Does It Work, Help, and Stay? A Framework for Implementing Artificial Intelligence Tools in Radiology

Abstract

The adoption of artificial intelligence (AI) into clinical practice in radiology can be facilitated by following a structured pipeline for implementation. In this article, we propose a practical framework for the responsible implementation of AI through four phases: validation, deployment, value assessment, and postdeployment surveillance. Validation involves retrospective or offline testing on institutional data to assess the model's local performance. Deployment progresses through limited trial and full deployment stages, with an emphasis on workflow considerations, integrations, operational metrics, and stakeholder feedback. Value assessment is longitudinal throughout these phases and encompasses both financial and nonfinancial returns on investment. Finally, ongoing surveillance can detect data drift, monitor clinical performance, and maintain AI safety. The framework proposed herein provides a governance-oriented approach to AI implementation, addressing the core questions: Does it work? Does it help? Does it stay?

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