Efficient Deep Learning for Medical Imaging: Bridging the Gap Between High-Performance AI and Clinical Deployment

TL;DR

This paper reviews efficient deep learning architectures and compression techniques tailored for medical imaging, aiming to enable high-performance AI deployment in resource-limited clinical settings.

Contribution

It categorizes modern efficient models, evaluates compression strategies, and discusses transitioning towards on-device medical AI applications.

Findings

Efficient models include CNNs, lightweight Transformers, and linear models.

Compression techniques like pruning and quantization effectively reduce hardware needs.

Identifies challenges and future directions for clinical deployment of AI.

Abstract

Deep learning has revolutionized medical image analysis, playing a vital role in modern clinical applications. However, the deployment of large-scale models in real-world clinical settings remains challenging due to high computational costs, latency constraints, and patient data privacy concerns associated with cloud-based processing. To address these bottlenecks, this review provides a comprehensive synthesis of efficient and lightweight deep learning architectures specifically tailored for the medical domain. We categorize the landscape of modern efficient models into three primary streams: Convolutional Neural Networks (CNNs), Lightweight Transformers, and emerging Linear Complexity Models. Furthermore, we examine key model compression strategies (including pruning, quantization, knowledge distillation, and low-rank factorization) and evaluate their efficacy in maintaining diagnostic performance while reducing hardware requirements. By identifying current limitations and discussing the transition toward on-device intelligence, this review serves as a roadmap for researchers and practitioners aiming to bridge the gap between high-performance AI and resource-constrained clinical environments.