About
As AI adoption accelerates in clinical practice, the need for robust safety and monitoring mechanisms has never been greater. In this session, we explore how platforms can play a central role in making AI safer and more reliable—before, during, and after deployment.
The discussion highlights a framework for AI quality assurance across three key phases: pre-deployment validation, live monitoring, and post-deployment surveillance. Topics include regulatory compliance, software reliability, population-specific validation, data quality checks, performance drift, and infrastructure readiness. The webinar also examines how hospitals can use independent testing, output guardrails, and deferral mechanisms to maintain clinical oversight and ensure AI remains aligned with both patient safety and operational value.
By addressing these challenges through a platform-based approach, institutions can better manage the complexity of deploying AI at scale and ensure that AI-supported decisions remain safe, accurate, and effective in real-world use.
Agenda
Speakers
M. Jorge Cardoso is Reader in Artificial Medical Intelligence at King’s College London, where he leads a research portfolio on big data analytics, quantitative radiology and value-based healthcare. Jorge is also the CTO of the new London Medical Imaging and AI Centre for Value-based Healthcare. He has more than 15 years expertise in advanced image analysis, big data, and artificial intelligence, and co-leads the development of project MONAI, a deep-learning platform for artificial intelligence in medical imaging.
