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background multiparametric mri mpmri improves prostate cancer pca detection compared systematic biopsy interpretation prone interreader variation results performance inconsistency artificial intelligence ai models assist mpmri interpretation large training data sets extensive model testing required purpose evaluate biparametric mri ai algorithm intraprostatic lesion detection segmentation compare performance radiologist readings biopsy results materials methods secondary analysis prospective registry included consecutive patients suspected known pca underwent mpmri usguided systematic biopsy combined systematic mrius fusionguided biopsy april 2019 september 2022 lesions prospectively evaluated using prostate imaging reporting data system version 21 lesion participantlevel performance previously developed cascaded deep learning algorithm compared histopathologic outcomes radiologist readings using sensitivity positive predictive value ppv dice similarity coefficient dsc results total 658 male participants median age 67 years iqr 6171 years 1029 mrivisible lesions included histopathologic analysis 45 294 658 participants lesions international society urological pathology isup grade group gg 2 higher algorithm identified 96 282 294 95 ci 94 98 participants clinically significant pca whereas radiologist identified 98 287 294 95 ci 96 99 p 23 algorithm identified 84 103 122 96 152 159 96 47 49 95 38 40 98 45 46 participants isup gg 1 2 3 4 5 lesions respectively lesionlevel analysis using radiologist ground truth detection sensitivity 55 569 1029 95 ci 52 58 ppv 57 535 934 95 ci 54 61 mean number falsepositive lesions per participant 061 range 03 lesion segmentation dsc 029 conclusion ai algorithm detected cancersuspicious lesions biparametric mri scans performance comparable experienced radiologist moreover algorithm reliably predicted clinically significant lesions histopathologic examination clinicaltrialsgov identifier nct03354416 rsna 2024 supplemental material available article