Abstract
The deployment of artificial intelligence (AI) and machine learning (ML) in diagnostic radiology has been slowed by issues of effectiveness, trust, economics, and regulation. We believe much of this can be mitigated by introducing a mechanism and infrastructure that support labeling of imaging studies at scale, automate cohort creation for algorithm training, provide real-world feedback on algorithms in development, and perform quality assurance and monitoring of deployed algorithms. This can be achieved by using interactive reporting technology as radiologists routinely interpret cases without burdening them.
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