Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Cuong Manh Nguyen, Truong-Son Hy

TL;DR

This work addresses the deployment gap in medical imaging AI by surveying efficient architectures across three families—CNNs for local efficiency, hybrid CNN–Transformer designs for balanced global context, and linear-complexity State Space Models (Mamba)—and by examining compression techniques (pruning, quantization, knowledge distillation, low-rank factorization). It introduces a deployment-centric lens, highlighting hardware-aware latency, energy use, privacy, and equity, and proposes a deployment-first evaluation framework with a practical reporting checklist to enable reproducible comparisons. The paper underscores that practical clinical impact requires alignment with edge hardware, compiler support, and robust calibration, not just peak accuracy, and it provides a roadmap for building trustworthy, deployable medical AI. By integrating architectural innovation with system-level efficiency and clinical trust, the authors advocate standardized benchmarks and robust explainability to realize sustainable, on-device medical AI across diverse settings.

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.

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

TL;DR

This work addresses the deployment gap in medical imaging AI by surveying efficient architectures across three families—CNNs for local efficiency, hybrid CNN–Transformer designs for balanced global context, and linear-complexity State Space Models (Mamba)—and by examining compression techniques (pruning, quantization, knowledge distillation, low-rank factorization). It introduces a deployment-centric lens, highlighting hardware-aware latency, energy use, privacy, and equity, and proposes a deployment-first evaluation framework with a practical reporting checklist to enable reproducible comparisons. The paper underscores that practical clinical impact requires alignment with edge hardware, compiler support, and robust calibration, not just peak accuracy, and it provides a roadmap for building trustworthy, deployable medical AI. By integrating architectural innovation with system-level efficiency and clinical trust, the authors advocate standardized benchmarks and robust explainability to realize sustainable, on-device medical AI across diverse settings.

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.
Paper Structure (22 sections, 2 figures, 6 tables)

This paper contains 22 sections, 2 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Architecture-Centric Surveys in Medical Imaging
  4. Efficiency-Aware and Deployment-Oriented Reviews
  5. Evolution of Deployment-Ready Architectures: From Inductive Bias to Linear Global Context
  6. CNNs as the Mature Standard for Local Efficiency
  7. The Hybrid Compromise: Reconciling Local Efficiency with Global Context
  8. The Linear Revolution: State Space Models and Mamba
  9. Comparative Analysis and Hardware Benchmarking
  10. Balancing Efficiency and Fidelity: Model Compression in Medical AI
  11. Network Pruning
  12. Quantization
  13. Knowledge Distillation
  14. Low-Rank Factorization
  15. Discussion
  16. ...and 7 more sections

Figures (2)

  • Figure 1: The Paradigm Shift toward Deployment-First Medical AI. The left boundary illustrates the limitations of centralized, resource-intensive models, including privacy risks and high latency. The right boundary showcases the improvements achieved through efficient architectures and model compression, enabling secure, real-time, and sustainable clinical deployment.
  • Figure 2: Chronological evolution of efficient deep learning architectures in medical imaging, showing the transition from inductive bias-heavy CNNs to global-context Transformers and the recent emergence of linear-complexity State Space Models.