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

Area of Science:

• Radiology
• Oncology
• Medical Imaging

Background:

• Magnetic resonance imaging (MRI) is vital for prostate cancer detection.
• Diagnostic variability exists due to patient factors, imaging protocols, and radiologist expertise.
• Artificial intelligence (AI) offers solutions to enhance diagnostic accuracy and efficiency.

Purpose of the Study:

• To review AI techniques (machine learning, deep learning) for prostate cancer diagnosis.
• To examine AI's role in improving MRI quality, artifact detection, and lesion interpretation.
• To assess AI's impact on reducing reading time and inter-reader variability.

Main Methods:

• Narrative review of AI applications in prostate cancer MRI.
• Exploration of AI for image quality enhancement and artifact detection.
• Analysis of AI's role in lesion detection, interpretation, and workflow efficiency.

Main Results:

• AI demonstrates sensitivity comparable to experienced radiologists, with potential for increased false positives due to lower specificity.
• AI effectively identifies artifacts, assesses MRI quality, and aids diagnostic efficiency.
• Generalizability of AI models is limited by variability in study methodologies and datasets.

Conclusions:

• AI holds significant promise for enhancing prostate cancer detection accuracy and efficiency.
• Challenges remain in AI model generalizability, requiring multicenter validation.
• Further development, validation, and standardization are crucial for AI's clinical integration.