Closed-Loop Learning and Self-Improving Clinical AI

We study how AI systems can improve by learning from labels, feedback, and supervision generated by earlier models. Our work focuses on understanding what remains reliably measurable in these closed learning loops and on developing methods that enable iterative improvement without reinforcing errors or biases.