AI-based models, such as XGBoost, random forest, and neural networks, use machine learning algorithms to integrate radiological features (e.g., PI-RADS scores) with clinical variables (age, PSA, prostate volume, PSA density, digital rectal examination (DRE) findings, family history, prior biopsy) to predict clinically significant prostate cancer (csPCa) and reduce unnecessary biopsies. This evidence concerns the gene KLK3 and Familial prostate cancer.