Panel: Proving AI Safety Through Robust Validation Beyond Performance Metrics
- Understand why AUC, sensitivity, and specificity are insufficient to demonstrate clinical safety
- The “no gold standard” problem: disagreement among clinicians as a validation challenge
- Identifying bias, edge cases, and underrepresented populations in training/testing data
- Integrating AI validation with risk management, V&V, and regulatory evidence expectations