Exploring the Use of Artificial Intelligence in the Management of Prostate Cancer

Abstract

Purpose of Review This review aims to explore the current state of research on the use of artificial intelligence (AI) in the management of prostate cancer. We examine the various applications of AI in prostate cancer, including image analysis, prediction of treatment outcomes, and patient stratification. Additionally, the review will evaluate the current limitations and challenges faced in the implementation of AI in prostate cancer management. Recent Findings Recent literature has focused particularly on the use of AI in radiomics, pathomics, the evaluation of surgical skills, and patient outcomes. Summary AI has the potential to revolutionize the future of prostate cancer management by improving diagnostic accuracy, treatment planning, and patient outcomes. Studies have shown improved accuracy and efficiency of AI models in the detection and treatment of prostate cancer, but further research is needed to understand its full potential as well as limitations.

Novel Histopathological Biomarkers in Prostate Cancer: Implications and Perspectives

“…It is also worth noting that new technologies such as artificial intelligence (AI) and machine learning (ML) are emerging as powerful tools in the field of prostate cancer diagnosis and management as well as in the analysis of histopathological samples [ 228 , 229 ]. ML algorithms, a subset of AI, can be trained on large datasets of medical images and clinical data to help clinicians make more accurate and informed decisions regarding treatment options for prostate cancer patients.…”

ChatGPT for medical applications and urological science

“…By inputting data from urological studies or clinical trials, ChatGPT can generate text that provides insights into the findings, significance, and implications of the data. This can be particularly helpful for uro-logical scientists who need to communicate their research findings in a clear and concise way ( 13 ).…”

Research on texture images and radiomics in urology: a review of urological MR imaging applications

“…Moreover, overall 80% of CaP detected may be csCaP vs insignificant (nsCaP). The use of radiomics in this setting has been investigated since several years (recent reviews in [9 ▪ ,10 ▪ ]) with an AUC about 80%. Very recently, Li et al studied 100 CaP and 136 BPH patients using radiomics and deep learning (DL) models [11 ▪ ].…”

“…In the same way, since several years, MRI-based radiomics have been widely studied for GG prediction, with an AUC ranging 70–85% (review in [9 ▪ ,10 ▪ ]). The explored thematics were utility of the different MRI sequences (T2WI, DWI, ADC) with conflicted results (unique vs. combined), as well as the type of the delineated ROIs, i.e.…”