# Using Artificial Intelligence as a Risk Prediction Model in Patients with Equivocal Multiparametric Prostate MRI Findings

## TL;DR

AI can help predict cancer risk in uncertain prostate MRI results, potentially reducing unnecessary biopsies and improving diagnosis.

## Contribution

This paper reviews how AI improves risk prediction for prostate cancer in equivocal MRI cases and discusses integration into clinical practice.

## Key findings

- AI models using radiomics and deep learning can distinguish benign from cancerous prostate lesions with high accuracy.
- AI systems matched or exceeded expert radiologists in detecting clinically significant prostate cancer in large multicenter studies.
- Combining AI with clinical data like PSA density improves cancer risk prediction.

## Abstract

Magnetic resonance imaging of the prostate is widely used to detect prostate cancer, but one common result—called a PI-RADS 3 lesion—remains difficult to interpret. These findings are uncertain: most are not dangerous, yet some contain clinically important cancer. This uncertainty leads to many men undergoing unnecessary biopsies, while others may experience delayed diagnosis of aggressive disease. New artificial intelligence techniques can analyse prostate scans in more detail than the human eye and combine imaging patterns with clinical information to estimate cancer risk more accurately. This review explains why PI-RADS 3 lesions remain a problem, summarises how artificial intelligence has been studied to improve decision-making in this setting, and discusses how these tools could be safely integrated into routine care. If validated and implemented carefully, artificial intelligence may help doctors reduce unnecessary procedures, improve cancer detection, and provide more consistent care for patients with uncertain prostate scan findings.

Introduction: PI-RADS 3 lesions represent a diagnostic grey zone on multiparametric MRI, with clinically significant prostate cancer (csPCa) detected in only 10–30%. Their equivocal nature leads to both unnecessary biopsies and missed cancers. Artificial intelligence (AI) has emerged as a potential tool to provide objective, reproducible risk prediction. This review summarises current evidence on AI for risk stratification in patients with indeterminate mpMRI findings, including clarification of key multicentre initiatives such as the PI-CAI (Prostate Imaging–Artificial Intelligence) study—a global benchmarking effort comparing AI systems against expert radiologists. Methods: A narrative review of PubMed and Embase (search updated to August 2025) was conducted using terms including “PI-RADS 3”, “radiomics”, “machine learning”, “deep learning”, and “artificial intelligence.” Eligible studies included those evaluating AI-based prediction of csPCa in PI-RADS 3 lesions using biopsy or long-term follow-up as reference standards. Both single-centre and multicentre studies were included, with emphasis on externally validated models. Results: Radiomics studies demonstrate that handcrafted features extracted from T2-weighted and diffusion-weighted imaging can distinguish benign tissue from csPCa, particularly in the transition zone, with area-under-the-ROC curves typically 0.75–0.82. Deep learning approaches—including convolutional neural networks and large-scale representation-learning frameworks—achieve higher performance and can reduce benign biopsy rates by 30–40%. Models that integrate imaging-based AI with clinical predictors such as PSA density further improve discrimination. The PI-CAI study, the largest international benchmark to date (>10,000 MRI exams), shows that state-of-the-art AI systems can match or exceed expert radiologists for csPCa detection across diverse scanners, centres, and populations, though prospective validation remains limited. Conclusions: AI shows strong potential to refine management of PI-RADS 3 lesions by reducing unnecessary biopsies, improving csPCa detection, and mitigating inter-reader variability. Translation into routine practice will require prospective multicentre validation, harmonised imaging protocols, and integration of AI outputs into clinical workflows with clear thresholds, decision support, and safety-net recommendations.