Artificial intelligence (AI) is increasingly being applied in prostate magnetic resonance imaging (MRI) for tasks such as lesion detection, segmentation, and classification. Prostate cancer is a major health issue globally, and MRI is a key diagnostic tool. However, the diagnostic performance of MRI is affected by various factors, including equipment, patient movement, and imaging protocols. The PI-RADS system is used to standardize MRI interpretation, but it remains subjective. AI, particularly deep learning techniques like convolutional neural networks (CNNs), is being used to improve diagnostic accuracy and consistency in prostate MRI. AI-based models can enhance image quality, reduce acquisition time, and improve lesion detection. Studies have shown that AI can achieve high accuracy in prostate segmentation and lesion identification, with results comparable to or better than human experts. AI also helps in reducing the time required for MRI data preparation and improves the efficiency of radiologists. However, challenges remain, including the need for large, diverse, and well-annotated datasets for training AI models. Federated learning is a promising approach to address data privacy and sharing issues. Despite these challenges, AI is showing significant potential in improving the accuracy and efficiency of prostate MRI in clinical practice.Artificial intelligence (AI) is increasingly being applied in prostate magnetic resonance imaging (MRI) for tasks such as lesion detection, segmentation, and classification. Prostate cancer is a major health issue globally, and MRI is a key diagnostic tool. However, the diagnostic performance of MRI is affected by various factors, including equipment, patient movement, and imaging protocols. The PI-RADS system is used to standardize MRI interpretation, but it remains subjective. AI, particularly deep learning techniques like convolutional neural networks (CNNs), is being used to improve diagnostic accuracy and consistency in prostate MRI. AI-based models can enhance image quality, reduce acquisition time, and improve lesion detection. Studies have shown that AI can achieve high accuracy in prostate segmentation and lesion identification, with results comparable to or better than human experts. AI also helps in reducing the time required for MRI data preparation and improves the efficiency of radiologists. However, challenges remain, including the need for large, diverse, and well-annotated datasets for training AI models. Federated learning is a promising approach to address data privacy and sharing issues. Despite these challenges, AI is showing significant potential in improving the accuracy and efficiency of prostate MRI in clinical practice.