Evaluating the Performance and Clinical Utility of AI-driven Diagnostic Tools in Radiology

Abstract

The increasing integration of artificial intelligence (AI) tools into radiology has created an urgent need for clear, practical guidance on how to evaluate them. These tools, including computer-assisted detection and triage devices, hold promise for improving accuracy, efficiency, and workflow. However, their adoption into clinical practice requires rigorous evaluation to ensure safety, generalizability, and clinical value. Radiology has a strong foundation in diagnostic test assessment, and AI models represent an extension of this tradition, with new considerations in evaluation strategy, performance measurement, and study design. This article provides a structured primer on evaluating AI models across their development and deployment life cycle. It outlines key principles for internal and external testing, highlights performance metrics tailored to AI outputs-including classification, detection, segmentation, and continuous measures-and describes how to assess clinical impact with multireader multicase studies. Practical examples from radiology research, as well as updated reporting standards, are incorporated throughout. By translating core statistical concepts into radiology-specific guidance, this article aims to support radiologists, researchers, and reviewers in conducting and interpreting high-quality AI evaluation studies.

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