Protocol for developing and validating explainable, privacy-preserving AI diagnostic models

An explainable AI diagnostic pipeline reported 96% classification accuracy, 94% sensitivity, and 95% specificity across heterogeneous healthcare datasets, with secure federated learning preserving privacy without sacrificing performance.

• The framework combined Grad-CAM, SHAP, LIME, and counterfactual analysis to improve interpretability and more accurately identify diagnosis-relevant regions than traditional black-box models.
• Federated learning with differential privacy minimized privacy leakage and remained robust to adversarial perturbations while preserving predictive performance.
• External evaluation confirmed reproducibility and generalizability across radiology, pathology, oncology, cardiology, neurology, and precision medicine.

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