의료 영상용 인공지능(AI) 시장 : 구성 요소, 영상 기술, 용도, 최종 사용자, 도입 형태별 - 세계 시장 예측(2026-2032년)

The Artificial Intelligence in Medical Imaging Market is projected to grow by USD 6.21 billion at a CAGR of 18.12% by 2032.

AI in Medical Imaging Executive Summary

Artificial intelligence in medical imaging is moving from experimental image analysis to regulated clinical deployment across radiology, cardiology, oncology, neurology, pathology-adjacent imaging, and emergency care. The strongest adoption is concentrated in AI radiology software, deep learning image reconstruction, triage algorithms, lesion detection, segmentation, workflow orchestration, and clinical decision support connected to PACS, RIS, VNA, and electronic health records.

The commercial case is supported by measurable healthcare pressure. The World Health Organization projects a global shortfall of 10 million health workers by 2030, while aging populations and chronic disease are increasing demand for CT, MRI, ultrasound, X-ray, mammography, and nuclear medicine. FDA public data also show more than 950 AI/ML-enabled medical devices authorized by 2024, with radiology representing the largest clinical category, confirming medical imaging as the leading regulated entry point for healthcare AI.

Transformative Shifts in the AI Imaging Landscape

The market landscape is being reshaped by three linked shifts: clinically validated algorithms, enterprise deployment, and regulatory maturity. Buyers are no longer evaluating AI tools as isolated detection products; they are prioritizing interoperable platforms that improve turnaround time, reduce reporting variation, support radiologist productivity, and integrate with existing imaging informatics infrastructure.

Generative AI and multimodal models are also changing product roadmaps, but adoption remains anchored in evidence, safety, and workflow fit. The EU Artificial Intelligence Act, FDA software-as-a-medical-device oversight, the International Medical Device Regulators Forum framework, and growing hospital AI governance programs are pushing vendors toward transparent performance monitoring, cybersecurity controls, bias testing, clinical risk management, and post-market surveillance.

Cumulative Impact of Artificial Intelligence on Imaging

The cumulative impact of artificial intelligence is most visible in high-volume, time-sensitive imaging pathways. AI-based triage can flag suspected intracranial hemorrhage, pulmonary embolism, pneumothorax, stroke-related findings, and critical chest abnormalities so radiology teams can prioritize urgent studies. In screening programs, AI supports detection and reading efficiency in mammography, lung nodule assessment, diabetic eye imaging, and fracture identification.

Operationally, AI reduces repetitive measurement tasks, accelerates image reconstruction, standardizes quantitative reporting, and helps manage backlog. The impact is not a replacement of clinicians; it is a shift toward augmented radiology, where machine learning handles pattern recognition and workflow automation while physicians retain diagnostic accountability, final interpretation, and patient-level clinical responsibility.

Key Regional Insights for AI Medical Imaging

North America leads AI medical imaging commercialization because of high imaging volumes, advanced hospital IT infrastructure, large clinical research networks, and a clear FDA pathway for AI/ML-enabled medical devices. The United States remains the primary launch market for regulated AI radiology software, while Canada is advancing responsible AI through provincial digital health systems, privacy frameworks, and academic hospital networks focused on safe clinical implementation.

Europe is shaped by strong research institutions, national radiology societies, and the EU AI Act, which classifies many AI medical devices as high risk and raises expectations for data quality, human oversight, transparency, and post-market monitoring. Asia-Pacific is scaling rapidly as China, Japan, South Korea, India, and Australia combine large patient populations, national AI strategies, aging demographics, and expanding imaging capacity. Latin America is seeing rising demand where AI can help extend access in Brazil and Mexico; the Middle East is accelerating adoption through digital hospital investments in the UAE and Saudi Arabia; and Africa shows practical need for AI-enabled teleradiology and point-of-care imaging support, with South Africa acting as a key implementation hub.

Key Group Insights Across ASEAN, GCC, EU, BRICS, G7, and NATO

ASEAN is becoming a high-potential region for AI in medical imaging because urban hospitals are investing in digital imaging while rural systems need scalable diagnostic support. Cloud-enabled AI, teleradiology, and portable ultrasound analytics are especially relevant where specialist access is uneven across Indonesia, Vietnam, Thailand, Malaysia, Singapore, and the Philippines, and where regional digital health strategies are improving connectivity and clinical data exchange.

The GCC is accelerating AI adoption through national health modernization programs, including Saudi Vision 2030 and the UAE National Strategy for Artificial Intelligence 2031, with hospitals prioritizing smart imaging departments, radiology workflow automation, and secure health data platforms. The European Union is setting the compliance benchmark through the EU AI Act, GDPR, and MDR-aligned medical device expectations. BRICS markets offer scale, large imaging backlogs, and unmet diagnostic demand across China, India, Brazil, Russia, and South Africa, while the G7 remains central for premium imaging systems, reimbursement evidence, clinical validation, and regulatory convergence. NATO countries add emphasis on cybersecurity, resilience, secure health data exchange, and operational continuity for connected imaging infrastructure.

Key Country Insights in AI Medical Imaging

The United States is the largest regulated AI imaging market, supported by FDA authorizations, academic medical centers, enterprise radiology networks, and widespread deployment of PACS and EHR-connected clinical workflows. Canada emphasizes responsible AI deployment through public health systems and privacy-focused governance, while Mexico and Brazil are using digital health expansion to improve diagnostic access across public and private systems. In Europe, the United Kingdom is investing in AI diagnostics through NHS programs, and Germany, France, Italy, and Spain are balancing innovation with strict data protection, medical device regulation, and radiology workforce pressures. Russia maintains domestic AI and imaging capabilities despite constrained international technology flows and a stronger focus on locally deployable platforms.

China is scaling AI imaging through large hospital networks, national AI policy support, and NMPA oversight, while India's diagnostic demand is supported by expanding health coverage, digital health infrastructure, and the need to serve large underserved populations. Japan and South Korea are strong in aging-related imaging, robotics, semiconductor-enabled medical technology, and advanced electronics. Australia combines high-quality clinical research with telehealth maturity and rural imaging needs, making it a practical market for validated AI radiology, remote reporting, and workflow optimization tools.

Actionable Recommendations for Industry Leaders

Industry leaders should prioritize clinically meaningful use cases where AI improves speed, accuracy, consistency, or access. The strongest opportunities are in emergency triage, cancer screening, image reconstruction, quantitative imaging, reporting automation, clinical workflow orchestration, and radiologist productivity enhancement.

Vendors should build evidence packages that include external validation, subgroup performance, real-world monitoring, cybersecurity documentation, data provenance, and integration proof with PACS, RIS, EHR, VNA, and cloud environments. Providers should establish AI governance committees, define radiologist-in-the-loop workflows, monitor algorithm drift, validate performance on local populations, and negotiate contracts based on measurable operational and clinical outcomes rather than claims of general automation.

Research Methodology for AI Medical Imaging Analysis

This executive summary is developed from verified public-domain evidence, regulatory databases, health authority guidance, and observable industry adoption signals. Core inputs include FDA AI/ML-enabled medical device listings, WHO workforce and disease-burden data, EU AI Act requirements, national digital health strategies, medical device regulatory guidance, and peer-reviewed evidence on AI-assisted imaging performance.

The methodology prioritizes triangulation across regulatory approvals, clinical workflow relevance, geographic adoption indicators, infrastructure readiness, and documented healthcare system pressures. Market interpretation excludes unsupported forecasts and focuses on observable drivers such as imaging demand, workforce constraints, reimbursement scrutiny, data governance, cybersecurity requirements, interoperability standards, and post-deployment performance monitoring.

Conclusion: The Future of AI-Enabled Diagnostic Imaging

Artificial intelligence in medical imaging has become one of the most mature areas of healthcare AI because it addresses measurable needs in diagnostic speed, imaging workload, radiology productivity, and clinical consistency. Its value is strongest when algorithms are validated, regulated, integrated into workflow, and monitored after deployment.

The next phase of adoption will favor organizations that combine clinical evidence with enterprise scalability, ethical data practices, cybersecurity readiness, and measurable productivity gains. As imaging volumes increase and specialist shortages persist, AI-enabled diagnostic imaging will remain a strategic priority for health systems, technology vendors, payers, and policymakers worldwide.

Table of Contents

1. Preface

• 1.1. Objectives of the Study
• 1.2. Market Definition
• 1.3. Market Segmentation & Coverage
• 1.4. Years Considered for the Study
• 1.5. Currency Considered for the Study
• 1.6. Language Considered for the Study
• 1.7. Key Stakeholders

2. Research Methodology

• 2.1. Introduction
• 2.2. Research Design
  • 2.2.1. Primary Research
  • 2.2.2. Secondary Research
• 2.3. Research Framework
  • 2.3.1. Qualitative Analysis
  • 2.3.2. Quantitative Analysis
• 2.4. Market Size Estimation
  • 2.4.1. Top-Down Approach
  • 2.4.2. Bottom-Up Approach
• 2.5. Data Triangulation
• 2.6. Research Outcomes
• 2.7. Research Assumptions
• 2.8. Research Limitations

3. Executive Summary

• 3.1. Introduction
• 3.2. CXO Perspective
• 3.3. Market Size & Growth Trends
• 3.4. Market Share Analysis, 2025
• 3.5. FPNV Positioning Matrix, 2025
• 3.6. New Revenue Opportunities
• 3.7. Next-Generation Business Models
• 3.8. Industry Roadmap

4. Market Overview

• 4.1. Introduction
• 4.2. Industry Ecosystem & Value Chain Analysis
  • 4.2.1. Supply-Side Analysis
  • 4.2.2. Demand-Side Analysis
  • 4.2.3. Stakeholder Analysis
• 4.3. Market Dynamics
  • 4.3.1. Key Drivers
  • 4.3.2. Key Restraints
  • 4.3.3. Key Opportunities
  • 4.3.4. Key Challenges
• 4.4. Porter's Five Forces Analysis
• 4.5. PESTLE Analysis
• 4.6. Market Outlook
  • 4.6.1. Near-Term Market Outlook (0-2 Years)
  • 4.6.2. Medium-Term Market Outlook (3-5 Years)
  • 4.6.3. Long-Term Market Outlook (5-10 Years)
• 4.7. Go-to-Market Strategy

5. Market Insights

• 5.1. Consumer Insights & End-User Perspective
• 5.2. Consumer Experience Benchmarking
• 5.3. Opportunity Mapping
• 5.4. Distribution Channel Analysis
• 5.5. Pricing Trend Analysis
• 5.6. Regulatory Compliance & Standards Framework
• 5.7. ESG & Sustainability Analysis
• 5.8. Disruption & Risk Scenarios
• 5.9. Return on Investment & Cost-Benefit Analysis

6. Cumulative Impact of Artificial Intelligence 2026

7. Artificial Intelligence in Medical Imaging Market, by Component

• 7.1. Hardware
• 7.2. Services
  • 7.2.1. Managed Services
  • 7.2.2. Professional Services
• 7.3. Software

8. Artificial Intelligence in Medical Imaging Market, by Imaging Technology

• 8.1. CT Scanners
• 8.2. MRI Systems
• 8.3. Ultrasound Devices
• 8.4. X-ray Systems

9. Artificial Intelligence in Medical Imaging Market, by Application

• 9.1. Cardiology
• 9.2. Neurology
• 9.3. Oncology
• 9.4. Pathology
• 9.5. Radiology

10. Artificial Intelligence in Medical Imaging Market, by End-User

• 10.1. Academic & Research Institutions
• 10.2. Diagnostic Centers
• 10.3. Hospitals & Clinics

11. Artificial Intelligence in Medical Imaging Market, by Deployment Type

• 11.1. On Premise Systems
• 11.2. Cloud Based Systems

12. Artificial Intelligence in Medical Imaging Market, by Region

• 12.1. Asia-Pacific
• 12.2. North America
• 12.3. Latin America
• 12.4. Europe
• 12.5. Middle East
• 12.6. Africa

13. Artificial Intelligence in Medical Imaging Market, by Group

• 13.1. ASEAN
• 13.2. GCC
• 13.3. European Union
• 13.4. BRICS
• 13.5. G7
• 13.6. NATO

14. Artificial Intelligence in Medical Imaging Market, by Country

• 14.1. United States
• 14.2. Canada
• 14.3. Mexico
• 14.4. Brazil
• 14.5. United Kingdom
• 14.6. Germany
• 14.7. France
• 14.8. Russia
• 14.9. Italy
• 14.10. Spain
• 14.11. China
• 14.12. India
• 14.13. Japan
• 14.14. Australia
• 14.15. South Korea

15. Competitive Landscape

• 15.1. Market Concentration Analysis, 2025
  • 15.1.1. Concentration Ratio (CR)
  • 15.1.2. Herfindahl Hirschman Index (HHI)
• 15.2. Recent Developments & Impact Analysis, 2025
• 15.3. Product Portfolio Analysis, 2025
• 15.4. Benchmarking Analysis, 2025

16. Company Profiles

• 16.1. Agfa-Gevaert Group
• 16.2. Aidoc Medical Ltd.
• 16.3. Behold.ai
• 16.4. Butterfly Network, Inc.
• 16.5. Canon Medical Systems Corp.
• 16.6. Clarius Mobile Health Corp.
• 16.7. Cleerly, Inc.
• 16.8. DeepTek Inc.
• 16.9. EchoNous, Inc.
• 16.10. Enlitic, Inc.
• 16.11. Fujifilm Holdings Corp.
• 16.12. GE HealthCare Technologies Inc.
• 16.13. HeartFlow, Inc.
• 16.14. iCAD, Inc.
• 16.15. Imagene AI Ltd.
• 16.16. Infervision
• 16.17. Koninklijke Philips N.V.
• 16.18. Lunit Inc.
• 16.19. Oxipit UAB
• 16.20. PathAI, Inc.
• 16.21. Quibim S.L.
• 16.22. Qure.ai Technologies Private Limited
• 16.23. RapidAI, Inc.
• 16.24. Sectra AB
• 16.25. Siemens Healthineers AG
• 16.26. Subtle Medical, Inc.
• 16.27. Tempus AI, Inc.
• 16.28. Visage Imaging GmbH
• 16.29. Viz.ai, Inc.
• 16.30. Zebra Medical Vision Ltd.