{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:56:01Z","timestamp":1778082961595,"version":"3.51.4"},"reference-count":71,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T00:00:00Z","timestamp":1762128000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Prostate cancer is one of the leading causes of cancer-related morbidity and mortality worldwide, and imaging plays a critical role in its detection, localization, staging, treatment, and management. The advent of artificial intelligence (AI) has introduced transformative possibilities in prostate imaging, offering enhanced accuracy, efficiency, and consistency. This review explores the integration of AI in prostate cancer diagnostics across key imaging modalities, including multiparametric MRI (mpMRI), PSMA PET\/CT, and transrectal ultrasound (TRUS). Advanced AI technologies, such as machine learning, deep learning, and radiomics, are being applied for lesion detection, risk stratification, segmentation, biopsy targeting, and treatment planning. AI-augmented systems have demonstrated the ability to support PI-RADS scoring, automate prostate and tumor segmentation, guide targeted biopsies, and optimize radiation therapy. Despite promising performance, challenges persist regarding data heterogeneity, algorithm generalizability, ethical considerations, and clinical implementation. Looking ahead, multimodal AI models integrating imaging, genomics, and clinical data hold promise for advancing precision medicine in prostate cancer care and assisting clinicians, particularly in underserved regions with limited access to specialists. Continued multidisciplinary collaboration will be essential to translate these innovations into evidence-based practice. This article explores current AI applications and future directions that are transforming prostate imaging and patient care.<\/jats:p>","DOI":"10.3390\/jimaging11110390","type":"journal-article","created":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T16:18:42Z","timestamp":1762186722000},"page":"390","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Next-Generation Advances in Prostate Cancer Imaging and Artificial Intelligence Applications"],"prefix":"10.3390","volume":"11","author":[{"given":"Kathleen H.","family":"Miao","sequence":"first","affiliation":[{"name":"Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA"}]},{"given":"Julia H.","family":"Miao","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Chicago Medicine, Chicago, IL 60637, USA"}]},{"given":"Mark","family":"Finkelstein","sequence":"additional","affiliation":[{"name":"Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4022-7106","authenticated-orcid":false,"given":"Aritrick","family":"Chatterjee","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Chicago Medicine, Chicago, IL 60637, USA"}]},{"given":"Aytekin","family":"Oto","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Chicago Medicine, Chicago, IL 60637, USA"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,3]]},"reference":[{"key":"ref_1","first-page":"209","article-title":"Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J. 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