This systematic review examines the diagnostic accuracy of deep learning (DL) models for the automatic detection, localization, and characterization of clinically significant prostate cancer (csPCa) using magnetic resonance imaging (MRI). Given the variability in MRI interpretation and diagnostic accuracy, the study evaluates the potential of DL models to enhance prostate cancer diagnosis, focusing on how these models can improve the accuracy and reliability of MRI-based prostate cancer localization and characterization to address issues such as inter-reader variability and specificity limitations. The selected studies validated fully automated DL models for csPCa detection on MRI, using pathologist confirmation as the reference standard. The 25 included studies demonstrated promising results, with most reporting high diagnostic accuracy in detecting and characterizing csPCa. However, performance varied significantly due to various differences in datasets, methodologies, and validation strategies. Only roughly one-third of the studies performed external validation which brought up concerns regarding generalizability and clinical applicability. Differences in AI architectures, input MRI sequences, and performance metrics limited the ability to draw definitive conclusions about the best approaches for clinical implementation. The review emphasizes the need for standardized methodologies in addition to larger and more diverse validation cohorts and external validation to improve the reliability of DL-based prostate cancer detection. The authors conclude that while DL models show significant promise, their integration into routine clinical practice requires addressing these limitations through prospective clinical trials and standardized reporting frameworks.