An overview of utilizing artificial intelligence in localized prostate cancer imaging.

Abstract

Introduction: Prostate cancer (PCa) is a leading cause of cancer-related deaths among men, and accurate diagnosis is critical for effective management. Multiparametric MRI (mpMRI) has become an essential tool in PCa diagnosis due to its superior spatial resolution which enables detailed anatomical, functional information and its resultant ability to detect clinically significant PCa. However, challenges such as subjective interpretation methods and high inter-reader variability remain. In recent years, artificial intelligence (AI) has emerged as a promising solution to enhance the diagnostic performance of mpMRI by automating key tasks such as prostate segmentation, lesion detection, classification.

Areas covered: This review provides a comprehensive overview of the current AI applications in prostate mpMRI, discussing advancements in automated image analysis and how AI-driven models are developed to improve detection and risk stratification. A literature search was conducted to examine both machine learning and deep learning techniques applied in this field, highlighting key studies and future directions.

Expert opinion: While AI models have shown significant promise, their clinical integration remains limited due to the need for larger, multi-institutional validation studies. As AI continues to evolve, multimodal approaches combining imaging with clinical data are likely to play pivotal role in personalized PCa diagnosis, treatment planning.