Prostate cancer (PCa) ranks as the second most prevalent malignancy among men worldwide. Early diagnosis, personalized treatment, and prognosis prediction of PCa play a crucial role in improving patients' survival rates. The advancement of artificial intelligence (AI), particularly the utilization of deep learning (DL) algorithms, has brought about substantial progress in assisting the diagnosis, treatment, and prognosis prediction of PCa. The introduction of the foundation model has revolutionized the application of AI in medical treatment and facilitated its integration into clinical practice. This review emphasizes the clinical application of AI in PCa by discussing recent advancements from both pathological and imaging perspectives. Furthermore, it explores the current challenges faced by AI in clinical applications while also considering future developments, aiming to provide a valuable point of reference for the integration of AI and clinical applications.
Humans ; Prostatic Neoplasms/diagnosis* ; Male ; Artificial Intelligence ; Deep Learning ; Prognosis

Artificial intelligence (AI) has important significance and great promise in the pathological diagnosis,imaging diagnosis,prognosis prediction,and molecular subtyping of prostate cancer (PCa). This review focuses on the progress of AI for the diagnosis and molecular classification of PCa,and briefly introduces the application of AI in the pathological diagnosis of needle biopsy and Gleason grading,pathological diagnosis and grading after prostatectomy,and prognosis prediction of PCa patients based on pathological sections. For the pathological diagnosis of needle biopsy and Gleason grading,AI has already comparable to general pathologists;for the pathological diagnosis and grading after prostatectomy,AI can accurately grade and classify tumors;and for the prognosis prediction of PCa patients,AI can directly extract relevant prognostic information from pathological tissue sections for prognosis prediction. In addition,AI can also predict gene mutations in PCa patients and suggest the probability of gene mutation by analyzing the pathological sections.