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False negatives, false positives, and moderate inter\u2010reader agreement undermine reliability, while long acquisition times restrict throughput. Artificial intelligence (AI) offers potential solutions to address many of the limitations of prostate MRI in the clinical management pathway. Machine learning\u2013based triage can refine patient selection to optimize resources. Deep learning reconstruction enables accelerated acquisition while preserving diagnostic quality, with multiple FDA\u2010cleared products now in clinical use. Ongoing development of automated quality assessment and artifact correction aims to improve reliability by reducing nondiagnostic exams. In image interpretation, AI models for lesion detection and clinically significant prostate cancer prediction achieve performance comparable to radiologists, and the PI\u2010CAI international reader study has provided the strongest evidence to date of non\u2010inferiority at scale. More recent work extends MRI\u2010derived features into prognostic modeling of recurrence, metastasis, and functional outcomes. This review synthesizes progress across five domains\u2014triage, accelerated acquisition and reconstruction, image quality assurance, diagnosis, and prognosis\u2014highlighting the level of evidence, validation status, and barriers to adoption. While acquisition and reconstruction are furthest along, with FDA\u2010cleared tools and prospective evaluations, triage, quality control, and prognosis remain earlier in development. Ensuring equitable performance across populations, incorporating uncertainty estimation, and conducting prospective workflow trials will be essential to move from promising prototypes to routine practice. Ultimately, AI could accelerate the adoption of prostate MRI toward a scalable platform for earlier detection and population\u2010level prostate cancer management.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Evidence Level<\/jats:title>\n                    <jats:p>N\/A<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Technical Efficacy<\/jats:title>\n                    <jats:p>3<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jmri.70189","type":"journal-article","created":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T19:13:53Z","timestamp":1764962033000},"page":"617-630","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Artificial Intelligence in Prostate\n                    <scp>MRI<\/scp>\n                    : Addressing Current Limitations Through Emerging 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