AI-driven precision diagnosis and treatment of prostate cancer: a narrative review.

Authors

Tonghui Chu, Youzhao Zhang, Jianuo Du, Abudurexiti Mierxiati

Published in

Frontiers in oncology. Volume 16. Pages 1877570. Epub Jul 16, 2026.

Abstract

Prostate cancer is one of the most common cancers in men, and its incidence has been increasing annually. Early screening, accurate diagnosis, and personalized treatment are crucial for improving patient prognosis. With technological advancements, artificial intelligence (AI) has been increasingly applied as a key tool in the diagnosis and treatment of prostate cancer. In the field of diagnosis, AI-driven multi-modal models that integrate imaging, pathological, and clinical data have enabled precise segmentation and grading of prostate cancer. This not only enhances diagnostic accuracy and efficiency but also reduces differences in judgments between medical professionals. In treatment, AI has been integrated into surgical procedures, radiation therapy, and targeted drug development to optimize treatment plans and explore relevant biological indicators and treatment targets. Although various AI models have demonstrated clinical value, most have not been put into practical clinical use, which highlights the significant limitations of current AI technology. This review focuses on the practical application outcomes and future development directions of AI in prostate cancer diagnosis and treatment. This study aims to explore the potential of AI in clinical practice, promote its deep integration with clinical work, and construct reliable and safe diagnostic and therapeutic models. These efforts are expected to alleviate the workload of medical professionals, improve diagnostic accuracy, facilitate personalized treatment planning, and ultimately enhance patient prognosis and quality of life. The trend of intelligent medicine should be embraced, and AI-assisted diagnostic and therapeutic technologies should be actively and rationally adopted to promote the advancement of medical care.

PMID:
42534734
Bibliographic data and abstract were imported from PubMed on 31 Jul 2026.

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