Abstract
This review investigates the effectiveness of exploiting the massive Artificial Intelligence (AI) technology in the diagnosis of prostate cancer histopathological images. It focuses on studying and analyzing the current state and practice for utilizing AI tools including the significant machine learning and deep learning models in the histopathological image analysis process. The PRISMA methodology was adopted for conducting this systematic review to include recent research articles that have been published since 2017. Leveraging novel deep learning models and advanced imaging techniques, AI demonstrates promising capabilities in improving accuracy and efficiency in detecting and classifying prostate cancer. A comprehensive comparison of existing works has been presented with in-depth discussions around current limitations and key challenges, while proposing some future advancements. This study aims to pave the way for future research and further integration of AI into the diagnostic processes towards early detection, personalized treatment strategies, and enhanced patient outcomes in the context of prostate cancer diagnosis.
