Prostate cancer (PCa) diagnosis has historically relied on the prostate-specific antigen (PSA) testing. Although the screening significantly reduces mortality rates, PSA has low specificity with risks of overdiagnosis and overtreatment. These limitations highlight the need for a more accurate diagnostic approach. Emerging technologies, such as artificial intelligence (AI), novel biomarkers, and advanced imaging techniques, offer promising avenues to enhance the accuracy and efficiency of PCa diagnosis and risk stratification. This narrative review comprehensively analyzed the current literature, focusing on new tools aiding PCa diagnosis (AI-driven image interpretation, radiomics, genomic classifiers, biomarkers, and multimodal data integration) with consideration for technical, regulatory, and ethical challenges related to clinical implementation of AI-based technologies. A literature search was performed using the PubMed and MEDLINE databases to identify relevant peer-reviewed articles published in English using the search terms “prostate cancer,” “artificial intelligence,” “machine learning,” “deep learning,” “MRI,” “histopathology,” and “diagnosis.” Articles were selected based on their relevance to AI-assisted diagnostic tools, clinical utility, and performance metrics. In addition, a separate section was developed initially to contextualize the limitations of current PSA-based screening approaches. The reviewed studies showed that AI had significant utility in prostate mpMRI interpretation (lesion detection; Gleason grading) with high accuracy and high reproducibility. For the pathologist, AI-driven algorithms improve the diagnostic accuracy of digital slide evaluation for histologic diagnosis of prostate cancer and automated Gleason score grading. Genomic tools such as the Oncotype DX test, combined with AI, could also allow for tailored and individualized risk prediction. Overall, multimodal models integrating clinical, imaging, and molecular data often outperform traditional PSA-based strategies and reduce unnecessary biopsies. Transition from PSA-centered toward AI-driven, biomarker-supported, and image-enhanced diagnosis marks a critical evolution in PCa diagnosis.

Background: Prostate cancer (PCa) diagnosis has historically relied on prostate-specific antigen (PSA) testing. Although PSA screening significantly reduces mortality rates, it is limited by its low specificity and the risk of overdiagnosis and overtreatment. These limitations highlight the need for more accurate diagnostic approaches that can be combined with PSA testing. Emerging technologies, such as artificial intelligence (AI), novel biomarkers, and advanced imaging techniques, offer promising avenues to enhance the accuracy and efficiency of PCa diagnosis and risk stratification. Materials and methods: This review comprehensively analyzes the current literature on the use of AI, machine learning, novel biomarkers, and imaging tools, particularly multiparametric magnetic resonance imaging and digital pathology, for the diagnosis of PCa. Studies on AI-driven image interpretation, lesion segmentation, radiomics, genomic classifiers, and multimodal data integration were evaluated. This study also considers the technical, regulatory, and ethical challenges related to the clinical implementation of AI technologies. Results: Artificial intelligence demonstrated significant utility in multiparametric magnetic resonance imaging interpretation, enhancing lesion detection, segmentation, and Gleason grading with high accuracy and reproducibility. In pathology, AI algorithms improve the diagnostic consistency of digital slides and assist with automated Gleason scoring. Genomic tools, such as Oncotype DX, when combined with AI, allow for individualized risk prediction. Multimodal models that integrate imaging, clinical, and molecular data outperform traditional PSA-based strategies and reduce unnecessary biopsies. Conclusions: The transition from PSA-centered to AI-driven, biomarker-supported, image-enhanced diagnosis marks a critical evolution in PCa care. While these technologies promise improved diagnostic accuracy compared with that with PSA alone, PSA will remain a foundation for model construction and risk stratification. Personalized treatment strategies and the successful clinical integration of AI depend on harmonized regulations, large-scale validation, equitable access, and transparent algorithm design. Future screening and treatment pathways for PCa are likely to be shaped by these multimodal precision diagnostic frameworks.

Background: Oligometastatic prostate cancer (OMPC) comprises biologically and clinically distinct states with a limited number of detectable metastases. Improved systemic therapy and prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA PET/CT) have intensified interest in treating the primary tumor. Methods: In this narrative review we searched PubMed/MEDLINE from inception to 1 July 2026, reviewed current guidelines, and searched ClinicalTrials.gov, the ISRCTN registry and the EU Clinical Trials Register for ongoing surgical studies, prioritizing randomized trials, prospective studies, PSMA PET-staged cohorts, systematic reviews, guidelines, and studies of perioperative, functional and patient-reported outcomes. Results: Registry and institutional data generated the hypothesis that cytoreductive prostatectomy improves outcomes but are vulnerable to selection bias; prospective and PSMA PET-staged cohorts established feasibility. The phase II trial by Dai and colleagues supported radical treatment of the primary tumor but combined surgery and radiotherapy. RAMPP provided the first prostatectomy-specific randomized signal, with lower 5-year cancer-specific mortality after radical prostatectomy plus best systemic therapy than after systemic therapy alone (13% versus 23%; hazard ratio 0.39, 95% confidence interval 0.16–0.98), but accrual ended early, clinical progression was similar, overall survival was not significantly improved, systemic therapy predated contemporary intensification, and major surgery-related complications occurred in 14%. A randomized phase II trial with a predominantly surgical local-therapy arm found no progression-free or overall survival benefit, so randomized evidence is mixed. All randomized evidence concerns radical prostatectomy or local treatment irrespective of surgical approach; no trial has tested a robot-specific oncologic effect. Guidelines recommend prostate radiotherapy for selected low-volume de novo metastatic disease and restrict surgery to clinical trials. Conclusions: Cytoreductive prostatectomy, delivered mostly as robot-assisted radical prostatectomy (RARP), is feasible and may improve local control while providing complete pathology, but it remains investigational; the robotic approach is the surgical platform, not a modality with distinct oncologic efficacy. Appropriate use requires expert-center surgery, modern staging, guideline-concordant systemic therapy, multidisciplinary selection, transparent counseling and, preferably, trial participation. Future trials must establish whether prostatectomy adds meaningful benefit beyond modern systemic therapy, prostate radiotherapy and metastasis-directed treatment, while measuring morbidity, continence and quality of life.