Introduction Prostate cancer, a major global health issue, necessitates early and precise detection for effective management. Traditional diagnostic methods like DRE, PSA tests, and biopsies have drawbacks in accuracy and safety. Treatment choices for prostate cancer are intricate, balancing benefits and risks. AI and ML have become instrumental in overcoming these challenges, enhancing diagnosis, imaging, and treatment decisions in prostate cancer research.

Methods A systematic literature review was conducted using PubMed, Embase, and Google Scholar, focusing on articles published from January 2016 to June 2021.

Results Our review reveals AI and ML significantly enhance prostate cancer diagnosis and management. AI and ML aid radiologists in identifying and characterizing prostate cancer in MRI scans with increased precision. For instance, Varghese et al. (2018) demonstrated high-accuracy prostate cancer classification using deep learning on MRI images. Shen et al. (2019) combined radiomics and machine learning to assess cancer aggressiveness on MRI. Beyond diagnosis, AI and ML refine treatment decision-making. Zhao et al. (2019) developed an ML-based decision support system to guide clinicians in choosing optimal treatments by analyzing patient and disease data against clinical guidelines and historical outcomes.

Conclusions AI and ML are set to transform prostate cancer diagnosis and management, showing significant improvements in accuracy and efficiency in diagnosis, imaging, and treatment planning.