Abstract:
Background. Current Prostate cancer screening methods have major drawbacks, such as
high rates of false-positive (70%), failure to detect clinically relevant tumors (20-30%), and
considerable inter-observer variability. Artificial intelligence holds promise to enhance
outcomes in the entire screening process.
Objective(s). To critically examine AI integration in prostate cancer screening, analyzing its
diagnostic performance, clinical relevance, and implementation challenges to guide
informed, evidence-based adoption.
Materials and methods. A narrative review using a systematic search of major databases
(PubMed/MEDLINE, Scopus, Google Scholar) following the SANRA guidelines. Studies
focusing on AI applications in PSA analysis, MRI interpretation, Histopathology, and multimodal diagnostic methods from 2018 - 2025 were included. Study quality assessment used
adapted QUADAS-2 criteria.
Results. AI outperformed traditional approaches across all diagnostic modalities. PSA
Analysis (0.82-0.89 vs 0.59-0.63 for conventional methods), digital pathology (97.4%
sensitivity,94.8% specificity), and MRI methods (AUC 0.91 vs 0.86 for radiologists). The
FDA-approved Paige system enhanced pathologist sensitivity by 8% while preserving
specificity. In the PI-CAI trials (n=10207 MRIs), AI diagnosed 6.8% more clinically relevant
tumors at equivalent specificity. Combined AI systems improved risk stratification by over
13-15%. Economic analysis also showed cost-effectiveness and its potential to reduce
diagnostic workload by 60-80%.
Conclusion(s). AI is clinically ready for prostate cancer screening, supported by extensive
validation studies. Effective implementation depends on infrastructure, workflow
integration, and clinician training. AI can enhance diagnostic accuracy and reduce unwanted
procedures, advancing prostate cancer screening.
