XNAT Scout: Enabling Translational AI
To Know
About this Class
Attend this webinar to learn more about XNAT Scout—a new extension of the XNAT imaging informatics platform that’s designed to close the gap between artificial intelligence (AI) model development and clinical deployment.
Washington University’s Dr. Daniel Marcus will introduce XNAT Scout’s architecture, key capabilities, and early deployment experiences. XNAT Scout provides structured tools for assembling training cohorts, managing annotations, benchmarking models, and monitoring performance over time. Integrated with XNAT’s mature imaging workflows and governance frameworks, it enables reproducible validation, multi-site collaboration, and deployment pathways aligned with clinical interoperability and security requirements. By unifying data curation, evaluation, and operationalization in one platform, XNAT Scout accelerates translation and supports health systems in safely adopting AI at scale.
XNAT is a a globally used, open-source imaging informatics platform funded by the NCI Informatics Technology for Cancer Research (ITCR) program.