Fans of data in health care often speculate about what clinicians and researchers could achieve by reducing friction in data sharing. What if we had easy access to group repositories, expert annotations and labels, robust and consistent metadata, and standards without inconsistencies? Since 2017, the Radiological Society of North America (RSNA) has been displaying a model for such data sharing.
That year marked RSNA's first AI challenge. RSNA has worked since then to make the AI challenge an increasingly international collaboration. Organizers of each challenge curate and annotate medical imaging studies and ask the research community to come up with models to answer important questions. I talked about the RSNA challenges and broader AI data collection efforts with Matthew P. Lungren, MD, MPH, who is widely recognized for his work on AI in radiology and is co-director of the Stanford Center for Artificial Intelligence in Medicine and Imaging. Read full article>>