Navigating the Frontier of artificial intelligence implementation in radiology - part 1: Performance assessment

Area of Science:

• Radiology
• Artificial Intelligence
• Medical Imaging

Background:

• Despite significant growth in AI research and investment in medical imaging, clinical translation remains limited.
• A critical gap exists due to the lack of standardized guidelines for evaluating AI model performance and ethical considerations.

Purpose of the Study:

• To provide a practical perspective on the performance challenges hindering AI implementation in radiology.
• To explore mitigation strategies for these challenges, focusing on data heterogeneity, performance metrics, and data access.

Main Methods:

• Exploration of challenges in AI performance evaluation within radiology.
• Focus on data heterogeneity, choice and interpretability of performance metrics, and data access issues.
• Discussion of potential opportunities for overcoming these hurdles.

Main Results:

• Identified key performance evaluation challenges for AI in radiology, including data variability and metric interpretation.
• Highlighted the need for improved data access and standardized performance metrics.
• Emphasized the gap between AI's theoretical potential and its practical clinical application.

Conclusions:

• Addressing performance evaluation challenges is crucial for the practical integration of AI in clinical radiology.
• Established guidelines are imperative for the safe and efficient deployment of AI in medical imaging.
• Bridging the gap between AI potential and clinical implementation requires focused efforts on performance and data standardization.