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As life sciences organizations implement more artificial intelligence and machine learning in their R&D processes, they rely on the services of expert readers to label and annotate their medical imaging assets for downstream analysis. But administering medical imaging workflows—assigning images to readers, adjudicating the resulting data, and keeping this work efficient and compliant—is no simple feat, particularly at the scale necessary for machine learning.
To date, research organizations have assembled various approaches for tackling this work. Many common tools used in reader studies are built for only a few rigid purposes, requiring researchers to invest significant upfront time creating data-capture systems external to the viewer. Once the data-capture system is built, readers must be trained to ensure accurate and adequate readings. Maintaining compliance is also a vital concern when research teams are collaborating on medical imaging.
In order to perform these studies at scale, researchers should be able to design their own custom medical imaging workflows without coding knowledge and with the confidence they are staying in compliance. Ideally, the result of their efforts is that readers are guided through their labeling workflow inside the viewer in a way that is intuitive and instructional while also providing beneficial data quality validation steps. Once reader data is collected, research teams then need additional tools to assist in adjudication, data standardization, and subsequent research workflows.
Join Flywheel’s experts for a look at the state of reader workflows and how annotation and labeling can work at scale in leading life sciences and clinical organizations. What is required to make this work efficient, and what research breakthroughs can it enable?
Dan Rafter, MD; Director of Product Management, Flywheel
Aaron Mintz, MD; Medical Director, Flywheel