REVIEW article
Front. Immunol.
Sec. Systems Immunology
This article is part of the Research TopicArtificial intelligence shapes the antibody/DNA/RNA-based diagnostics and therapeuticsView all 3 articles
Utilization of Artificial Intelligence in Prostate Cancer Detection: A Comprehensive Review of Innovations in Screening and Diagnosis
Provisionally accepted
- 1Taibah University College of Medicine, Medina, Saudi Arabia
- 2Taibah University, Medina, Saudi Arabia
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Prostate cancer management has long been challenged by the limitations of traditional screening tools like PSA testing, which contribute to significant rates of overdiagnosis and overtreatment. While advanced imaging such as multiparametric MRI (mpMRI) has improved the diagnostic pathway, the integration of Artificial Intelligence (AI) is now catalyzing a paradigm shift across the entire continuum of care. This comprehensive review details the transformative role of AI in prostate cancer. In diagnostics, deep learning algorithms enhance the interpretation of mpMRI by improving lesion detection, segmentation, and risk stratification, thereby reducing unnecessary biopsies. In digital pathology, AI provides automated and consistent Gleason grading, minimizing inter-observer variability and refining prognostication. In the therapeutic domain, AI is crucial for personalizing treatment by streamlining radiotherapy planning through automated contouring, predicting patient outcomes and toxicity, and enabling the development of adaptive therapy strategies for advanced disease. Multimodal AI models that synthesize imaging, biomarker, and clinical data are creating robust predictive tools for superior clinical decision support. Despite formidable challenges related to prospective validation, data equity, and regulatory approval, AI is paving the way for a new standard of care characterized by greater precision, efficiency, and personalization.
Keywords: artificial intelligence, prostate cancer, medical imaging, deep learning, personalized medicine
Received: 05 Aug 2025; Accepted: 17 Nov 2025.
Copyright: © 2025 Rajih, Bakhsh, Borhan and Alqahtani. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Saeed Awad M. Alqahtani, dr_alqahtani@hotmail.com
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