Trustworthy And Explainable Deep Learning For Clinical Decision Support With Blockchain-Based Audit And Compliance Assurance
DOI:
https://doi.org/10.29284/xmm82z90Keywords:
Clinical decision support, explainable deep learning, blockchain audit, healthcare AI, regulatory compliance.Abstract
The rapid adoption of deep learning models in clinical decision support systems has shown promising improvements in disease prediction and risk stratification; however, their deployment in real-world healthcare environments remains limited due to concerns related to trust, explainability, auditability, and regulatory compliance. Existing approaches largely operate as black-box models, offering limited transparency and weak mechanisms for validating the integrity of clinical decisions. To address these limitations, this paper proposes a trustworthy and explainable deep learning framework for clinical decision support, integrated with a blockchain-based audit and compliance assurance layer. The proposed system employs an attention-enhanced deep neural network to generate interpretable clinical predictions, while blockchain smart contracts record model decisions, explanation hashes, and consent validation logs to ensure tamper-proof auditing and regulatory traceability. The framework is evaluated using the MIMIC-IV clinical dataset, with experiments conducted under realistic healthcare deployment settings. Quantitative results demonstrate that the proposed model achieves an accuracy of 97.1% and an AUC of 0.982, outperforming conventional deep learning baselines while providing verifiable decision transparency. The results confirm that combining explainable deep learning with blockchain-based assurance significantly enhances trust, accountability, and clinical reliability.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
