Designing Explainable Artificial Intelligence with Edge Cloud Computing for Real-Time Healthcare Decision Intelligence
Abstract
The increasing adoption of Artificial Intelligence (AI) in healthcare has transformed clinical decision-making by enabling rapid analysis of medical data and improving diagnostic accuracy. However, conventional cloud-based AI systems often face challenges related to latency, privacy, transparency, and regulatory compliance, particularly in real-time healthcare environments. Explainable Artificial Intelligence (XAI) addresses the transparency issue by providing interpretable and understandable predictions, allowing healthcare professionals to trust and validate AI-driven recommendations. When integrated with Edge Cloud Computing, XAI enables intelligent healthcare applications to process sensitive patient data closer to data sources, thereby reducing communication delays, enhancing privacy protection, and supporting timely clinical interventions. This study explores the design of an Explainable Artificial Intelligence framework integrated with Edge Cloud Computing for real-time healthcare decision intelligence. The proposed architecture combines edge devices, cloud infrastructure, machine learning models, explainability techniques, and secure communication protocols to facilitate accurate, transparent, and efficient healthcare analytics. The research evaluates existing technologies, identifies implementation challenges, and proposes a methodology for developing scalable and privacy-preserving healthcare intelligence systems. Key performance indicators include prediction accuracy, interpretability, response time, resource utilization, and security compliance. The proposed framework contributes to intelligent healthcare by improving clinician confidence, optimizing computational efficiency, strengthening patient data privacy, and supporting reliable real-time decision-making across distributed healthcare environments
Article Information
Journal |
International Journal of Emerging Trends in Engineering and Management Research |
|---|---|
Volume (Issue) |
Vol. 11 No. 3 (2026): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) |
DOI |
|
Pages |
21073-21082 |
Published |
June 15, 2026 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Tommi Mikkonen (%2026). Designing Explainable Artificial Intelligence with Edge Cloud Computing for Real-Time Healthcare Decision Intelligence. International Journal of Emerging Trends in Engineering and Management Research , Vol. 11 No. 3 (2026): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 21073-21082. https://doi.org/10.15662/ijetemr.2026.1103009 |
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