As the conversation regarding AI in healthcare in general, and medical imaging in particular, turns from suspicion to the nuts and bolts of integration into existing workflows, and measuring benefits for patients, providers, and payers, questions about how to go about starting AI projects in areas such as radiomics and pathology are becoming commonplace. In addition, recent research shows that the democratization of AI research is not just a nice concept, but rather a requirement for AI to be of benefit to patients at a national and global scale -- particularly to historically underserved populations.
This presentation will focus on the the importance of viewing healthcare data as an asset, the importance of starting imaging AI projects in the first place, and the importance of IT infrastructure in enabling those AI projects to become clinically useful.
How the proper infrastructure can enable the translation of AI from research to clinical care
How a data hub can accelerate the patient-care benefits of AI
How to avoid "GPU starvation"