Background Although artificial intelligence is actively being developed for prostate MRI, few studies have prospectively validated these tools. Purpose To compare the diagnostic performance of a commercial deep learning algorithm (DLA) and radiologists' clinical reports for cancer detection in participants from two hospitals using histopathologic findings from biopsy specimens as the reference standard. Materials and Methods This prospective bicenter study enrolled participants suspected of having prostate cancer (PCa) who were scheduled for biopsy based on clinical information, including prostate MRI, from December 2022 to July 2023. Targeted prostate biopsies were performed for lesions with Prostate Imaging Reporting and Data System (PI-RADS) scores of 3 or higher, identified by either radiologists or the DLA. PI-RADS classifications by radiologists (using all imaging sequences), the DLA (using biparametric MRI), and the scenario in which radiologist-based PI-RADS 3 scores were modulated with DLA-based PI-RADS scores were compared using the area under the receiver operating characteristic curve (AUC) with DeLong and McNemar tests. Results A total of 259 lesions, including 117 clinically significant PCas (csPCas) (Gleason grade group ≥2), were evaluated in 205 men (median age, 68 years
age range, 47-90 years). At per-lesion analysis, the DLA had a lower sensitivity (94 of 117
80%) and higher positive predictive value (PPV) (94 of 163
58%) for detecting csPCa than did the radiologists (109 of 117 [93%] and 109 of 227 [48%]