How image analysis competitions can promote faster, more collaborative AI research


The rise of AI in medical imaging has paved way for the improvement of workflow standardization, consistency and dependability imaging providers need in order to achieve the best patient care. However, as when implementing any new kind of technology into clinical workflows, there are challenges. In a special report published Jan. 30 in the inaugural issue of Radiology: Artificial Intelligence, Luciano M. Prevedello, MD, and chief of imaging informatics at The Ohio State University Wexner Medical Center in Columbus, Ohio, and colleagues recognize these challenges, but also offer potential solutions--specifically image-based competitions--which could foster collaborative AI research. The authors noted that although AI holds exciting opportunities for medical imaging, challenges related to data complexity, data access and curation, patient privacy, transferability of algorithms to mass markets and the integration of AI in clinical workflows must be addressed first in order to effectively bring AI to the forefront of augmenting patient care. "Readily available, well-curated, and labeled data of high quality is paramount to performing effective research in this area," Prevedello et al. wrote.