Navigating PI-RADS v2.1 in clinical practice: pitfalls, variability, and the supportive role of AI

Prostate Imaging Reporting and Data System (PI-RADS) version 2.1 has substantially advanced the standardization of prostate MRI acquisition, interpretation, and reporting and has helped establish a common language for MRI-directed diagnostic pathways. Nevertheless, clinically meaningful variability persists across readers and institutions and continues to influence biopsy referral, targeting, and risk stratification. This review focuses on high-yield pitfalls that drive false-positive and false-negative interpretation in the peripheral and transition zones, including background prostatitis, post-biopsy hemorrhage, base artifacts, dynamic contrast-enhancement (DCE) overcalling, transition-zone nodule misclassification, upgrading-rule misuse, and measurement/mapping inconsistency. We then examine how protocol differences, image quality, reader experience, and biopsy pathways create inter-institutional variability in positive predictive value and cancer detection. Particular emphasis is placed on PI-RADS category 3 management, reporting language that communicates uncertainty, and quality-assurance strategies such as prostate imaging quality-based auditing and multidisciplinary feedback. Finally, we discuss Artificial Intelligence (AI) as a potentially helpful adjunct for quality assessment, lesion detection, triage, and decision support while emphasizing its current limitations in validation, generalizability, and clinical accountability. Continued gains in prostate MRI performance will depend on preserving the strengths of PI-RADS, maintaining consistent image quality and reader calibration, and integrating supportive AI tools cautiously and under clinical oversight.