Related Experiment Video
Updated: Jan 13, 2026
05:47
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
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An Efficient Clinical Decision Support Framework Using IoMT Based on Explainable and Trustworthy Artificial
Kübra Arslanoğlu1, Mehmet Karaköse2
1The Department of Software Engineering, The University of Firat, 23119 Elazig, Turkey.
Diagnostics (Basel, Switzerland)
|January 10, 2026
Summary
This study introduces a secure, cost-effective edge-cloud framework for AI health data analysis using blockchain and data chunking. It achieves high accuracy in stress detection while optimizing data transmission and energy efficiency.
Area of Science:
- Health Informatics
- Artificial Intelligence
- Cybersecurity
Background:
- Edge-cloud architectures are increasingly used for AI health data analysis.
- Challenges include data security, communication overhead, cost, and transmission losses.
Purpose of the Study:
- To propose a reliable, explainable, and energy-efficient stress detection framework.
- To address security, cost, and data loss issues in AI-enabled health data analysis.
Main Methods:
- Utilized a cost-oriented blockchain-based content-defined chunking approach for Internet of Medical Things (IoMT) data.
- Employed chunking to reduce communication volume and storage costs.
- Leveraged blockchain for data immutability, traceability, and to prevent unauthorized access.
Main Results:
- Achieved over 99% accuracy with Transformer-based models, notably TimesNet.
- Demonstrated a novel integrated framework combining data chunking, blockchain, edge-cloud computing, and Explainable AI (XAI).
- Optimized data transmission reliability, energy efficiency, cost-effectiveness, and clinical reliability.
Conclusions:
- Presents a scalable, reliable, and repeatable approach for health decision support systems.
- Holistically addresses data security, integrity, and explainability in IoMT data processing.
- Offers a secure and transparent solution for analyzing AI-enabled health data.
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