One-fifth of all men will develop clinically significant prostate cancers (CsPC) in their lifetime. An estimated 268,490 new prostate cancer (PCa) cases and 34,500 deaths are expected in the United States during the year 2022, making PCa the second most common cause of cancer-related deaths in men. MRI with the Prostate Imaging Reporting and Data System (PI-RADS) is a current widely used communicative tool for both CsPC detection and guiding targeted prostate biopsy. The high level of expertise required for accurate interpretation and persistent inter-reader variability has limited consistency and it has hindered the widespread adoption of PI-RADS. Artificial intelligence (AI) shows a broad prospect for medical interpretation and triage in various challenging tasks , including the PCa detection and staging with MRI. While rapid technical advances are furthering the application of AI medical imaging, their implementation in clinical practice remains a major hurdle. Besides, the prospect of data-derived AI tool is to assist human experts rather than replace them, and whether AI can match or exceed the human experts is still a matter of debate. Therefore, despite strong potential, there is urgent need for research to better quantify the accuracy, generalizability and clinical applicability before the clinical use of an AI in a real-world clinical setting.