Your question is Interpretability in Medical AI. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
Explain the trade-offs between interpretability and predictive performance in a high-stakes medical diagnostic tool.
Discuss how you would choose between an interpretable model, such as regularized logistic regression or a shallow decision tree, and a more complex model, such as gradient-boosted trees or a neural network. Explain how you would compare discrimination, calibration, error costs, subgroup performance, and clinician usability. Include a practical evaluation and deployment plan that addresses threshold selection, human oversight, monitoring, and the possibility that explanations may be misleading.