CertiHealth: Towards Certified, Uncertainty-Aware, and Explainable AI for Medical Decision-Making

Medical AI systems remain unreliable, opaque, and unverified because interpretability, uncertainty quantification, and robustness certification are typically addressed in isolation. This paper introduces CertiHealth, a unified framework that constrains neural architectures through Lipschitz continuity for formal robustness guarantees, integrates ensemble-based probabilistic uncertainty estimation with conformal prediction for calibrated confidence, and employs uncertainty-weighted SHAP attribution and clinically constrained counterfactuals for transparent explanations. Evaluated on sepsis onset and in-hospital mortality prediction using MIMIC-IV, CertiHealth matches the AUROC of unconstrained baselines (0.876) while substantially improving calibration (ECE 0.026 vs. 0.087 for a standard DNN), certifying robustness for 94.1% of test samples, and achieving higher explanation fidelity, stability, and clinical alignment than post-hoc or inherently interpretable baselines. These results show that certification, uncertainty quantification, and interpretability can be combined without sacrificing diagnostic performance, advancing verifiably trustworthy AI for safety-critical healthcare.