Edge Deep Learning in Computer Vision and Medical Diagnostics: A Comprehensive Survey

A comprehensive academic survey examines edge deep learning—the integration of deep learning with edge computing—and its applications in computer vision and medical diagnostics. The paper categorizes hardware platforms, reviews model optimization techniques like compression and lightweight design, and identifies future challenges for deploying neural networks on resource-constrained devices.

Edge deep learning represents a fundamental shift in how artificial intelligence systems process data by moving computation closer to data sources rather than relying on centralized cloud infrastructure. This paradigm enables real-time decision-making with reduced latency, lower bandwidth consumption, and improved privacy—critical factors for medical diagnostics and computer vision applications where millisecond delays or data transmission vulnerabilities pose risks. The survey catalogs the maturation of this field, documenting both technical progress in model compression techniques and practical validation across healthcare and vision domains.

The motivation behind this research stems from inherent limitations in cloud-dependent AI: network dependency creates single points of failure, data privacy concerns arise from transmitting sensitive medical information externally, and latency becomes problematic for time-critical applications like real-time surgical guidance. Edge deployment addresses these constraints by enabling sophisticated inference on smartphones, embedded medical devices, and IoT sensors. The hardware categorization framework presented in the survey acknowledges varying computational constraints across deployment scenarios, from resource-limited microcontrollers to GPU-equipped edge servers.

For practitioners and enterprises, this survey validates edge deep learning as a viable commercial pathway rather than theoretical concept. Healthcare providers gain a technical roadmap for deploying diagnostic models locally, reducing infrastructure costs while enhancing patient data protection. Developers benefit from consolidated knowledge of optimization methods—quantization, pruning, knowledge distillation—that bridge the gap between state-of-the-art models and device constraints.

The identified obstacles—including model accuracy degradation during compression, fragmented hardware ecosystems, and standardization gaps—signal that the field remains in active development. Organizations implementing edge AI solutions should expect evolving toolchains and potential performance trade-offs. Future adoption hinges on advancing compression algorithms and establishing clearer deployment benchmarks.

• →Edge deep learning enables real-time AI inference on local devices, reducing latency and improving data privacy for sensitive applications like medical diagnostics.
• →Model compression techniques such as quantization and knowledge distillation are essential for deploying sophisticated neural networks on resource-constrained hardware.
• →Hardware platform categorization based on performance metrics helps practitioners select appropriate devices for specific computer vision and medical application requirements.
• →Current obstacles include accuracy loss during model compression, hardware fragmentation, and lack of standardized deployment frameworks across edge platforms.
• →Adoption growth depends on advances in optimization algorithms and establishment of clearer benchmarks and best practices for edge AI implementation.