방사선 의학용 인공지능(AI) : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the AI in radiology market size is expected to grow from USD 1.85 billion in 2025 to USD 2.32 billion in 2026 and is forecast to reach USD 7.19 billion by 2031 at 25.38% CAGR over 2026-2031.

This report is Segmented by Component (Software, Services, Hardware), Technology (Deep Learning, Computer Vision, and More), Modality (CT, MRI, X-Ray, Ultrasound, Mammography, PET), Deployment (Cloud, On-Premise, Hybrid), Application (Detection, Segmentation, Triage, Analytics, Risk), End User (Hospitals and More), and Geography (North America and More). The Market Forecasts are Provided in Terms of Value (USD).

Global AI In Radiology Market Trends and Insights

Rising Imaging Volumes and Scan Backlogs

The AI in radiology market is expanding because scan volumes continue to rise faster than reading capacity in many health systems. In the United Kingdom, diagnostic imaging demand grows at more than 5% annually, while workforce supply has been expanding at close to 3%, and a 33% radiology staffing shortfall was documented in 2023. Aging populations, oncology surveillance, and wider use of advanced CT are keeping imaging workloads elevated across both developed and emerging care settings. This makes queue control a direct purchasing argument in the AI in radiology market, because providers want tools that can prioritize urgent studies and keep reporting delays from widening. The result is that buyers are looking beyond stand-alone detection and are asking whether a tool can reduce backlogs across the full imaging workflow. That demand pattern supports broader platform adoption in the AI in radiology market instead of narrow point-solution deployment.

Radiologist Shortage and Burnout Relief

The radiologist workforce gap is a long-term driver for the AI in radiology market because the training pipeline is not expanding fast enough to match care demand. Between 2014 and 2023, radiology job listings on the ACR Career Center totaled 31,825 against 10,180 anticipated residency graduates, leaving a cumulative deficit of 21,645 positions in the United States. A 2025 review also noted projections of a 122,000 radiologist shortage by 2032, which reinforces the view that this supply issue is structural rather than cyclical. In a 2025 Journal of the American College of Radiology survey, 100% of academic radiology department chairs planned AI implementation to improve quality and efficiency, and 95% planned it to reduce burnout. The practical value in the AI in radiology market is not only task automation, because cognitive offloading through prioritization, structured outputs, and draft support can lower fatigue without changing the radiologist's clinical role. That keeps adoption interest high across hospital networks that need productivity gains but still want physician oversight.

High Implementation Cost and ROI Uncertainty

Implementation cost is still a real brake on the AI in radiology market, especially for smaller health systems and imaging providers outside top-tier institutions. The cost issue goes beyond software licenses because buyers also face integration testing, workflow changes, staff retraining, and ongoing monitoring after deployment. In the 2025 Journal of the American College of Radiology survey, cost was identified as the leading concern among academic radiology department chairs evaluating AI deployment. Smaller providers often have less room to absorb uncertain returns, so the AI in radiology market can move more slowly where digital imaging infrastructure is still limited. The challenge is stronger when procurement teams cannot compare outcomes across similar facilities with a consistent cost-benefit model. That is why shared-risk pricing and managed service structures are gaining attention in the AI in radiology market, because they shift some of the commercial burden back to vendors.

Other drivers and restraints analyzed in the detailed report include:

1. Demand for Faster Triage and Turnaround Times
2. Regulatory Support for AI SaMD Approvals
3. Data Quality, Label Scarcity, and Annotation Cost

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

Segment Analysis

Software accounted for 42.31% of AI in radiology market share in 2025, while services are projected to grow at 27.38% CAGR through 2031. The current revenue lead reflects the installed base of cleared tools used for detection, classification, prioritization, and reporting support across hospital and imaging networks. In the AI in radiology market, software remains the first layer buyers evaluate because it directly affects reading workflow, case routing, and operational throughput. The installed base also gives software vendors a recurring opportunity to expand into adjacent imaging indications within the same health system. As a result, the software layer still anchors procurement decisions in the AI in radiology market.

Services are gaining faster because deployment is no longer limited to a single algorithm or a narrow pilot program. Enterprise buyers now want implementation support, model monitoring, retraining, governance, and change management as standard parts of the contract. The AI in radiology market therefore creates room for managed services that help hospitals run multiple clinical algorithms under one operating structure. Hardware continues to support growth through AI-accelerated scanner upgrades, but the revenue mix is shifting toward software and services as institutions prioritize flexible deployment and long-term support. That shift also reflects the wider move in the AI in radiology industry toward repeatable platform economics instead of one-time tool purchases.

Deep Learning held 55.24% of revenue in 2025, which kept it as the core technology foundation in the AI in radiology market. Its lead comes from established use in image classification, anomaly detection, segmentation, and organ quantification across CT and X-ray workflows. Because most deployed imaging algorithms still depend on these tasks, deep learning is likely to remain central to the installed base over the forecast period. Machine learning also supports the AI in radiology market through predictive models and risk stratification use cases that extend beyond direct image reading. Computer vision tools are increasingly being used during image acquisition to help assess quality and reduce the need for repeat studies.

Natural language processing is the fastest-growing technology in the AI in radiology market at 26.52% CAGR through 2031. That growth shows that the next control point is shifting toward reporting support, structured documentation, and automatic population of routine findings. A 2026 paper in Information Systems Frontiers described concept-enhanced multimodal retrieval-augmented generation as a viable path toward more interpretable and accurate radiology report generation. The reporting layer matters because radiologists need speed, consistency, and lower cognitive load, not only better image classification. This is one of the clearest areas where the AI in radiology industry can move from assisting image review to supporting the full reporting workflow.

Complete Report Scope:

• By Component
  • Software
  • Services
  • Hardware
• By Technology
  • Deep Learning
  • Machine Learning
  • Natural Language Processing
  • Computer Vision
• By Modality
  • Computed Tomography
  • Magnetic Resonance Imaging
  • X-ray
  • Ultrasound
  • Mammography
  • Positron Emission Tomography
  • Other Modalities
• By Deployment Mode
  • Cloud-Based
  • On-Premise
  • Hybrid
• By Application
  • Detection and Diagnosis
  • Image Segmentation and Classification
  • Workflow Optimization and Triage
  • Predictive and Prognostic Analytics
  • Disease Risk Assessment
  • Other Applications
• By End User
  • Hospitals and Clinics
  • Diagnostic Imaging Centers
  • Ambulatory Surgical Centers
  • Other End Users
• 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 and Africa
    • GCC
    • South Africa
    • Rest of Middle East and Africa
  • South America
    • Brazil
    • Argentina
    • Rest of South America

Geography Analysis

North America held 43.22% of AI in radiology market share in 2025, which kept it as the largest regional base for the AI in radiology market. The region has moved further than others from pilot testing into enterprise procurement, especially in hospital systems with strong digital imaging infrastructure. The United States remains the main driver because it combines large imaging volumes, active academic deployment, and a dense vendor base spanning OEMs and software specialists. In March 2026, GE HealthCare completed its USD 2.3 billion acquisition of Intelerad, which expanded its cloud-first enterprise imaging footprint across the United States, Canada, the United Kingdom, and Oceania. Canada and Mexico remain smaller contributors, but they benefit from proximity to the same interoperability standards and vendor ecosystem that support the AI in radiology market in the United States.

Europe remains the second-largest regional block in the AI in radiology market, with Germany, the United Kingdom, France, Italy, and Spain forming the core demand base. Germany has been especially important because hospital modernization funding accelerated imaging AI rollout at scale, including the 28-hospital Asklepios deployment reported by Aidoc in 2025. As of January 2025, at least 219 radiology AI products held EU CE certification, which shows the breadth of available products in the region. This gives the AI in radiology market in Europe a wide product base, even though compliance demands remain layered across national and EU-level requirements.

Asia-Pacific is the fastest-growing region in the AI in radiology market at 27.15% CAGR through 2031. China, Japan, South Korea, India, and Australia are the main growth engines because they combine healthcare digitalization with expanding imaging capacity and a rising number of local and imported AI products. China's National Medical Products Administration approved 76 innovative medical devices in 2025, up 17% year on year, which points to stronger product availability for advanced care technologies. In August 2025, Shanghai United Imaging Healthcare's uCT Ultima, described as the first domestically developed photon-counting spectral CT, received NMPA approval and entered clinical testing at major Shanghai hospitals. Japan also formalized its operating environment in 2025 through revised guidance from the Japan Radiological Society. Middle East and Africa are gaining traction through national digital health programs, while South America remains earlier stage, with Brazil and Argentina as the main openings for the AI in radiology market.

1. Aidoc Medical Ltd.
2. AIRS Medical, Inc.
3. Annalise.ai Pty Ltd.
4. Canon
5. DeepHealth, Inc.
6. Enlitic
7. FUJIFILM
8. GE HealthCare Technologies Inc.
9. Hologic
10. iCAD, Inc.
11. Koninklijke Philips
12. Lunit
13. Merative
14. Qure.ai Technologies Pvt. Ltd.
15. Rad AI, Inc.
16. Shanghai United Imaging Healthcare Co., Ltd.
17. Siemens Healthineers
18. Subtle Medical, Inc.
19. 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 & 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 Rising Imaging Volumes and Scan Backlogs
  • 4.2.2 Radiologist Shortage and Burnout Relief
  • 4.2.3 Demand for Faster Triage and Turnaround Times
  • 4.2.4 Regulatory Support for AI SaMD Approvals
  • 4.2.5 Enterprise PACS and EHR Interoperability Push
  • 4.2.6 Value-Based Care Pressure on Repeat-Scan Reduction
• 4.3 Market Restraints
  • 4.3.1 High Implementation Cost and ROI Uncertainty
  • 4.3.2 Data Quality, Label Scarcity, and Annotation Cost
  • 4.3.3 Regulatory Fragmentation Across Countries
  • 4.3.4 Low Trust in Black-Box Outputs for Edge Cases
• 4.4 Value Chain Analysis
• 4.5 Technological Outlook
• 4.6 Regulatory Landscape
• 4.7 Porter's Five Forces Analysis
  • 4.7.1 Bargaining Power of Suppliers
  • 4.7.2 Bargaining Power of Buyers
  • 4.7.3 Threat of New Entrants
  • 4.7.4 Threat of Substitutes
  • 4.7.5 Industry Rivalry

5 Market Size & Growth Forecasts (Value, USD)

• 5.1 By Component
  • 5.1.1 Software
  • 5.1.2 Services
  • 5.1.3 Hardware
• 5.2 By Technology
  • 5.2.1 Deep Learning
  • 5.2.2 Machine Learning
  • 5.2.3 Natural Language Processing
  • 5.2.4 Computer Vision
• 5.3 By Modality
  • 5.3.1 Computed Tomography
  • 5.3.2 Magnetic Resonance Imaging
  • 5.3.3 X-ray
  • 5.3.4 Ultrasound
  • 5.3.5 Mammography
  • 5.3.6 Positron Emission Tomography
  • 5.3.7 Other Modalities
• 5.4 By Deployment Mode
  • 5.4.1 Cloud-Based
  • 5.4.2 On-Premise
  • 5.4.3 Hybrid
• 5.5 By Application
  • 5.5.1 Detection and Diagnosis
  • 5.5.2 Image Segmentation and Classification
  • 5.5.3 Workflow Optimization and Triage
  • 5.5.4 Predictive and Prognostic Analytics
  • 5.5.5 Disease Risk Assessment
  • 5.5.6 Other Applications
• 5.6 By End User
  • 5.6.1 Hospitals and Clinics
  • 5.6.2 Diagnostic Imaging Centers
  • 5.6.3 Ambulatory Surgical Centers
  • 5.6.4 Other End Users
• 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 Japan
    • 5.7.3.3 India
    • 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 AIRS Medical, Inc.
  • 6.3.3 Annalise.ai Pty Ltd.
  • 6.3.4 Canon Medical Systems Corporation
  • 6.3.5 DeepHealth, Inc.
  • 6.3.6 Enlitic, Inc.
  • 6.3.7 Fujifilm Holdings Corporation
  • 6.3.8 GE HealthCare Technologies Inc.
  • 6.3.9 Hologic, Inc.
  • 6.3.10 iCAD, Inc.
  • 6.3.11 Koninklijke Philips N.V.
  • 6.3.12 Lunit Inc.
  • 6.3.13 Merative
  • 6.3.14 Qure.ai Technologies Pvt. Ltd.
  • 6.3.15 Rad AI, Inc.
  • 6.3.16 Shanghai United Imaging Healthcare Co., Ltd.
  • 6.3.17 Siemens Healthineers AG
  • 6.3.18 Subtle Medical, Inc.
  • 6.3.19 Viz.ai, Inc.

7 Market Opportunities & Future Outlook

• 7.1 White-Space and Unmet-Need Assessment