Summary
Provides an integrated suite of practical guides organized around eight key decisions in healthcare AI adoption: identifying and prioritizing a problem; defining product scope and intended use; developing success measures; designing workflows; generating evidence of safety, efficacy and equity; executing rollout; monitoring; and updating or decommissioning.
Healthcare Implications
Health systems can use the toolkit to structure multidisciplinary decision-making across the full adoption lifecycle, conduct local validation and risk assessment, prepare workflows and end users, prevent use outside intended scope, monitor real-world effects, and decide when to modify or retire an AI solution.