엣지 AI 의료용 소프트웨어 시장 : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the edge AI medical software market size was valued at USD 0.65 billion in 2025 and is estimated to grow from USD 0.77 billion in 2026 to reach USD 1.81 billion by 2031, at a CAGR of 18.45% during the forecast period (2026-2031).

This report is Segmented by Component (Hardware-Integrated, Platforms, Services), Software Type (Imaging, Decision Support, Others), Technology (Computer Vision, ML/DL, and More), Clinical Application (Radiology, Cardiology, Neurology, Others), End User (Hospitals, Imaging Centers, Others), Deployment (On-Device, Edge Server, Hybrid), and Geography (North America, Europe, and More). In Value (USD).

Global Edge AI Medical Software Market Trends and Insights

Real-Time Clinical Decision-Making and Low-Latency Inference

The edge AI medical software market benefits from processing clinical data close to the point of collection, especially in emergency medicine, ambulatory surgery, and other time-sensitive care settings. A 2026 Scientific Reports study reported median inference latency of 118 ms in an edge setup, compared with 246 ms for a cloud-only baseline, with accuracy of 94.7% versus 83.1%. The same study found that INT8 quantization reduced model size by up to 74% while limiting accuracy loss to 0.4 percentage points. These results made local inference more viable for community hospitals with limited budgets for large GPU-server installations and helped vendors serve clinical sites that had not adopted enterprise PACS-integrated AI.

Expansion of AI-Enabled Medical Devices and Imaging Workflows

The growing range of AI-enabled medical devices increased the need for software that can operate safely within local clinical workflows. Imaging systems, point-of-care devices, and procedural platforms require model management and inference environments aligned with hardware and regulatory requirements. In April 2026, Abbott received FDA clearance and CE Mark for Ultreon 3.0, an AI-powered coronary imaging platform that combines real-time planning guidance with automated blood-flow assessment. Such product development expanded the need for edge software across imaging data, device controls, and workflow integration, supporting market growth across cardiology, neurology, oncology, and other clinical areas.

High Deployment, Validation, and Lifecycle-Management Costs

Institutional deployment requires more than purchasing an algorithm or an edge device. Health systems must validate clinical performance, integrate software with existing systems, train staff, and maintain models after go-live. These requirements can make implementation difficult for community hospitals, even when local processing supports their care settings. Ongoing workflow changes and safety and performance reviews can add costs, favoring vendors that offer managed services, monitoring, and regulatory support, while early adoption may remain concentrated in larger tertiary centers with established clinical informatics resources.

Other drivers and restraints analyzed in the detailed report include:

1. Growth of Remote Patient Monitoring and Hospital-at-Home Care
2. Data Residency, Privacy, and Cybersecurity Requirements
3. Limited Reimbursement Alignment for Medical AI Software

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Edge AI hardware-integrated software held 33.22% of revenue in 2025, making it the largest component group in the edge AI medical software market. This category includes software embedded in imaging systems, surgical platforms, and point-of-care medical devices. Its leadership reflects the value of running inference close to the device where clinical data originates. Once an original equipment manufacturer integrates a software layer into a scanner or ultrasound system, switching costs can rise as updates, validation, and retraining remain tied to that ecosystem.

Edge AI software platforms connect individual models with broader clinical workflows. They support model orchestration, workflow integration, and management across devices from different suppliers. Their role is becoming more important as hospitals run several algorithms without creating separate processes for each one. Edge AI professional and managed services is forecast to grow at a CAGR of 20.93% through 2031, driven by demand from health systems that lack dedicated teams for governance, monitoring, validation, and audit processes.

Medical imaging analysis software accounted for 36.23% of the edge AI medical software market share in 2025. The category benefits from high imaging volumes and the need to prioritize studies when radiology resources are constrained. It is expanding beyond detection into report generation, strengthening its role in imaging workflows. Lunit announced in December 2025 that its multimodal foundation models would support chest X-ray report generation across SimonMed Imaging's more than 175 locations.

Patient monitoring and remote care software is forecast to grow at a CAGR of 19.67% through 2031, the highest rate among software types. Hospital-at-home programs and monitoring devices that generate continuous patient data streams support this growth. Local software can process these streams faster when network availability or data governance limits a cloud-first approach. Medical data management, interoperability software, clinical documentation, and ambient intelligence tools are also gaining traction across electronic health record and clinical workflow environments.

Complete Report Scope:

• By Component
  • Edge AI Hardware-Integrated Software
  • Edge AI Software Platforms
  • Edge AI Professional and Managed Services
• By Software Type
  • Clinical Decision Support Software
  • Medical Imaging Analysis Software
  • Patient Monitoring and Remote Care Software
  • Surgical and Procedural Guidance Software
  • Medical Data Management and Interoperability Software
  • Clinical Documentation and Ambient Intelligence Software
• By Technology
  • Machine Learning and Deep Learning
  • Computer Vision
  • Natural Language Processing
  • Generative AI
• By Clinical Application
  • Radiology and Medical Imaging
  • Cardiology
  • Neurology and Stroke Care
  • Oncology and Digital Pathology
  • Ophthalmology
  • Gastroenterology
  • Obstetrics and Women's Health
  • Critical Care and Emergency Medicine
  • Remote Patient Monitoring and Chronic Disease Management
  • Others
• By End User
  • Hospitals and Health Systems
  • Diagnostic and Imaging Centers
  • Ambulatory Surgical Centers
  • Long-Term and Home-Care Providers
  • Physician Offices and Primary-Care Clinics
  • Emergency Medical Services
  • Pharmaceutical and Biotechnology Companies
  • Academic and Research Institutions
• By Deployment
  • On-Device
  • On-Premises Edge Server
  • Private Edge Cloud
  • Public Cloud and Edge-Cloud Hybrid
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • Germany
    • United Kingdom
    • France
    • Italy
    • Spain
    • Rest of Europe
  • Asia-Pacific
    • China
    • India
    • Japan
    • Australia
    • South Korea
    • Rest of Asia-Pacific
  • Middle East and Africa
    • GCC
    • South Africa
    • Rest of Middle East and Africa
  • South America
    • Brazil
    • Argentina
    • Rest of South America

Geography Analysis

North America held 42.76% of the edge AI medical software market share in 2025, supported by established health IT investments, large hospital networks, and a strong base of clinical AI platforms. The United States remained central to regional growth, as its regulatory and payment environment shaped product development and provider purchasing decisions. Abbott's April 2026 clearance for Ultreon 3.0 highlighted continued AI product activity in coronary imaging. Canada's virtual ward programs and Mexico's digital health activity added opportunities for local data processing in community settings, while regional vendors combined clinical software with infrastructure and deployment support.

Europe is the second-largest regional revenue contributor in the edge AI medical software market. Germany supported clinical AI infrastructure through the Hospital Future Act, and the Asklepios rollout across 28 hospitals showed how funding and implementation readiness supported larger deployments. The United Kingdom also used AI-supported remote monitoring in virtual ward care. European providers prioritized data handling, governance, and systems aligned with existing clinical processes, supporting edge architectures that retained patient information within the provider environment. France, Italy, and Spain also developed digital health frameworks linked to the European Health Data Space, making compliance and integration key purchasing factors.

Asia-Pacific is forecast to grow at a CAGR of 19.56% through 2031, the fastest regional growth rate. Large patient populations, expanding hospital networks, and policy interest in digital health supported demand for locally deployable clinical AI. NVIDIA, Foxconn, and Taiwan medical centers announced a June 2026 collaboration to deploy agentic and physical AI in hospital workflows, including breast cancer screening, ECG analysis, fundus imaging, and coronary artery analysis. India also remained relevant for point-of-care applications in settings with constrained connectivity. The Middle East and Africa and South America remained at earlier stages of adoption, with Gulf Cooperation Council countries and Brazil contributing to broader digital health activity.

1. Aidoc Medical Ltd.
2. Amazon Web Services, Inc.
3. Butterfly Network, Inc.
4. Canon
5. Clarius Mobile Health Corp.
6. Digital Diagnostics
7. GE Medical Systems, LLC
8. Google LLC
9. Heartflow
10. IBM
11. Lunit, Inc.
12. Medtronic Navigation, Inc.
13. Microsoft
14. NVIDIA
15. Philips Medical Systems Nederland B.V.
16. Qure.ai Technologies Pvt. Ltd.
17. RapidAI, Inc.
18. Siemens Medical Solutions USA, Inc.
19. Tempus AI, Inc.
20. Viz.ai, Inc.

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 Study Assumptions and Market Definition
• 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

• 4.1 Market Overview
• 4.2 Market Drivers
  • 4.2.1 Real-Time Clinical Decision-Making and Low-Latency Inference
  • 4.2.2 Expansion of AI-Enabled Medical Devices and Imaging Workflows
  • 4.2.3 Growth of Remote Patient Monitoring and Hospital-at-Home Care
  • 4.2.4 Data-Residency, Privacy and Cybersecurity Requirements
  • 4.2.5 Federated Learning for Multi-Institutional Clinical Intelligence
  • 4.2.6 TinyML and Ultra-Low-Power Inference in Wearable Medical Devices
• 4.3 Market Restraints
  • 4.3.1 High Deployment, Validation and Lifecycle-Management Costs
  • 4.3.2 Limited Reimbursement Alignment for Medical AI Software
  • 4.3.3 Model Drift Across Patient Populations and Clinical Workflows
  • 4.3.4 Heterogeneous Edge Hardware, Connectivity and Legacy-System Integration
• 4.4 Value Chain Analysis
• 4.5 Regulatory Landscape
• 4.6 Technological Outlook
• 4.7 Porter's Five Forces Analysis
  • 4.7.1 Bargaining Power of Buyers
  • 4.7.2 Bargaining Power of Suppliers
  • 4.7.3 Threat of New Entrants
  • 4.7.4 Threat of Substitutes
  • 4.7.5 Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE, USD)

• 5.1 By Component
  • 5.1.1 Edge AI Hardware-Integrated Software
  • 5.1.2 Edge AI Software Platforms
  • 5.1.3 Edge AI Professional and Managed Services
• 5.2 By Software Type
  • 5.2.1 Clinical Decision Support Software
  • 5.2.2 Medical Imaging Analysis Software
  • 5.2.3 Patient Monitoring and Remote Care Software
  • 5.2.4 Surgical and Procedural Guidance Software
  • 5.2.5 Medical Data Management and Interoperability Software
  • 5.2.6 Clinical Documentation and Ambient Intelligence Software
• 5.3 By Technology
  • 5.3.1 Machine Learning and Deep Learning
  • 5.3.2 Computer Vision
  • 5.3.3 Natural Language Processing
  • 5.3.4 Generative AI
• 5.4 By Clinical Application
  • 5.4.1 Radiology and Medical Imaging
  • 5.4.2 Cardiology
  • 5.4.3 Neurology and Stroke Care
  • 5.4.4 Oncology and Digital Pathology
  • 5.4.5 Ophthalmology
  • 5.4.6 Gastroenterology
  • 5.4.7 Obstetrics and Women's Health
  • 5.4.8 Critical Care and Emergency Medicine
  • 5.4.9 Remote Patient Monitoring and Chronic Disease Management
  • 5.4.10 Others
• 5.5 By End User
  • 5.5.1 Hospitals and Health Systems
  • 5.5.2 Diagnostic and Imaging Centers
  • 5.5.3 Ambulatory Surgical Centers
  • 5.5.4 Long-Term and Home-Care Providers
  • 5.5.5 Physician Offices and Primary-Care Clinics
  • 5.5.6 Emergency Medical Services
  • 5.5.7 Pharmaceutical and Biotechnology Companies
  • 5.5.8 Academic and Research Institutions
• 5.6 By Deployment
  • 5.6.1 On-Device
  • 5.6.2 On-Premises Edge Server
  • 5.6.3 Private Edge Cloud
  • 5.6.4 Public Cloud and Edge-Cloud Hybrid
• 5.7 By Geography
  • 5.7.1 North America
    • 5.7.1.1 United States
    • 5.7.1.2 Canada
    • 5.7.1.3 Mexico
  • 5.7.2 Europe
    • 5.7.2.1 Germany
    • 5.7.2.2 United Kingdom
    • 5.7.2.3 France
    • 5.7.2.4 Italy
    • 5.7.2.5 Spain
    • 5.7.2.6 Rest of Europe
  • 5.7.3 Asia-Pacific
    • 5.7.3.1 China
    • 5.7.3.2 India
    • 5.7.3.3 Japan
    • 5.7.3.4 Australia
    • 5.7.3.5 South Korea
    • 5.7.3.6 Rest of Asia-Pacific
  • 5.7.4 Middle East and Africa
    • 5.7.4.1 GCC
    • 5.7.4.2 South Africa
    • 5.7.4.3 Rest of Middle East and Africa
  • 5.7.5 South America
    • 5.7.5.1 Brazil
    • 5.7.5.2 Argentina
    • 5.7.5.3 Rest of South America

6 COMPETITIVE LANDSCAPE

• 6.1 Market Concentration
• 6.2 Market Share Analysis
• 6.3 Company Profiles (includes Global Level Overview, Market Level Overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share, Products and Services, Recent Developments)
  • 6.3.1 Aidoc Medical Ltd.
  • 6.3.2 Amazon Web Services, Inc.
  • 6.3.3 Butterfly Network, Inc.
  • 6.3.4 Canon Medical Systems Corporation
  • 6.3.5 Clarius Mobile Health Corp.
  • 6.3.6 Digital Diagnostics, Inc.
  • 6.3.7 GE Medical Systems, LLC
  • 6.3.8 Google LLC
  • 6.3.9 Heartflow, Inc.
  • 6.3.10 IBM Corporation
  • 6.3.11 Lunit, Inc.
  • 6.3.12 Medtronic Navigation, Inc.
  • 6.3.13 Microsoft Corporation
  • 6.3.14 NVIDIA Corporation
  • 6.3.15 Philips Medical Systems Nederland B.V.
  • 6.3.16 Qure.ai Technologies Pvt. Ltd.
  • 6.3.17 RapidAI, Inc.
  • 6.3.18 Siemens Medical Solutions USA, Inc.
  • 6.3.19 Tempus AI, Inc.
  • 6.3.20 Viz.ai, Inc.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment