Currently, the Enterprise Radiology Framework for AI Software Technology (FAST) Team has rolled out 17 internally developed, medical image-based algorithms, averaging over 10,000 algorithm runs weekly.
Recently, we have placed an increased focus on monitoring these algorithms as there are few reports with practical experience documented in the literature.
Our increased monitoring efforts include daily, weekly, and yearly monitoring of utilization, failure modes, data drift, and end-user feedback through automated alerts, dedicated dashboards, and pointed investigations to enable optimal algorithmic processing.
Automated monitoring has enabled earlier identification of problems, such as images no longer routing through the orchestration engine to the appropriate algorithm, minimizing potential disruption to the clinical practice and ensuring continued algorithmic utilization.
Monitoring has also reinforced the importance of key aspects of interdisciplinary research and translation such as early discussions on clinical needs coupled with technological ability and proper training.
By providing our experience in and continuing to improve monitoring methods as a community, we can all minimize risk and maximize the benefits of medical pixel-based AI.
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