Artificial intelligence (AI) in prostate MRI analysis shows great promise and impressive performance. A large number of studies present the usefulness of AI models in tasks such as prostate segmentation, lesion detection, and the classification and stratification of a cancer’s aggressiveness. This article presents a subjective critical review of AI in prostate MRI analysis. It discusses both the technology’s current state and its most recent advances, as well as its challenges. The article then presents opportunities in the context of ongoing research, which possesses the potential to reduce bias and to be applied in clinical settings.

The detection of prostate cancer is an important challenge for medical personnel. To improve the medical system’s ability to process increasing numbers of oncological patients, demand for automation systems is growing. At the National Information Processing Institute, such systems are undergoing active development. In this work, the authors present the results of a pilot study whose goal is to analyze possible directions in the development of new, advanced deep learning systems using a high quality dataset that is currently in development.

Accurate detection of prostate cancer suspicious areas in biparametric MRI (bpMRI) remains challenging because of severe lesion-to-background imbalance, limited lesion contrast, and inter-reader variability in lesion delineation. Unlike prior approaches that collapse inter-reader disagreement into a single consensus label, this study makes three contributions: (1) an adapted nnU-Net framework with prostate-centered preprocessing to reduce voxel-level class imbalance; (2) a class-imbalance-aware composite loss combining Dice, binary cross-entropy, and tailored focal loss to improve sensitivity to small and low-contrast lesions; and (3) a multi-expert learning strategy that preserves reader-specific annotations as separate supervision targets and aggregates predictions at the ensemble level. The method was developed on a single-center dataset of 378 bpMRI studies independently annotated by three board-certified radiologists. Of these, 323 studies were used for model development with patient-level 5-fold cross-validation, and 55 studies were reserved as a fixed independent test set. Compared with our previously published U-Net baseline, the proposed consensus-based nnU-Net improved Average Precision (AP) from 0.69 to 0.75, AUROC from 0.92 to 0.96, and the PI-CAI score from 0.81 to 0.85 on the independent test set. In addition, the multi-expert approach further improved AP to 0.81 versus 0.76 (+6.6%, p < 0.01), AUROC to 0.99 versus 0.95 (+4.2%, p < 0.01), and the PI-CAI score to 0.90 versus 0.86 (+4.7%). These findings demonstrate that explicitly preserving expert disagreement as a training signal, combined with anatomically targeted preprocessing and tailored loss design, substantially improves prostate lesion detection in bpMRI, providing a strong basis for future multi center external validation.