Google publishes framework for evaluating medical models on accuracy, safety, and usefulness
Prepared by Sintelligent with the help of SI. Published in SI: . Updated: .
A model's medical answers can be assessed separately for factual accuracy, risks, and practical usefulness rather than reduced to one overall score.
The document, prepared with physicians Elizabeth McGlynn and Dana Gelb Safran, describes end-to-end evaluation of medical use cases for SI. The methodology combines closed-question benchmarks with known answers, autoraters, and review by human experts, covering both common and high-risk cases. Google uses this framework to evaluate its own models and is publishing it as a reference for developers, researchers, and clinicians.
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