Artificial intelligence: redefining the future of prostate cancer diagnostics

Artificial intelligence (AI) is revolutionizing the diagnosis and management of prostate cancer (PCa), one of the most common cancers worldwide. Despite its high incidence, PCa's mortality rate remains relatively low, yet its heterogeneity poses significant diagnostic and therapeutic challenges. Clinicians face difficulties in distinguishing between indolent and aggressive forms of the disease, compounded by limitations in biomarkers and traditional diagnostic methods, such as serological markers, multiparametric MRI (mpMRI), and histopathological evaluation of prostate biopsies. AI offers innovative solutions by improving diagnostic precision, reducing interobserver variability, and streamlining workflows across multiple domains, including radiology, pathology, immunohistochemistry (IHC), and genomics. In radiology, AI-integrated systems enhance the interpretation of mpMRI, outperforming radiologists using the PI-RADS standard in identifying clinically significant PCa while minimizing false positives. Similarly, in pathology, AI algorithms refine tumor grading by accurately identifying Gleason patterns, perineural invasion, and other diagnostic features. Studies have demonstrated the ability of AI to serve as a second-read system, reducing workloads and supporting pathologists in delivering consistent, high-quality diagnoses. AI's role in IHC includes the evaluation of prognostic markers such as Ki-67 and PTEN, where it improves accuracy and aids in predicting patient outcomes. Tools like virtual multiplexing further advance IHC by enabling simultaneous analysis of multiple biomarkers without compromising morphological integrity. In genomics and proteomics, AI facilitates the identification of novel biomarkers using mass spectrometry, offering non-invasive diagnostic approaches and personalized therapeutic strategies. While AI demonstrates substantial potential in PCa diagnostics, it is not intended to replace clinicians but to serve as an invaluable adjunct. The integration of AI with standardized, diverse datasets and clinical workflows holds the promise of advancing PCa care through enhanced precision, efficiency, and patient outcomes.