An Efficient Clinical Decision Support Framework Using IoMT Based on Explainable and Trustworthy Artificial Intelligence with Transformer Model and Blockchain-Integrated Chunking

Sep 30, 2026

Edge–cloud architectures for analyzing AI-enabled health data are increasingly adopted, yet data security, communication overhead, cost-effectiveness, and transmission losses remain unresolved. The study proposes a reliable, explainable, energy-efficient stress detection framework that uses cost-oriented blockchain-based content-defined chunking to reduce transfer losses. Transformer models achieve over 99% accuracy, with TimesNet as the reference model. The framework integrates chunking, blockchain trust mechanisms, and edge–cloud computing with XAI to deliver secure, transparent IoMT processing with improved transmission reliability, energy, and cost efficiency.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages22

Reading Time55 min

AccessFree to download

Abstract

Introduction

IoMT and cloud–edge processing

Security, privacy, and blockchain integration

Offloading challenges and AI-based resource allocation

Article An Efficient Clinical Decision Support Framework Using IoMT Based on Explainable and Trustworthy Artificial Intelligence with Transformer Model and Blockchain-Integrated Chunking Kübra Arslano ˘glu 1, * and Mehmet Karaköse 2 Received: 8 October 2025 Revised: 23 November 2025 Accepted: 16 December 2025 Published: 19 December 2025 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. 1 The Department of Software Engineering, The University of Firat, 23119 Elazig, Turkey 2 The Department of Computer Engineering, The University of Firat, 23119 Elazig, Turkey; [mkarakose@firat.edu.tr](mkarakose@firat.edu.tr) * [Correspondence: karslanoglu@firat.edu.tr](Correspondence: karslanoglu@firat.edu.tr) Abstract Background/Objectives: The use of edge–cloud architectures has increased rapidly to move the analysis of AI-enabled health data to global environments. However, data security, communication overhead, cost-effectiveness, and data transmission losses are still important problems to be solved. Methods: In this paper, we propose a reliable, explainable, and energy-efficient stress detection framework supported by a cost-oriented blockchain-based content-defined chunking approach to minimise the losses during data transfer. In the proposed architecture, the Nurse Stress dataset represents IoMT data. While the chunking process reduces communication volume and storage costs by avoiding data duplication, blockchain technology eliminates the risks of unauthorised access and manipulation by ensuring the immutability and traceability of data blocks. Results: All Transformer-based models have demonstrated over 99% accuracy. The TimesNet model, in particular, has been designated as the system’s reference model, exhibiting superior performance in terms of both stability and accuracy. The main contribution of this study lies in proposing one of the first integrated frameworks that jointly employs chunking-based data management, blockchain-enabled trust mechanisms, and edge–cloud computing with XAI to ensure secure and transparent IoMT data processing. The proposed system not only performs highly accurate stress detection, but also optimises the dimensions of reliable data transmission, energy and cost efficiency, and clinical reliability. Conclusions: In this respect, the study presents a scalable, reliable, and repeatable approach in health decision support systems by combining data security, integrity, and explainability issues, which are addressed separately in the literature, in a holistic manner. Keywords: internet of medical things; cloud; edge; artificial intelligence; blockchain; explainability; trustworthy 1. Introduction The concept of the Internet of Things (IoT) greatly expands people’s ability to access information, while cloud computing brings powerful information processing capability to IoT technology and improves the efficiency of IoT systems [1] . Internet of Medical Things (IoMT) is an IoT-based technology that is generally used for the development of IoT-enabled healthcare systems for monitoring various vital signs such as ECG, pulse, blood pressure, and brain signals [2] . Nowadays, IoMT is a technology that has the potential to revolutionise the development of the healthcare industry. IoMT offers a system that significantly improves the quality, accessibility, and personalization of healthcare services by connecting medical devices and their applications over the internet [3,4] . These smart devices, which continuously monitor the health status of patients and collect data of their health status, form the basis for real-time health monitoring and interventions. However, for this technology to realise its full potential, the collected data needs to be processed and analysed effectively. This is a significant challenge, especially given the l

FAQs

What problems does the framework address in edge–cloud IoMT systems?

It targets data security and privacy risks, communication overhead, cost-effectiveness issues, and transmission losses during data transfer and processing.

How does blockchain-based chunking contribute to data transfer efficiency and trust?

Content-defined chunking reduces communication volume and storage costs by avoiding duplication, while blockchain immutability and traceability mitigate unauthorized access and data manipulation risks.

Which model performs best and what accuracy level is reported?

All Transformer-based models achieve over 99% accuracy, and TimesNet is designated as the reference model with superior stability and accuracy.

Sep 30, 2026

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