A Narrative Review of Artificial Intelligence in MRI-Guided Prostate Cancer Diagnosis: Addressing Key Challenges

• Published in: Diagnostics (Basel) Vol. 15; no. 11; p. 1342
• Main Authors: Alis, Deniz, Onay, Aslihan, Colak, Evrim, Karaarslan, Ercan, Bakir, Baris
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 26.05.2025; MDPI
• Subjects: Accuracy; Algorithms; Artificial intelligence; artificial intelligence (AI); Biopsy; Cancer; Deep learning; deep learning (DL); Diagnosis; Machine learning; machine learning (ML); Magnetic resonance imaging; magnetic resonance imaging (MRI) of prostate; Medical imaging equipment; Mortality; Neural networks; Prostate cancer; prostate cancer (PCa); Quality standards; Reproducibility; Review; Turkey; machine learning (ML); prostate cancer (PCa); artificial intelligence (AI); magnetic resonance imaging (MRI) of prostate; deep learning (DL)
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics15111342

Background/Objectives: Magnetic resonance imaging (MRI) is crucial in detecting suspicious lesions and diagnosing clinically significant prostate cancer (csPCa). However, variability in MRI-targeted diagnostic pathways arises due to factors such as patient characteristics, imaging protocols, and radiologist expertise. Artificial intelligence (AI) offers potential solutions to these challenges by enhancing diagnostic accuracy and efficiency. Methods: This narrative review explores AI techniques, particularly machine learning and deep learning, in the context of prostate cancer diagnosis. It examines their application in improving MRI scan quality, detecting artifacts, and assisting radiologists in lesion detection and interpretation. It also considers how AI helps to reduce reading time and inter-reader variability. Results: AI has demonstrated sensitivity that is generally comparable to experienced radiologists, although specificity tends to be lower, potentially increasing false-positive rates. The clinical impact of these results requires validation in larger, prospective multicenter studies. AI is effective in identifying artifacts, assessing MRI quality, and assisting in diagnostic efficiency by providing second opinions and automating lesion detection. However, variability in study methodologies, datasets, and imaging protocols can impact AI’s generalizability, limiting its broader clinical application. Conclusions: While AI shows significant promise in enhancing diagnostic accuracy and efficiency for csPCa detection, challenges remain, particularly with the generalizability of AI models. To improve AI robustness and integration into clinical practice, multicenter datasets and transparent reporting are essential. Further development, validation, and standardization are required for AI’s widespread clinical adoption.