This session from Sentikon examines why the foundation of effective healthcare AI lies in the quality, relevance, and governance of data. Dr. Woodhouse outlines the principles that determine whether AI models succeed in real clinical environments or fail at deployment.
You’ll learn:
- Why poor data selection leads to biased or unsafe AI outcomes
- Key challenges and best practices in real-world data sourcing
- How to ensure data is fit-for-purpose and regulator-ready
- Lessons from case studies where data quality determined success or failure