Abstract: This paper introduces a novel framework integrating Large Language Models (LLMs) with blockchain technology for medical device fault detection and diagnostics in Health-care 4.0 environments. The proposed framework addresses key challenges, including real-time fault detection, data security, and automated diagnostics through a multi-layered architecture incorporating Internet of Things (IoT) integration, blockchain-based security, and LLM-driven diagnostics. Experimental evaluations demonstrate substantial improvements in diagnostic accuracy and response time while maintaining stringent security standards and regulatory compliance. The system provides enhanced fault detection with real-time monitoring capabilities and secure maintenance record management for smart healthcare. Comparative analysis of different LLMs and traditional Machine Learning (ML) methods shows that Deepseek-R1:7b achieved 97.6% classification accuracy, while O3-mini reached 90.4%and 91.2% in diagnosis accuracy and problem identification, respectively. Claude demonstrated the highest technical accuracy (98.4%), while Traditional ML excelled in processing time (11.7) and processing rate (10.68). Deepseek-R1:7b’s offline capabilities ensure stringent security, privacy, and confidentiality with restricted connectivity, making it particularly suitable for sensitive healthcare applications where data protection is paramount.

Khalid Alsaif, Aiiad Albeshri, Maher Khemakhem and Fathy Eassa, “Healthcare 4.0: A Large Language Model-Based Blockchain Framework for Medical Device Fault Detection and Diagnostics” International Journal of Advanced Computer Science and Applications(IJACSA), 16(4), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160495

@article{Alsaif2025,
title = {Healthcare 4.0: A Large Language Model-Based Blockchain Framework for Medical Device Fault Detection and Diagnostics},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160495},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160495},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {4},
author = {Khalid Alsaif and Aiiad Albeshri and Maher Khemakhem and Fathy Eassa}
}