Lightweight Deep Learning for IoT-Based Medical Diagnosis: A Survey

Authors

• Balqees Talal Hasan, Department of Computer Networks and Internet, College of Information Technology, Ninevah University, Mosul, Iraq.Follow
• Ali Mohsin Ahmed Al-Sabaawi, Department of Software, College of Information Technology, Ninevah University, Mosul, Iraq.
• Zaid J. Al-Araji, Department of Computer Networks and Internet, College of Information Technology, Ninevah University, Mosul, Iraq.

Abstract

The rapid expansion of Internet of Things (IoT) technologies in healthcare has enabled continuous patient monitoring and data-driven clinical decision support. Deep learning models excel at extracting intricate patterns from high-dimensional medical data but face significant deployment challenges on resource-constrained IoT devices due to high computational and memory demands. This survey systematically analyzes four core strategies to adapt deep learning for edge based medical diagnosis: model pruning, quantization, knowledge distillation, and inherently lightweight architectures. For each approach, we dissect the underlying methodologies, evaluate the trade-offs between model efficiency and predictive performance, and present relevant medical case studies. We further review deployment frameworks (e.g., TensorFlow Lite, PyTorch Mobile, ONNX) that facilitate integration with IoT hardware. Evidence indicates that these lightweight techniques can substantially reduce model size and inference latency while preserving diagnostic accuracy, enabling real-time AI-powered healthcare in decentralized settings. Finally, we identify critical research challenges including energy-efficient optimization, privacy-aware model design, and end-to-end automation across software and hardware layers—and outline future directions to advance robust, efficient, and trustworthy edge AI in healthcare.

Recommended Citation

Hasan, Balqees Talal; Al-Sabaawi, Ali Mohsin Ahmed; and Al-Araji, Zaid J. (2025) "Lightweight Deep Learning for IoT-Based Medical Diagnosis: A Survey," Mesopotamian Journal of Artificial Intelligence in Healthcare: Vol. 3: Iss. 1, Article 22.
DOI: https://doi.org/10.58496/MJAIH/2025/023

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.