The Application of Artificial Intelligence in Detecting Prostate Cancer with Medical Imaging: A Literature Review

REVIEW
Abdulrahman Tajaldeen

 

For citation: Tajaldeen A. The Application of Artificial Intelligence in Detecting Prostate Cancer with Medical Imaging: A Literature Review. International Journal of Biomedicine. 2026;16(3):294-305. doi:10.21103/Article16(3)_RA1

Originally published September 5, 2026

Abstract: 

Background: Prostate cancer (PCa) is the world's second most common cancer in men and a major cause of cancer death. Studies published from 2023 to 2026 have shown that the use of artificial intelligence (AI) can significantly enhance several imaging modalities for PCa detection, Gleason grading, and risk stratification, such as multiparametric magnetic resonance imaging (mpMRI), transrectal ultrasound (TRUS), micro-ultrasound, prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA-PET/CT), and digital pathology using whole-slide image (WSI) analysis. Although the potential of mpMRI guided by the Prostate Imaging Reporting and Data System (PI-RADS) is significant, considerable inter-reader variability still results in missed diagnoses and unnecessary biopsies. These limitations are mitigated by the development of AI-based systems, including deep learning (DL), machine learning (ML), and radiomics, which are designed to address them.
Methods and Results: The Web of Science and PubMed databases were searched on 3 May 2026 using a three-domain Boolean strategy encompassing PCa-specific, AI-specific, and imaging/pathology-specific terminology. A total of 4,791 records from Web of Science and 2,910 from PubMed were retrieved; following deduplication (n = 4,054), year filtering (2023–2026), and quality screening, n = 2,348 records remained eligible. Only studies using AI in medical imaging or digital pathology in PCa detection, grading, or staging were included in a narrative synthesis.
AI applications were identified across four imaging modalities: mpMRI, ultrasound/micro-ultrasound, PSMA-PET/CT, and digital pathology. Studies consistently illustrate that AI makes meaningful contributions to the detection and characterization of prostate cancer, provides high accuracy, sensitivity, and specificity across all modalities, and that several validated systems approach or equal the performance of trained radiologists and uropathologists.
Landmark studies, including the PI-CAI trial and the PANDA challenge, provided Level 1b evidence for AI in MRI-based csPCa detection and Gleason grading, respectively.
Conclusion: Across all reviewed modalities, AI contributes to the clinically meaningful detection, characterization, and grading of PCa. Widespread clinical integration depends on clinical transparency requirements and regulatory pathways that align with prospective multi-center, outcome-driven trials and standardized AI evaluation frameworks.

Keywords: 
prostate cancer, artificial intelligence, convolutional neural network, deep learning, machine learning, multiparametric MRI
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Received May 13, 2026.
Accepted July 2, 2026.
© 2026 The Author(s). International Journal of Biomedicine is published by IMRDC. This is an open access article under the CC BY-NC-ND 4.0 license.