Artificial Intelligence in the Assessment of Males with Chronic Pelvic Pain Syndrome: An Up-to-Date UPOINTS-Based Narrative Mapping Review

Background/Objectives: Male chronic pelvic pain syndrome (CPPS) is a heterogeneous condition involving overlapping urinary, psychosocial, organ-specific, infectious, neurological, myofascial, and sexual phenotypes. This complexity limits the effectiveness of routine symptom assessment and empirical treatment strategies. Artificial intelligence (AI) may support more reproducible interpretation, differential diagnosis, phenotyping, and personalized management. This up-to-date narrative mapping review aimed to identify AI-assisted approaches that are directly or indirectly relevant to the assessment of males with CPPS, classify them according to UPOINTS phenotypic domains and clinical functions, and critically discuss the extent to which current evidence is disease-specific or extrapolated from related conditions. Methods: A literature search was performed in PubMed/MEDLINE, the Cochrane Library, and Google Scholar from database inception to May 2026 using terms related to male CPPS, UPOINTS domains, diagnosis, treatment, prognosis, digital solutions, artificial intelligence, machine learning, deep learning, natural language processing, computer vision, and decision support. Studies were included if they described AI-based or AI-adjacent computational approaches relevant to male CPPS or to related conditions important for UPOINTS-based phenotyping, differential diagnosis, or phenotype-specific assessment. Results: Available evidence remains fragmented and is largely extrapolated from related urological, chronic pain, pelvic floor, infectious, neurological, and sexual medicine conditions. AI applications were most developed in urinary and organ-specific domains, including uroflowmetry analysis, bladder volume assessment, cystoscopy, prostate imaging, urinary biomarkers, and differential diagnosis of lower urinary tract disorders. AI tools also showed potential for infection detection, psychosocial screening, chronic pain monitoring, neuroimaging-based phenotyping, pelvic floor dysfunction assessment, and evaluation of sexual dysfunction. However, male-CPPS-specific validation remains limited. Conclusions: AI has promising potential to improve differential diagnosis, multidomain phenotyping, and individualized management in males with CPPS. Current evidence is mainly translational and hypothesis-generating. Future studies should focus on prospective male-CPPS-specific cohorts, external validation, explainable multimodal models, and integration of AI tools into clinically meaningful, patient-centered workflows.