Published online May 28, 2023. doi: 10.4329/wjr.v15.i5.136
Peer-review started: December 19, 2022
First decision: February 20, 2023
Revised: March 21, 2023
Accepted: April 10, 2023
Article in press: April 10, 2023
Published online: May 28, 2023
Prostate cancer (Pca; adenocarcinoma) is one of the most common cancers in adult males and one of the leading causes of death in both men and women. The diagnosis of Pca requires substantial experience, and even then the lesions can be difficult to detect. Moreover, although the diagnostic approach for this disease has improved significantly with the advent of multiparametric magnetic resonance, that technology has certain unresolved limitations. In recent years artificial intelligence (AI) has been introduced to the field of radiology, providing new software solutions for prostate diagnostics. Precise mapping of the prostate has become possible through AI and this has greatly improved the accuracy of biopsy. AI has also allowed for certain suspicious lesions to be attributed to a given group according to the Prostate Imaging-Reporting & Data System classification. Finally, AI has facilitated the combination of data obtained from clinical, laboratory (prostate-specific antigen), imaging (magnetic resonance), and biopsy examinations, and in this way new regularities can be found which at the moment remain hidden. Further evolution of AI in this field is inevitable and it is almost certain to significantly expand the efficacy, accuracy and efficiency of diagnosis and treatment of Pca.
Core Tip: The peer reviewed literature has provided sufficient support for the continued application and development of artificial intelligence (AI) in prostate cancer clinical care. In addition, the expanding introduction of various AI-based software products created by leading companies is providing practical benefits to radiologists for improved prostate cancer diagnosis. Certainly, the known complexity of the disease and its consequential difficult diagnosis supports the continued development of new approaches for earlier and more accurate detection, such as could be provided through AI technologies.