79 Artificial intelligence (AI) is set to revolutionise prostate cancer management, promising more accurate diagnoses, targeted treatments, and improved patient outcomes, according to a comprehensive review published in the Asian Journal of Urology. The rapid evolution of AI, specifically machine learning (ML) and deep learning (DL), has enhanced diagnostic imaging, allowing clinicians to distinguish between clinically significant prostate cancer and less aggressive forms more effectively than conventional methods. AI systems, particularly those using multiparametric MRI (mpMRI), significantly improve the precision and reliability of diagnoses by automatically identifying and classifying tumours. Recent developments suggest that these AI-driven techniques not only match but often surpass traditional diagnostic procedures. One crucial development highlighted in the study is radiomics, an AI technique that extracts detailed quantitative data from medical images. Radiomics offers clinicians powerful insights into tumour characteristics, including their texture, shape, and intensity. This approach has proven particularly useful in identifying aggressive cancers and predicting the risk of tumour progression. For instance, radiomics has successfully predicted tumour upgrades from lower-risk classifications to more aggressive categories following surgical procedures. AI applications are also making notable progress in pathology. Automated systems for grading prostate cancer biopsies reduce subjectivity and variability inherent in traditional manual assessments, resulting in more consistent and accurate evaluations. Recent studies demonstrate that these systems can perform at levels comparable to experienced pathologists, offering potential as both primary and supplementary diagnostic tools. The use of AI extends beyond diagnosis into treatment planning, including surgery and radiation therapy. AI algorithms now assess surgical skill levels and predict patient outcomes based on surgical videos, facilitating objective assessments of surgical quality and efficiency. Similarly, AI-driven radiation therapy planning tools significantly shorten treatment planning times, demonstrating clear potential to improve clinical workflow and reduce healthcare costs. Despite these promising advancements, challenges remain. The effectiveness of AI systems relies heavily on the quality and diversity of the data used in training these algorithms. Most AI models, particularly those used outside of initial cancer detection, have been tested only in smaller datasets, raising concerns about their reliability in varied clinical settings. Moreover, the integration of AI into everyday clinical practice still faces hurdles, including clinicians’ trust in automated systems and ethical considerations regarding data use. But the trajectory remains positive. As larger and more diverse datasets become available, AI’s accuracy and reliability are expected to increase, ultimately enabling personalised treatments tailored specifically to individual patients’ cancer profiles and risk levels. Future research, as highlighted by the review, should prioritise expanding data availability, addressing potential biases in AI algorithms, and enhancing transparency to encourage clinical acceptance. With continued development, AI has the potential to make prostate cancer diagnosis more precise, treatment more personalised, and outcomes significantly better, marking a transformative moment in cancer care.