방사선 의학용 인공지능(AI) 시장 : 전략적 인사이트와 예측(2026-2031년)

The global AI in Radiology market is forecast to grow at a CAGR of 31.3%, reaching USD 15.2 billion in 2031 from USD 3.9 billion in 2026.

The global artificial intelligence (AI) in Radiology market is poised for strong growth through 2031 as healthcare providers increasingly adopt advanced AI technologies to enhance imaging diagnostics and workflow efficiency. AI solutions are transforming the radiology ecosystem by automating image analysis, improving disease detection accuracy, and reducing interpretive workloads for radiologists. The expansion of medical imaging procedures driven by rising incidences of chronic diseases and ageing populations further supports demand for AI-enabled radiology tools. Moreover, technological advancements in deep learning and machine learning are enabling more sophisticated applications that deliver faster and more reliable diagnostic insights. The confluence of rising healthcare expenditure, the need for enhanced diagnostic precision, and supportive digital health initiatives positions the AI in Radiology market for sustained expansion over the forecast period.

Market Drivers

One of the primary drivers of market growth is the increasing demand for improved diagnostic accuracy and faster image interpretation. AI algorithms can detect subtle patterns and anomalies in complex imaging data that may be difficult for the human eye to discern, thus enhancing early disease detection and treatment planning. This is particularly relevant in areas such as oncology and neurology where timely and precise interpretation of radiographic images is critical.

Healthcare providers are also adopting AI to address workforce challenges and rising imaging volumes. Radiology departments face workload pressures due to growing patient demand, limited specialist availability, and the complexity of diagnostic procedures. AI-enabled tools that automate routine tasks and support diagnostic workflows can help reduce turnaround times and improve overall operational efficiency.

Technological advancements in machine learning, deep learning, and computer vision are expanding the capabilities of AI applications in radiology. These technologies facilitate sophisticated image analysis, segmentation, and predictive analytics, enabling more accurate and consistent outputs. Continuous innovation by key technology vendors and partnerships with healthcare organisations are accelerating adoption of AI solutions across clinical environments.

Market Restraints

Despite robust growth prospects, the AI in Radiology market faces challenges related to data privacy, regulatory compliance, and integration complexity. Healthcare data is highly sensitive, and stringent regulations governing patient information require rigorous safeguards for AI implementations. Ensuring compliance with varying regulatory frameworks across regions can increase deployment complexity and cost.

Integration of AI solutions with existing hospital information systems, such as picture archiving and communication systems (PACS) and radiology information systems (RIS), can be technically challenging. Legacy infrastructure and interoperability issues may slow the adoption of new technologies, particularly in resource-constrained clinical settings.

Another restraint is the need for high-quality, annotated medical imaging datasets to train and validate AI models. Variability in data standards and limited access to diverse datasets can impact model performance and clinical acceptance. Addressing these data challenges is essential to ensure reliable AI outputs and build clinician trust.

Technology and Segment Insights

The AI in Radiology market encompasses various technology segments, including computer-aided detection, auto-segmentation, natural language processing, and quantitative imaging analytics. Computer-aided detection is widely used to support image interpretation, while emerging technologies enable enhanced automation and decision support.

Application segments include mammography, chest imaging, neurology, cardiovascular imaging, and others. AI is extensively used for image analysis and risk assessment across these applications, helping clinicians to prioritise and interpret high volumes of imaging studies. End-users include hospitals, diagnostic imaging centres, and research institutions, with hospitals accounting for a significant share due to high procedural volumes and diagnostic demand.

Competitive and Strategic Outlook

The competitive landscape comprises technology companies and specialised AI solution providers that offer platforms and services tailored to radiology needs. Key players include Microsoft Corporation, Amazon Web Services, IBM Corporation, Rad AI, and Behold.ai, among others. These firms focus on product innovation, strategic partnerships, and integration of AI capabilities into broader healthcare IT ecosystems to expand market reach.

Strategic initiatives in the market include enhancing AI functionalities for clinical decision support, expanding geographic presence, and collaborating with healthcare institutions to co-develop tailored solutions. Vendors are also investing in validation studies and regulatory approvals to strengthen clinical credibility and facilitate wider adoption.

Key Takeaways

The AI in Radiology market is on a strong growth trajectory through 2031, driven by rising demand for improved diagnostic capabilities, operational efficiencies, and innovative AI technologies. While data governance and integration challenges persist, the strategic value of AI in enhancing radiology workflows and patient outcomes will continue to propel market growth.

Key Benefits of this Report

• Insightful Analysis: Gain detailed market insights across regions, customer segments, policies, socio-economic factors, consumer preferences, and industry verticals.
• Competitive Landscape: Understand strategic moves by key players to identify optimal market entry approaches.
• Market Drivers and Future Trends: Assess major growth forces and emerging developments shaping the market.
• Actionable Recommendations: Support strategic decisions to unlock new revenue streams.
• Caters to a Wide Audience: Suitable for startups, research institutions, consultants, SMEs, and large enterprises.

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Report Coverage

• Historical data from 2021 to 2025 and forecast data from 2026 to 2031
• Growth opportunities, challenges, supply chain outlook, regulatory framework, and trend analysis
• Competitive positioning, strategies, and market share evaluation
• Revenue growth and forecast assessment across segments and regions
• Company profiling including strategies, products, financials, and key developments

TABLE OF CONTENTS

1. INTRODUCTION

• 1.1. Market Overview
• 1.2. Market Definition
• 1.3. Scope of the Study
• 1.4. Market Segmentation
• 1.5. Currency
• 1.6. Assumptions
• 1.7. Base and Forecast Years Timeline
• 1.8. Key Benefits for the Stakeholders

2. RESEARCH METHODOLOGY

• 2.1. Research Design
• 2.2. Research Process

3. EXECUTIVE SUMMARY

• 3.1. Key Findings

4. MARKET DYNAMICS

• 4.1. Market Drivers
• 4.2. Market Restraints
• 4.3. Porter's Five Forces Analysis
  • 4.3.1. Bargaining Power of Suppliers
  • 4.3.2. Bargaining Power of Buyers
  • 4.3.3. Threat of New Entrants
  • 4.3.4. Threat of Substitutes
  • 4.3.5. Competitive Rivalry in the Industry
• 4.4. Industry Value Chain Analysis
• 4.5. Analyst View

5. AI IN RADIOLOGY MARKET BY TECHNOLOGY

• 5.1. Introduction
• 5.2. Computer-aided Detection
• 5.3. Auto-segmentation of Organs
• 5.4. Natural Language Processing
• 5.5. Consultation
• 5.6. Quantification and Kinetics
• 5.7. Others

6. AI IN RADIOLOGY MARKET BY APPLICATION

• 6.1. Introduction
• 6.2. Mammography
• 6.3. Chest Imaging
• 6.4. Neurology
• 6.5. Cardiovascular
• 6.6. Others

7. AI IN RADIOLOGY MARKET BY END-USER

• 7.1. Introduction
• 7.2. Hospitals
• 7.3. Diagnostic Imaging Centers
• 7.4. Others

8. AI IN RADIOLOGY MARKET BY GEOGRAPHY

• 8.1. Introduction
• 8.2. North America
  • 8.2.1. By Technology
  • 8.2.2. By Application
  • 8.2.3. By End-User
  • 8.2.4. By Country
    • 8.2.4.1. USA
    • 8.2.4.2. Canada
    • 8.2.4.3. Mexico
• 8.3. South America
  • 8.3.1. By Technology
  • 8.3.2. By Application
  • 8.3.3. By End-User
  • 8.3.4. By Country
    • 8.3.4.1. Brazil
    • 8.3.4.2. Argentina
    • 8.3.4.3. Others
• 8.4. Europe
  • 8.4.1. By Technology
  • 8.4.2. By Application
  • 8.4.3. By End-User
  • 8.4.4. By Country
    • 8.4.4.1. Germany
    • 8.4.4.2. France
    • 8.4.4.3. United Kingdom
    • 8.4.4.4. Spain
    • 8.4.4.5. Others
• 8.5. Middle East and Africa
  • 8.5.1. By Technology
  • 8.5.2. By Application
  • 8.5.3. By End-User
  • 8.5.4. By Country
    • 8.5.4.1. Saudi Arabia
    • 8.5.4.2. UAE
    • 8.5.4.3. Israel
    • 8.5.4.4. Others
• 8.6. Asia Pacific
  • 8.6.1. By Technology
  • 8.6.2. By Application
  • 8.6.3. By End-User
  • 8.6.4. By Country
    • 8.6.4.1. China
    • 8.6.4.2. Japan
    • 8.6.4.3. India
    • 8.6.4.4. South Korea
    • 8.6.4.5. Taiwan
    • 8.6.4.6. Indonesia
    • 8.6.4.7. Others

9. COMPETITIVE ENVIRONMENT AND ANALYSIS

• 9.1. Major Players and Strategy Analysis
• 9.2. Market Share Analysis
• 9.3. Mergers, Acquisitions, Agreements, and Collaborations
• 9.4. Competitive Dashboard

10. COMPANY PROFILES

• 10.1. Microsoft Corporation
• 10.2. Amazon Web Services Inc.
• 10.3. IBM Corporation
• 10.4. Rad AI
• 10.5. Behold.ai
• 10.6. IMAGEN
• 10.7. Aidoc
• 10.8. Koninklijke Philips N.V.
• 10.9. GE Healthcare
• 10.10. Siemens Healthcare GmbH