Google publishes framework for checking health AI accuracy, safety and usefulness

Google published a whitepaper on November 4, 2026, describing how it evaluates AI for health questions and other health needs. The framework treats accuracy, safety and usefulness as separate areas to assess, rather than focusing on correctness alone. Google says it already uses the approach to check its own models.

Writing in Google’s announcement, Dr. Sarah Montgomery-Taylor, its clinical evaluation lead for health, framed the work around a practical question: how to assess whether AI tools are safe and helpful as people bring them health questions and doctors use them in care. She described adoption of health technology as accelerating.

Benchmarks, automated raters and health experts

The response is an end-to-end evaluation approach rather than a single benchmark. It combines closed-question benchmarks, autoraters—automated systems that rate responses—and expert human review. These are the three methods Google identifies for assessing health-related AI use cases.

The scenarios cover both common health needs and high-risk situations. The framework’s scope therefore includes routine questions as well as health contexts Google identifies as carrying greater risk.

Google developed the approach in partnership with health experts Dr. Elizabeth McGlynn and Dr. Dana Gelb Safran. Montgomery-Taylor described publication as an effort to make the company’s methodology more transparent.

By sharing our methodology, we hope to promote transparency in how we approach this important area, and seek to support the entire health ecosystem — from developers to researchers to clinicians — in building AI tools people can trust.

Dr. Sarah Montgomery-Taylor, Clinical Evaluation Lead, Google for Health

A methodology disclosure, not clinical results

The announcement describes the evaluation framework but supplies no model-by-model scores or patient-outcome results. Publishing the method is not itself a demonstration that a particular model improves care. That distinction concerns the announcement; it does not establish what results the linked whitepaper contains.