Summary
Provides a structured framework for evaluating AI tools that support clinical decision-making. Organizes review around the clinical use case and users, relevance of training and validation data, risks and safeguards, effectiveness and performance, and workflow integration and ongoing monitoring, with questions for interpreting model cards and engaging vendors.
Healthcare Implications
Physicians, health systems, procurement teams, and developers can use the guide to assess whether an AI tool fits the intended patient population and clinical workflow, identify missing evidence or disclosures, evaluate performance and known limitations, and establish monitoring expectations before adoption. It complements the AMA governance toolkit by focusing on individual tool evaluation rather than enterprise governance design.