Prostate　cancer　(PCa)　is　a　common　malignancy　in　males.　The　pathology　review　of　PCa　is　crucial　for　clinical　decision　-making,　but　traditional　pathology　review　is　labor　intensive　and　subjective　to　some　extent.　Digital　pathology　and　whole　-slide　imaging　enable　the　application　of　artificial　intelligence　(AI)　in　pathology.　This　review　highlights　the　success　of　AI　in　detecting　and　grading　PCa,　predicting　patient　outcomes,　and　identifying　molecular　subtypes.　We　propose　that　AI　-based　methods　could　collaborate　with　pathologists　to　reduce　workload　and　assist　clinicians　in　formulating　treatment　recommendations.　We　also　introduce　the　general　process　and　challenges　in　developing　AI　pathology　models　for　PCa.　Importantly,　we　summarize　publicly　available　datasets　and　open　-source　codes　to　facilitate　the　utilization　of　existing　data　and　the　comparison　of　the　performance　of　different　models　to　improve　future　studies.