Transformer-Based Deep Learning for Multimodal IoT Data Fusion in Smart Healthcare: A Comprehensive Review with Emphasis on Cardiovascular Disease Monitoring and Real-Time Deployment

• Author(s): Muhammed Kuliya; Zaharaddeen Salele Iro
• Paper ID: 1712301
• Page: 2335-2356
• Published Date: 02-12-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 5 November-2025

Abstract

The integration of Internet of Things (IoT) devices and artificial intelligence has revolutionized smart healthcare, enabling continuous monitoring, personalized treatment, and proactive intervention. Cardiovascular disease (CVD) is still one of the main causes of death worldwide, yet we don?t have a strong, unified way to monitor it in real time using deep learning. Traditional machine learning methods often fall short; they strive to combine different kinds of medical data, deal with delays in processing, and work reliably in low-resource settings where technology and infrastructure are limited. This review focuses on closing that gap by examining how transformer-based deep learning models can be applied to multimodal IoT data in smart healthcare, with a special emphasis on predicting CVD. We classify and assess the latest transformer architectures based on how they fuse data, their areas of application, and their readiness for real-time use. Our analysis shows that transformer models, with their attention mechanisms and ability to handle information across multiple formats, perform much better than traditional approaches when it comes to combining data from sources like physiological signals, medical imaging, and clinical records. However, we also identify several challenges: high computational demands for edge devices, limited interpretability, a limited multimodal dataset, and infrastructure barriers in under-resourced regions. To address these challenges, we highlight future directions such as creating lightweight transformer models, using privacy-preserving federated learning, and developing unified multimodal pretraining strategies. This review aims to provide a roadmap for building fair, scalable, and low-latency AI solutions for real-time cardiovascular prediction, offering valuable insights for both researchers and healthcare system developers.

Keywords

Transformer Models, Internet of Things, Multimodal Data Fusion, Cardiovascular Disease Monitoring, Real-Time Deployment

Citations

IRE Journals:
Muhammed Kuliya, Zaharaddeen Salele Iro "Transformer-Based Deep Learning for Multimodal IoT Data Fusion in Smart Healthcare: A Comprehensive Review with Emphasis on Cardiovascular Disease Monitoring and Real-Time Deployment" Iconic Research And Engineering Journals Volume 9 Issue 5 2025 Page 2335-2356 https://doi.org/10.64388/IREV9I5-1712301

IEEE:
Muhammed Kuliya, Zaharaddeen Salele Iro "Transformer-Based Deep Learning for Multimodal IoT Data Fusion in Smart Healthcare: A Comprehensive Review with Emphasis on Cardiovascular Disease Monitoring and Real-Time Deployment" Iconic Research And Engineering Journals, 9(5) https://doi.org/10.64388/IREV9I5-1712301