Artificial Intelligence in Prostate Cancer Detection and PI-RADS Scoring

Abstract

Prostate cancer (PCa) continues to be the second most prevalent malignant tumor among men worldwide, underscoring the pressing demand for precise and efficient diagnostic tools. The Prostate Imaging-Reporting and Data System (PI-RADS) has set standardized guidelines for interpreting prostate magnetic resonance imaging (MRI), whereas artificial intelligence (AI) technologies—particularly deep learning and machine learning—have emerged as revolutionary tools to boost diagnostic accuracy and optimize work processes.This review systematically summarizes the integration of AI with PI-RADS scoring for PCa detection, covering technological advancements, clinical applications, epidemiological implications, ethical controversies, and future directions. We synthesize evidence from 25 recent studies, highlighting AI's role in improving lesion detection, reducing inter-reader variability, and optimizing PI-RADS-based risk stratification. Critical challenges, including algorithm generalizability, regulatory hurdles, and ethical concerns, are discussed, alongside prospects for personalized medicine and multi-modal integration. This review presents a detailed update to clinicians, researchers, and policymakers concerning the current state and future possibilities of AI-supported PCa diagnosis based on PI-RADS.

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