Published on: June 2026
EXPLAINABLE FEDERATED ARTIFICIAL INTELLIGENCE FOR PRIVACY-PRESERVING PREDICTIVE HEALTHCARE ANALYTICS: A REVIEW OF CLINICAL DECISION SUPPORT SYSTEMS
Shikhar Mathur
Persistent Systems Inc. Pune, Maharashtra
Article Status
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Abstract
Objective: This systematic review critically examines the integration of explainability frameworks within federated AI architectures deployed for predictive healthcare analytics, focusing on clinical decision support applications published between 2018 and 2025.
Methods: A structured literature search was conducted across PubMed, IEEE Xplore, Scopus, and ACM Digital Library using PRISMA guidelines. A total of 127 primary studies were identified, of which 84 met full inclusion criteria spanning federated learning architectures, XAI techniques (SHAP, LIME, attention mechanisms, counterfactual explanations), differential privacy, and clinical validation studies.
How to Cite this Paper
Mathur, S. (2026). Explainable Federated Artificial Intelligence for Privacy-Preserving Predictive Healthcare Analytics: A Review of Clinical Decision Support Systems. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(6). https://doi.org/10.55041/ijcope.v2i6.416
Mathur, Shikhar. "Explainable Federated Artificial Intelligence for Privacy-Preserving Predictive Healthcare Analytics: A Review of Clinical Decision Support Systems." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 6, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i6.416.
Mathur, Shikhar. "Explainable Federated Artificial Intelligence for Privacy-Preserving Predictive Healthcare Analytics: A Review of Clinical Decision Support Systems." International Journal of Creative and Open Research in Engineering and Management 02, no. 6 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i6.416.
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Ethical Compliance & Review Process
- •All submissions are screened under plagiarism detection.
- •Review follows editorial policy.
- •Authors retain copyright.
- •Peer Review Type: Double-Blind Peer Review
- •Published on: Jul 01 2026
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