Objective:

To provide a comprehensive account of the current AI landscape in prostate MRI, focusing on capabilities, limitations, and future research priorities.

Approach:

• Introduction: Discusses the transition of AI from research to clinical use in prostate MRI, emphasizing the potential for improved detection of clinically significant prostate cancer (csPCa).
• State of the art in prostate MRI AI medical devices: Describes the shift from radiomics to deep learning in AI systems for prostate MRI, detailing applications in segmentation, lesion detection, and reporting.
• Segmentation and lesion detection: Highlights the effectiveness of deep-learning segmentation approaches and their impact on reducing unproductive biopsies and improving csPCa detection.
• Commercially available software and real-world performance: Examines the challenges of validating AIaMDs in real-world settings and the need for more robust evidence on their clinical efficacy.
• Explainability and transparency: Addresses the importance of understanding AI algorithms and their implications for clinical practice.

Key Findings:

• AI systems can match or exceed radiologists' performance in detecting clinically significant prostate cancer.
• Deep learning methods improve segmentation accuracy and reduce manual errors.
• Real-world evidence for AI as medical devices is limited, necessitating further research.

Interpretation:

The report emphasizes the need for ongoing evaluation of AI capabilities and the importance of addressing validation and regulatory challenges.

Limitations:

• Limited real-world validation data for AI as medical devices.
• Regulatory requirements may not ensure clinical validity.
• Performance variability based on application and interaction models.

Conclusion:

The document outlines priorities for future research and development in AI applications for prostate MRI.

Sources:

• ESUR Prostate MRI Working Group