This article reviews the application of artificial intelligence (AI) in prostate magnetic resonance imaging (MRI) screening, focusing on its performance and impact on diagnostic accuracy. Prostate cancer is a prevalent form of cancer in men, and MRI-guided biopsies have become a reliable diagnostic tool. However, the diagnostic performance of MRI can vary significantly due to the complex and multi-step nature of the diagnostic process. The development of AI models, particularly through deep learning techniques, has significantly enhanced the field of radiology. These models are designed for tasks such as prostate segmentation, lesion identification, and classification, aiming to improve diagnostic performance and consistency among different observers. The article highlights the challenges in prostate MRI, including the subjective nature of the Prostate Imaging Reporting and Data System (PI-RADS) and the impact of patient preparation and equipment parameters. AI-based approaches, such as convolutional neural networks (CNNs), have shown promise in improving image quality and reducing acquisition time. Studies have demonstrated that AI algorithms can achieve high accuracy in lesion detection and segmentation, with some models achieving Dice similarity coefficients (DSCs) of over 0.90. These advancements are crucial for enhancing the reliability and efficiency of MRI-guided prostate cancer diagnosis. The article also discusses the integration of AI in prostate tissue segmentation, which is essential for precise measurements and planning of radiation therapy. Commercial AI platforms for prostate MRI are available, but they often require larger and more diverse datasets for optimal performance. The authors emphasize the need for further research and clinical validation to ensure the widespread applicability and reliability of AI-based systems in clinical practice. In conclusion, the integration of AI in prostate MRI has significantly improved diagnostic accuracy and efficiency. While challenges remain, such as the need for larger and more diverse datasets, the potential benefits are substantial, and ongoing efforts are underway to further enhance the performance and reliability of AI in this field.This article reviews the application of artificial intelligence (AI) in prostate magnetic resonance imaging (MRI) screening, focusing on its performance and impact on diagnostic accuracy. Prostate cancer is a prevalent form of cancer in men, and MRI-guided biopsies have become a reliable diagnostic tool. However, the diagnostic performance of MRI can vary significantly due to the complex and multi-step nature of the diagnostic process. The development of AI models, particularly through deep learning techniques, has significantly enhanced the field of radiology. These models are designed for tasks such as prostate segmentation, lesion identification, and classification, aiming to improve diagnostic performance and consistency among different observers. The article highlights the challenges in prostate MRI, including the subjective nature of the Prostate Imaging Reporting and Data System (PI-RADS) and the impact of patient preparation and equipment parameters. AI-based approaches, such as convolutional neural networks (CNNs), have shown promise in improving image quality and reducing acquisition time. Studies have demonstrated that AI algorithms can achieve high accuracy in lesion detection and segmentation, with some models achieving Dice similarity coefficients (DSCs) of over 0.90. These advancements are crucial for enhancing the reliability and efficiency of MRI-guided prostate cancer diagnosis. The article also discusses the integration of AI in prostate tissue segmentation, which is essential for precise measurements and planning of radiation therapy. Commercial AI platforms for prostate MRI are available, but they often require larger and more diverse datasets for optimal performance. The authors emphasize the need for further research and clinical validation to ensure the widespread applicability and reliability of AI-based systems in clinical practice. In conclusion, the integration of AI in prostate MRI has significantly improved diagnostic accuracy and efficiency. While challenges remain, such as the need for larger and more diverse datasets, the potential benefits are substantial, and ongoing efforts are underway to further enhance the performance and reliability of AI in this field.