Applied IT & Engineering
Information and engineering sciences | Online ISSN 3068-0115
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Volume 1 Number 1 2023
RESEARCH ARTICLE (Open Access)
Toward Trustworthy Healthcare AI: A Federated Explainable Deep Learning Framework for Secure and Privacy-Preserving Clinical Decision Support
Md Tanzimul Islam 1, Md Rokibul Hasan 2, Md Jahid Howlader 1, Sinigdha Islam 3*
Applied IT & Engineering 1 (1) 1-11 https://doi.org/10.25163/engineering.1110824
Submitted: 26 July 2023 Revised: 05 October 2023 Accepted: 11 October 2023 Published: 13 October 2023
Abstract
Background: Artificial intelligence (AI) holds extraordinary promise for transforming clinical decision-making in modern healthcare. Yet three persistent barriers — compromised patient data privacy, inadequate model transparency, and insufficient security infrastructure — continue to constrain its adoption at scale. These are not small technical inconveniences; they represent fundamental gaps between the potential of AI and its safe, ethical deployment in clinical environments.Methods: We propose the Federated Explainable AI (FEXAI) Framework — a unified architecture that integrates TensorFlow Federated (TFF)-based distributed model training with SHapley Additive exPlanations (SHAP) for post-hoc interpretability, and a blockchain-anchored secure aggregation protocol for model update integrity. Using a composite distributed dataset from three simulated healthcare institutions (n = 42,830 de-identified patient records drawn from MIMIC-III and UCI repository benchmarks), local deep neural network models were trained independently at each node. Federated Averaging (FedAvg) was applied across 50 communication rounds to converge a global model, with no raw patient data ever leaving the local institution.Results: The FEXAI Framework achieved a diagnostic accuracy of 96.8% (F1-score: 95.5%), surpassing centralized deep neural network (92.7%) and traditional machine learning baselines including Random Forest (89.4%) and Support Vector Machine (86.9%). Privacy preservation and security metrics — assessed against defined scoring rubrics — reached 98.3% and 97.6%, respectively. SHAP-based explainability produced clinician-interpretable feature attribution scores with an explainability rating of 94.8%. Mean inference latency was 0.42 seconds per case.Conclusion: The FEXAI Framework demonstrates that accuracy, privacy, security, and transparency are not mutually exclusive objectives in healthcare AI. The findings suggest a viable and reproducible path toward clinical AI systems that clinicians can trust, regulators can audit, and patients can rely on.
Keywords: Federated learning; Explainable artificial intelligence; Clinical decision support systems; Healthcare data privacy; Secure deep learning
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