# Edge Intelligence for Empowering IoT-based Healthcare Systems

arXiv: https://arxiv.org/abs/2103.12144 · HTML: https://mlsanity.com/paper/2103.12144/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 20 references that are in the map.
- First posted on arXiv 22 Mar 2021.

## Builds on (most cited first, 2 of 2)

- [Edge Computing For Smart Health: Context-aware Approaches, Opportunities, and Challenges](https://mlsanity.com/paper/2004.07311/) (2020)
- [HealthFog: An Ensemble Deep Learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in Integrated IoT and Fog Computing Environments](https://mlsanity.com/paper/1911.06633/) (2019)

## Built on by (most cited first, 8 of 8)

- [Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Models](https://mlsanity.com/paper/2503.06027/) (2025)
- [Trustworthy Federated Learning via Blockchain](https://mlsanity.com/paper/2209.04418/) (2022)
- [Differentially Private Federated Learning via Reconfigurable Intelligent Surface](https://mlsanity.com/paper/2203.17028/) (2022)
- [Edge AI-based Radio Frequency Fingerprinting for IoT Networks](https://mlsanity.com/paper/2412.10553/) (2024)
- [A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks](https://mlsanity.com/paper/2508.18803/) (2025)
- [Beyond Prediction: Reinforcement Learning as the Defining Leap in Healthcare AI](https://mlsanity.com/paper/2508.21101/) (2025)
- [Diffusion Learning with Partial Agent Participation and Local Updates](https://mlsanity.com/paper/2505.11307/) (2025)
- [Two-Valued Symmetric Circulant Matrices: Applications in Deep Learning](https://mlsanity.com/paper/2605.16443/) (2026)

## Abstract (arXiv)

The demand for real-time, affordable, and efficient smart healthcare services is increasing exponentially due to the technological revolution and burst of population. To meet the increasing demands on this critical infrastructure, there is a need for intelligent methods to cope with the existing obstacles in this area. In this regard, edge computing technology can reduce latency and energy consumption by moving processes closer to the data sources in comparison to the traditional centralized cloud and IoT-based healthcare systems. In addition, by bringing automated insights into the smart healthcare systems, artificial intelligence (AI) provides the possibility of detecting and predicting high-risk diseases in advance, decreasing medical costs for patients, and offering efficient treatments. The objective of this article is to highlight the benefits of the adoption of edge intelligent technology, along with AI in smart healthcare systems. Moreover, a novel smart healthcare model is proposed to boost the utilization of AI and edge technology in smart healthcare systems. Additionally, the paper discusses issues and research directions arising when integrating these different technologies together.