Shifting from black box decisions to informed decision-making in using artificial intelligence to analyze prostate...

Prostate magnetic resonance imaging (MRI) is central to the detection and localization of clinically significant prostate cancers. However, its interpretation is time-consuming and variable. Artificial intelligence (AI) tools for prostate MRI are rapidly emerging, with their performance approaching that of expert readers in select settings, although robust external validation in unselected real-world cohorts remains incomplete. Moreover, many deep learning models are limited in terms of transparency; that is, the "black box" problem hinders trust, safe deployment, and regulatory acceptance. This review summarizes the core concepts of interpretability and explainable artificial intelligence (XAI), highlights commonly used approaches to explain the output of AI models, and discusses how the lack of transparency hinders clinical deployment and how this can be addressed using standardized reporting frameworks. Ultimately, the purpose of this review is to draw attention to the pressing need for XAI and to spark interest and awareness of how standardized frameworks may help us to shift from "black box" decisions to informed decision-making in clinical practice.