Edge Deep Learning Survey Reveals Key Techniques for Medical Vision AI

This comprehensive survey, authored by Yiwen Xu, Tariq M. Khan, Yang Song, and Erik Meijering, and published in Artificial Intelligence Review (Volume 58, Article 93, 2025), provides an in-depth analysis of edge deep learning for computer vision, with a strong focus on medical diagnostics. The paper begins by outlining the foundational principles of edge deep learning, highlighting how it merges edge computing with deep learning to enable real-time decision-making that is attuned to environmental factors. A key contribution is a novel categorization of edge hardware platforms based on performance and usage scenarios, which helps practitioners select the optimal hardware for their specific applications. The survey then dives into implementation approaches, covering lightweight network design and model compression techniques such as pruning, quantization, and knowledge distillation, which are essential to run deep neural networks on resource-constrained edge devices.

The authors review practical applications in both general computer vision and medical diagnostics, demonstrating how edge-deployed models can deliver profound real-world impact—from real-time anomaly detection in surveillance to on-device disease screening in clinical settings. The survey also discusses future directions and obstacles, including challenges related to latency, energy efficiency, and data privacy. By providing a structured taxonomy of methods and hardware, this paper serves as a go-to reference for researchers and engineers aiming to push intelligent edge solutions into production, especially in healthcare where immediate, on-device analysis can save lives.