Bridging the validation gap in artificial intelligence in radiology

Abstract

Artificial intelligence (AI) has rapidly advanced in radiology, demonstrating high performance across a wide range of diagnostic tasks. However, clinical adoption remains slower and more uneven than anticipated. This discrepancy reflects a fundamental gap between algorithm validation and clinical implementation. Current validation strategies primarily rely on controlled datasets and performance metrics such as accuracy and area under the curve, which often fail to capture the complexity of clinical environments. This article examines the nature of this “validation gap” and argues that it reflects a broader structural mismatch between how AI systems are evaluated and how clinical care operates. We propose a conceptual framework comprising three levels of validation: technical validity, workflow validity, and clinical validity. While most studies focus on technical performance, limited attention is given to integration into clinical workflows and impact on patient outcomes. Key factors contributing to this gap include limited generalizability across diverse populations and imaging protocols, poor alignment with clinical workflows, and the underrepresentation of uncertainty in model outputs. These limitations hinder effective implementation and may reduce trust in AI systems. Bridging this gap requires a shift toward more comprehensive validation strategies, including multicenter and prospective studies, improved workflow integration, and explicit incorporation of uncertainty and human–AI interaction. Ultimately, the clinical value of AI in radiology should be assessed not only by its performance in controlled settings but also by its ability to support decision-making and improve patient outcomes in real-world practice.

Keywords

artificial intelligence / radiology / validation / generalizability / clinical implementation / uncertainty

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