Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
November 11, 2025BioengineeringOpen Access

Transforming Smart Healthcare Systems with AI-Driven Edge Computing for Distributed IoMT Networks

View Full Paper
Ask AI
Bookmark
Share

Authors

MAMaram Fahaad AlmufarehMHMamoona HumayunKHKhalid Haseeb

Discussion

Loading...

Member takes

Overview

Analysis shows AI and edge computing improve latency by 46% and energy consumption by 53% in smart healthcare systems, indicating effective IoMT integration.

Key Points

  • Improved latency and energy consumption significantly enhance smart healthcare systems, leading to better performance in critical situations.
  • The system incorporates wearable sensors to improve health data collection and processing, enabling timely responses to health conditions.
  • Analysis investigates edge computing in distributed IoMT networks, showcasing its impact on efficiency metrics and system responsiveness.
  • Efficient handling of network anomalies underscores the need for robust AI-driven approaches in public infrastructure for healthcare solutions.

Cite This Study

Almufareh et al. (2025) studied this question.

synapsesocial.com/papers/69252ea3c0ce034ddc356747https://doi.org/10.3390/bioengineering12111232
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Internet of Medical Things Integrating IoT with Healthcare for Remote Monitoring and Diagnosis2025
  2. 2Edge computing in IoT for smart healthcare2024
  3. 3AI-Driven Fog-Edge Computing for IoMT Systems: Architecture and Use Cases2025 · 4 citations
  4. 4SmartEdge: Smart Healthcare End-to-End Integrated Edge and Cloud Computing System for Diabetes Prediction Enabled by Ensemble Machine Learning2024 · 8 citations
  5. 5Blockchain-enabled intelligent model for energy-constrained IoMT networks with privacy-preserving2026