Achieving high model accuracy is often the easiest part of the journey. The real challenge lies in transitioning a research-grade model into a regulated, AI-enabled medical device. I will share how we streamlined R&D by centralizing data assets and building cross-functional infrastructure to bridge the gap between data science, IT, regulatory, and medical teams. Industrializing our data processes and fostering deep internal collaboration allows us to move beyond standalone ML models to deliver genuine clinical utility and insights that integrate seamlessly into patient care.
