Generalizable and Explainable Deep Learning for Medical Image Computing: An Overview
Clinical deployment of AI in medical imaging is hindered by poor generalizability across devices, institutions, and diseases, alongside insufficient interpretability and decision transparency. Method: We propose the first unified framework that jointly integrates domain generalization and self-supervised pretraining—enhancing cross-domain robustness—with concept bottleneck models, disentangled attention, counterfactual reasoning, and uncertainty quantification—to improve decision interpretability and trustworthiness. Contribution/Results: We systematically survey over 100 state-of-the-art works to clarify technical evolution and clinical translation bottlenecks. We introduce a novel, clinically grounded evaluation paradigm for trustworthy AI, spanning four orthogonal dimensions: performance, robustness, interpretability, and uncertainty. Our framework provides both a methodological foundation and a reproducible implementation roadmap for deploying reliable, clinically viable AI systems in medical imaging.