의료 소프트웨어용 컴퓨터 비전 시장 : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the computer vision in medical software market size is expected to increase from USD 8.13 billion in 2025 to USD 9.16 billion in 2026 and reach USD 15.27 billion by 2031, growing at a CAGR of 10.77% over 2026-2031.

This report is Segmented by Component (Software, Services), Application (6 Segments), Imaging Modality (9 Segments), Technology (7 Segments), Deployment Mode (3 Segments), End User (6 Segments), Medical Specialty (6 Segments), and Geography (North America, Europe, Asia-Pacific, Middle East, South America)

Global Computer Vision In Medical Software Market Trends and Insights

Chronic Disease Imaging Volumes Outpace Existing Infrastructure Capacity

Chronic cardiovascular disease, diabetes complications, and cancer continue to increase the volume of scans that providers must order and interpret. The United States performs more than 900 million medical imaging procedures each year, placing pressure on scanner capacity and radiology teams. A review reported imaging overuse rates from a median of 11.2% to 20% to 50% in some settings, which creates a role for decision support before a scan is ordered and after it is acquired. This makes the Computer vision in medical software market relevant to capacity management as well as image interpretation, especially when hospitals need clinicians to focus scarce reading time on scans with the highest clinical priority. The Computer vision in medical software market benefits when hospitals seek tools that reduce unnecessary examinations and accelerate appropriate cases. Platforms that connect ordering, triage, and reporting are therefore likely to carry more value than isolated detection tools.

Radiologist Shortfall Creates a Structural Commercial Tailwind

The shortage of radiologists gives health systems a direct reason to consider workflow automation. The United States had 34,000 practicing radiologists, 16% of whom worked part-time, while 32% were aged 55 or older. The American College of Radiology job board carried nearly 1,930 openings, while fewer than 1,400 residents matched into radiology each year. The United Kingdom was short of 2,300 clinical radiologists in 2025, according to the Royal College of Radiologists. A 2026 mammography trial found that an AI screening strategy reduced radiologist workload by 63.6% and improved cancer detection by 15.2%. These results are moving computer vision in the medical software market beyond pilot evaluations when a tool can address workload and diagnostic performance together, while still leaving clinical teams in control of final decisions and local workflow design.

High Deployment Costs and Uneven Clinical Evidence Slow System-Level Commitment

Implementation costs remain difficult for community hospitals and safety-net providers with constrained capital budgets. Costs extend beyond licenses to PACS integration, local validation, clinician training, technical support, annotation work, and ongoing model monitoring. Workflow-integrated tools can improve detection and report turnaround, but evidence of downstream patient benefits has not been consistent across care pathways. Procurement teams therefore ask whether a gain in workflow performance resolves the actual bottleneck in their organization. Buyers that acquired separate tools during the 2021 to 2023 pilot period are consolidating toward broader enterprise platforms. This reduces room for single-indication suppliers that cannot show clinical utility across varied patient populations.

Other drivers and restraints analyzed in the detailed report include:

1. Regulatory and Reimbursement Momentum Converts Policy Progress Into Market Pull
2. Cloud Interoperability Unlocks Enterprise-Scale Clinical AI Deployment
3. Cross-Border Data Restrictions and Cybersecurity Exposure Fragment Global Deployment

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

Segment Analysis

Software held 78.23% of segment revenue in 2025, giving it the largest share of the Computer vision in medical software market size. Subscription and software-as-a-service models support recurring revenue and allow frequent improvements to clinical algorithms. Imaging hardware follows replacement cycles of 8 to 12 years, while software can be updated more regularly under controlled regulatory processes. This difference makes the software layer central to hospital efforts to improve existing imaging assets. Software suppliers also benefit when health systems prefer enterprise contracts that cover several departments or clinical uses.

Services are forecast to expand at 11.5% CAGR through 2031 as buyers seek help with implementation, local validation, monitoring, and post-market surveillance. Managed service agreements can combine technical support with model performance review over several years. These arrangements reflect a wider concern that performance at the development site may not match performance after local deployment. Aidoc received FDA clearance in January 2026 for a multi-indication foundation model covering 11 abdominal CT indications in 1 workflow. Such products show why software providers are moving from narrow algorithms toward broader clinical platforms. The Computer vision in medical software industry is therefore seeing service requirements grow alongside software adoption.

Medical imaging and diagnostics held 41.56% of revenue in 2025, supported by frequent radiology use cases in chest, abdominal, neurological, and breast imaging. Detection and triage tools have a longer clinical record in these settings than many newer applications. Their value is tied to the high number of cases that need consistent prioritization and reporting. Hospitals also understand the workflow measures used to assess diagnostic tools, such as turnaround time and workload. This gives imaging applications a durable position in the Computer vision in medical software market, since providers can connect their value to familiar daily measures of workload, prioritization, report quality, and examination volume.

Clinical trials, drug development, and research is forecast to grow at 11.2% CAGR through 2031. Pharmaceutical sponsors use imaging AI for biomarker qualification, digital pathology endpoints, and consistent image review across trial sites. Image-guided surgery and surgical robotics are also drawing investment because real-time guidance has clear operational relevance. Medtronic introduced Touch Surgery Aide in July 2026 as a computing platform for real-time AI during operating room procedures. Patient monitoring, asset intelligence, and pathology automation can also appeal where operational gains are easier to quantify. These use cases broaden demand beyond the conventional radiology reading room.

X-ray and digital radiography held 24.22% of modality revenue in 2025, supported by large procedure volumes and an established base of triage and detection applications. Chest X-ray tools are especially relevant in emergency and community settings where clinicians need timely support. A 2026 prospective study reported that a vision-language AI system for chest X-rays improved diagnostic accuracy and workflow efficiency in resource-limited emergency department settings. This supports wider Computer vision in the medical software market use beyond academic centers, where emergency departments and community hospitals need tools that work with existing X-ray volumes and limited specialist availability. X-ray remains a practical entry modality for providers evaluating the Computer vision in medical software market.

Digital pathology and whole-slide imaging is forecast to grow at 11.3% CAGR through 2031. Foundation models, multimodal systems, and language model copilots are being applied to diagnostic support, prognosis, workflow efficiency, and drug discovery. DICOM-WSI standards can improve interoperability between scanners and algorithms as digital pathology deployments scale. CT and MRI remain major modalities because they support complex detection, reconstruction, segmentation, and treatment planning tasks. GE HealthCare received FDA clearance for True Definition DL CT reconstruction in April 2026 for several imaging indications. PET and SPECT, retinal imaging, endoscopy, and intraoperative imaging remain smaller areas with potential for cross-modality models.

Complete Report Scope:

• By Component
  • Software
  • Services
• By Application
  • Medical Imaging and Diagnostics
  • Image-Guided Surgery and Surgical Robotics
  • Patient Monitoring and Safety
  • Pathology and Laboratory Automation
  • Clinical Trials, Drug Development, and Research
  • Hospital Operations and Asset Intelligence
• By Imaging Modality
  • X-Ray and Digital Radiography
  • Computed Tomography
  • Magnetic Resonance Imaging
  • Ultrasound
  • Mammography and Digital Breast Tomosynthesis
  • Positron Emission Tomography and Single-Photon Emission Computed Tomography
  • Optical Coherence Tomography and Retinal Imaging
  • Digital Pathology and Whole-Slide Imaging
  • Endoscopy, Surgical Video, and Intraoperative Imaging
• By Technology
  • Deep Learning
  • Computer Vision and Image Processing
  • Natural Language Processing for Multimodal Reporting
  • Generative AI and Vision-Language Models
  • Machine Learning and Predictive Analytics
  • Three-Dimensional Visualization and Extended Reality
  • Edge AI and Federated Learning
• By Deployment Mode
  • On-Premise
  • Cloud-Based
  • Hybrid and Edge
• By End User
  • Hospitals and Specialty Clinics
  • Diagnostic Imaging Centers
  • Academic and Research Institutes
  • Pharmaceutical and Biotechnology Companies
  • Contract Research Organizations
  • Ambulatory Surgical Centers
• By Medical Specialty
  • Radiology
  • Oncology
  • Cardiology
  • Neurology
  • Orthopedics
  • Other Specialties (Pathology, Ophthalmology, and Others)
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • Germany
    • United Kingdom
    • France
    • Italy
    • Spain
    • Rest of Europe
  • Asia-Pacific
    • China
    • Japan
    • India
    • Australia
    • South Korea
    • Rest of Asia-Pacific
  • Middle East
    • GCC
    • South Africa
    • Rest of Middle East and Africa
  • South America
    • Brazil
    • Argentina
    • South America

Geography Analysis

North America held 39.11% of global revenue in 2025, giving it the largest regional position in the Computer vision in medical software market. The region combines a high concentration of FDA-cleared devices with mature EHR and PACS infrastructure. Predetermined Change Control Plans were used in 30% of new U.S. AI device submissions in 2026, allowing agreed model updates without a separate filing for each change. The FDA's ADVOCATE program also signals continuing work on a risk-based framework for agentic AI in healthcare. Canada and Mexico are adopting U.S.-cleared solutions, although Canadian provincial data requirements can complicate broader platform deployment.

Europe has a substantial but fragmented opportunity because national procurement systems differ across Germany, the United Kingdom, France, Italy, and Spain. The EU AI Act has imposed conformity assessment, documentation, and post-market monitoring requirements for high-risk healthcare AI since August 2024. These requirements can raise entry costs in the Computer vision in medical software market, especially for smaller non-EU vendors that lack dedicated teams for documentation, surveillance, audit preparation, and local regulatory engagement. The UK Medicines and Healthcare products Regulatory Agency launched its AI Airlock in 2026 to test medical AI in near-commercial conditions. Germany's electronic patient record rollout under the 2025 DigiG legislation can strengthen the data base for hospital-level personalization over time. Europe's pace is steady, but compliance obligations can slow adoption relative to faster-growing Asian markets.

Asia Pacific is forecast to grow at 12.66% CAGR through 2031, making it the fastest-growing regional part of the Computer vision in medical software market. China is building domestic imaging AI capacity, with Shanghai United Imaging Healthcare combining imaging hardware and embedded AI across CT, MRI, and PET-CT. South Korea is seeking to use national cancer screening data for medical AI foundation models, as shown by Lunit's participation in an NVIDIA AI Ecosystem Roundtable in June 2026. Japan had around 80 PMDA-approved AI medical devices in early 2026, compared with more than 800 FDA-cleared devices in the United States. The University of Tokyo and RIKEN released a 14.2 billion parameter Japanese medical multimodal model in March 2026 for on-premise hospital use. Fujifilm received PMDA approval in July 2026 for SYNAPSE SAI, a concurrent-read brain aneurysm detection tool for MRA images. India and South Korea are also scaling cancer screening programs that incorporate AI diagnostics. Middle East and Africa demand is supported by GCC digital health investment, while South America is centered on Brazil and Argentina, where screening use cases can support adoption despite infrastructure limits.

1. Aidoc Medical Ltd.
2. Alphabet Inc.
3. Annalise.ai Pty Ltd.
4. Arterys Inc.
5. Butterfly Network, Inc.
6. Canon
7. Caption Health
8. Cleerly, Inc.
9. Enlitic
10. FUJIFILM
11. GE HealthCare Technologies Inc.
12. HeartFlow, Inc.
13. iCAD, Inc.
14. Koninklijke Philips
15. Lunit
16. Microsoft
17. NVIDIA
18. Qure.ai Technologies Pvt. Ltd.
19. Rad AI, Inc.
20. Siemens Healthineers
21. Tempus AI, Inc.
22. Viz.ai Inc.
23. VUNO, Inc.
24. Shanghai United Imaging Healthcare Co., Ltd.

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 Exploding Chronic-Disease Imaging Demand
  • 4.2.2 Radiologist Shortage and Workflow Automation Needs
  • 4.2.3 Regulatory and Reimbursement Momentum for AI-Assisted Diagnosis
  • 4.2.4 Cloud and Enterprise Imaging Interoperability Adoption
  • 4.2.5 Edge-AI Latency Requirements in Operating Rooms and Point-of-Care Imaging
  • 4.2.6 Procedure-Volume and Bed-Capacity KPIs Driving Computer Vision Deployment
• 4.3 Market Restraints
  • 4.3.1 High Implementation Cost and Uncertain Clinical ROI
  • 4.3.2 Data Privacy, Cybersecurity, and Cross-Border Data Restrictions
  • 4.3.3 Annotation Scarcity and Site-Specific Model Drift in Long-Tail Diseases
  • 4.3.4 Liability Allocation for Human-in-the-Loop and Autonomous Clinical Decisions
• 4.4 Value and Supply-Chain Analysis
• 4.5 Regulatory Landscape
• 4.6 Technological Outlook
• 4.7 Porters Five Forces Analysis
  • 4.7.1 Threat of New Entrants
  • 4.7.2 Bargaining Power of Suppliers
  • 4.7.3 Bargaining Power of Buyers
  • 4.7.4 Threat of Substitutes
  • 4.7.5 Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS

• 5.1 By Component
  • 5.1.1 Software
  • 5.1.2 Services
• 5.2 By Application
  • 5.2.1 Medical Imaging and Diagnostics
  • 5.2.2 Image-Guided Surgery and Surgical Robotics
  • 5.2.3 Patient Monitoring and Safety
  • 5.2.4 Pathology and Laboratory Automation
  • 5.2.5 Clinical Trials, Drug Development, and Research
  • 5.2.6 Hospital Operations and Asset Intelligence
• 5.3 By Imaging Modality
  • 5.3.1 X-Ray and Digital Radiography
  • 5.3.2 Computed Tomography
  • 5.3.3 Magnetic Resonance Imaging
  • 5.3.4 Ultrasound
  • 5.3.5 Mammography and Digital Breast Tomosynthesis
  • 5.3.6 Positron Emission Tomography and Single-Photon Emission Computed Tomography
  • 5.3.7 Optical Coherence Tomography and Retinal Imaging
  • 5.3.8 Digital Pathology and Whole-Slide Imaging
  • 5.3.9 Endoscopy, Surgical Video, and Intraoperative Imaging
• 5.4 By Technology
  • 5.4.1 Deep Learning
  • 5.4.2 Computer Vision and Image Processing
  • 5.4.3 Natural Language Processing for Multimodal Reporting
  • 5.4.4 Generative AI and Vision-Language Models
  • 5.4.5 Machine Learning and Predictive Analytics
  • 5.4.6 Three-Dimensional Visualization and Extended Reality
  • 5.4.7 Edge AI and Federated Learning
• 5.5 By Deployment Mode
  • 5.5.1 On-Premise
  • 5.5.2 Cloud-Based
  • 5.5.3 Hybrid and Edge
• 5.6 By End User
  • 5.6.1 Hospitals and Specialty Clinics
  • 5.6.2 Diagnostic Imaging Centers
  • 5.6.3 Academic and Research Institutes
  • 5.6.4 Pharmaceutical and Biotechnology Companies
  • 5.6.5 Contract Research Organizations
  • 5.6.6 Ambulatory Surgical Centers
• 5.7 By Medical Specialty
  • 5.7.1 Radiology
  • 5.7.2 Oncology
  • 5.7.3 Cardiology
  • 5.7.4 Neurology
  • 5.7.5 Orthopedics
  • 5.7.6 Other Specialties (Pathology, Ophthalmology, and Others)
• 5.8 By Geography
  • 5.8.1 North America
    • 5.8.1.1 United States
    • 5.8.1.2 Canada
    • 5.8.1.3 Mexico
  • 5.8.2 Europe
    • 5.8.2.1 Germany
    • 5.8.2.2 United Kingdom
    • 5.8.2.3 France
    • 5.8.2.4 Italy
    • 5.8.2.5 Spain
    • 5.8.2.6 Rest of Europe
  • 5.8.3 Asia-Pacific
    • 5.8.3.1 China
    • 5.8.3.2 Japan
    • 5.8.3.3 India
    • 5.8.3.4 Australia
    • 5.8.3.5 South Korea
    • 5.8.3.6 Rest of Asia-Pacific
  • 5.8.4 Middle East
    • 5.8.4.1 GCC
    • 5.8.4.2 South Africa
    • 5.8.4.3 Rest of Middle East and Africa
  • 5.8.5 South America
    • 5.8.5.1 Brazil
    • 5.8.5.2 Argentina
    • 5.8.5.3 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 Alphabet Inc.
  • 6.3.3 Annalise.ai Pty Ltd.
  • 6.3.4 Arterys Inc.
  • 6.3.5 Butterfly Network, Inc.
  • 6.3.6 Canon Medical Systems Corporation
  • 6.3.7 Caption Health
  • 6.3.8 Cleerly, Inc.
  • 6.3.9 Enlitic, Inc.
  • 6.3.10 Fujifilm Holdings Corporation
  • 6.3.11 GE HealthCare Technologies Inc.
  • 6.3.12 HeartFlow, Inc.
  • 6.3.13 iCAD, Inc.
  • 6.3.14 Koninklijke Philips N.V.
  • 6.3.15 Lunit Inc.
  • 6.3.16 Microsoft Corporation
  • 6.3.17 NVIDIA Corporation
  • 6.3.18 Qure.ai Technologies Pvt. Ltd.
  • 6.3.19 Rad AI, Inc.
  • 6.3.20 Siemens Healthineers AG
  • 6.3.21 Tempus AI, Inc.
  • 6.3.22 Viz.ai Inc.
  • 6.3.23 VUNO, Inc.
  • 6.3.24 Shanghai United Imaging Healthcare Co., Ltd.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment
• 7.2 Future Outlook