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Current AI-driven research in radiology requires resources and expertise thatare often inaccessible to small and resource-limited labs. The clinicians whoare able to participate in AI research are frequently well-funded,well-staffed, and either have significant experience with AI and computing, orhave access to colleagues or facilities that do. Current imaging data isclinician-oriented and is not easily amenable to machine learning initiatives,resulting in inefficient, time consuming, and costly efforts that rely upon acrew of data engineers and machine learning scientists, and all too oftenpreclude radiologists from driving AI research and innovation. We present thesystem and methodology we have developed to address infrastructure and platformneeds, while reducing the staffing and resource barriers to entry. We emphasizea data-first and modular approach that streamlines the AI development anddeployment process while providing efficient and familiar interfaces forradiologists, such that they can be the drivers of new AI innovations.

An Orchestration Platform that Puts Radiologists in the Driver's Seat of AI Innovation: A Methodological Approach