Real-World Monitoring of Artificial Intelligence in Radiology: Challenges and Best Practices

Abstract

The integration of artificial intelligence (AI) into radiology has the potential to enhance diagnostic accuracy, streamline workflows, and improve patient outcomes. However, successful real-world adoption hinges on robust systems for ongoing monitoring to maintain safety, efficacy, and compliance with regulatory standards. This article delves into the critical need for such monitoring in radiology, examining current regulatory frameworks and proposing actionable strategies for overseeing technical performance, algorithm reliability, and human-AI interactions. Key topics include methods for aligning imaging studies with appropriate AI tools, addressing challenges related to data transmission and processing delays, and evaluating approaches to algorithm performance monitoring, ranging from vendor-based and specialized systems to in-house solutions. The potential of using large language models to help algorithm monitoring is also highlighted as a promising avenue. Additionally, the article explores human-AI interaction challenges, such as automation bias (the tendency of users to overly trust automated decisions), misuse, and underuse, offering strategies to mitigate these risks through structured protocols and ongoing education. By aligning regulatory requirements with practical implementation strategies, comprehensive AI monitoring can optimize diagnostic decision-making while ensuring patient safety.

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