most workflows today, picking the right model is still a very manual process, and deployment ends up fragmented across many task-specific pipelines. We wanted to see if a single VLM could help coordinate both steps.

We used MedGemma to:
• Route tasks (modality → abnormality → model matching)
• Execute tasks via specialty-level fine-tuning

This made model selection more structured/transparent and let us consolidate multiple models into fewer specialty-level ones, while still maintaining performance. We also saw ~10% improvement in routing accuracy.

Blog post: From Data to Decision: VLMs for Model Selection and Deployment in Clinical AI

Curious if others have tried similar approaches for model routing or multi-stage reasoning in clinical workflows.

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Hi @Navin_Kumar1,

This is a very fascinating approach, using a VLM to unify model selection and execution is a very compelling direction. A brilliant application of the model, and we really appreciate you sharing your insights with the broader community.

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Thank you! Much more to come.

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