Navigating the Frontier of artificial intelligence implementation in radiology – part 1: Performance assessment

Abstract

Despite the exponential growth in academic publications and industrial investments in artificial intelligence (AI) in medical imaging, clinical translation remains disproportionately low. Notably, the absence of internationally recognized guidelines for evaluating AI model performance and ethical considerations creates a critical gap in current practices. In this regard, we aim to offer a practical concise perspective exploring performance challenges to implementation while focusing on their mitigation. The dialog continues in subsequent work (part 2) which focuses on ethical issues. In this part, we explore the challenges inherent to the performance evaluation of AI in radiology, focusing on data heterogeneity, the choice of performance metrics and their interpretability, and data access. By shedding light on these issues and discussing potential opportunities, this work contributes to the ongoing dialog surrounding the practical integration of AI in clinical settings. It highlights the imperative need for established guidelines to ensure the safe and efficient deployment of AI technologies in medical imaging, ultimately bridging the gap between theoretical potential and practical implementation.

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