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An infrastructure for multisite, geographically-distributed creation andcollection of diverse, high-quality, curated and labeled radiology image datais crucial for the successful automated development, deployment, monitoring andcontinuous improvement of Artificial Intelligence (AI)/Machine Learning (ML)solutions in the real world. An interactive radiology reporting approach thatintegrates image viewing, dictation, natural language processing (NLP) andcreation of hyperlinks between image findings and the report, provideslocalized labels during routine interpretation. These images and labels can becaptured and centralized in a cloud-based system. This method provides apractical and efficient mechanism with which to monitor algorithm performance.It also supplies feedback for iterative development and quality improvement ofnew and existing algorithmic models. Both feedback and monitoring are achievedwithout burdening the radiologist. The method addresses proposed regulatoryrequirements for post-marketing surveillance and external data. Comprehensivemulti-site data collection assists in reducing bias. Resource requirements aregreatly reduced compared to dedicated retrospective expert labeling.

A Multisite, Report-Based, Centralized Infrastructure for Feedback and Monitoring of Radiology AI/ML Development and Clinical Deployment