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.Th...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.展开更多
基金This review was supported by the National Natural Science Foundation of China(Nos.82272905 and 82473385)the Natural Science Foundation of Science and Technology Commission of Shanghai(No.22ZR1478000).
文摘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.
