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This review explores recent advances in histopathology\u2010based AI systems for prostate cancer. We examine how these algorithms perform relative to pathologists for tumour diagnosis and grading, and the ways in which they surpass pathologists with respect to reducing inter\u2010observer variability and providing quantified tumour metrics. We particularly focus on prognostic algorithms that have been benchmarked against \u2018gold standard\u2019 patient outcomes such as metastasis or death, and we highlight the emerging role of digital pathology\u2010enabled AI for predicting response to therapy or underlying tumour molecular alteration status. Finally, we touch on the advantages of, and barriers to, implementation of digital pathology and histopathology\u2010based AI algorithms in clinical practice. 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