A bibliometric analysis of using machine learning and artificial intelligence in prostate cancer detection

Prostate cancer stands as one of the most prevalent cancers globally among men, exhibiting substantial geographical variations in both incidence and mortality. While developed countries bear a higher incidence, developing countries grapple with elevated mortality rates. The heightened mortality in the latter is attributed to variations in practices that impede early diagnosis. In this context, the integration of artificial intelligence (AI) and machine learning (ML) has become increasingly common to improve the diagnostic accuracy of prostate cancer. This review delves into the existing literature to scrutinize the utilization of AI and ML in the diagnosis of prostate cancer. To compile relevant literature, comprehensive searches were conducted on research databases, including SCOPUS, Web of Science, and Google Scholar, to identify articles related to AI or ML (AI/ML) in the diagnosis and management of prostate cancer. Using a screening criterion, 293 reviewed research papers were identified. The two most consistent themes were predictive modeling and the application of AI/ML tools for cancer grading and radiomics. AI and ML enhance diagnostic accuracy by reducing inter-individual