AI In Radiology Market Size, Share & Trends Analysis Report By Component (AI-enabled Devices, Software & Platform), By Technology (Machine Learning Algorithms), By Modality (Computed Tomography (CT), Ultrasound), By Application, By End-use, By Region, And
Description
AI in Radiology Market Summary
The global AI in radiology market size was estimated at USD 10.57 billion in 2024 and is projected to reach USD 193.02 billion by 2033, growing at a CAGR of 38.12% from 2025 to 2033. Rising demand for accurate and early disease detection, rapid growth in imaging volumes, and government initiatives and regulatory support are significant factors contributing to market growth.
In addition, growing emphasis on cost-efficiency in healthcare delivery and technological advancements in machine learning, natural language processing, and computer vision are some other factors fueling market growth further. The rising demand for accurate and early disease detection is a primary driver of the AI in radiology market. Chronic conditions such as cancer, cardiovascular diseases, and neurological disorders are increasing globally, necessitating advanced imaging tools for early intervention. AI-enabled image analysis enhances diagnostic accuracy by detecting subtle abnormalities that might be missed by human interpretation. Automated quantification and risk stratification further improve diagnostic confidence. These capabilities reduce variability across radiologists and support personalized treatment planning. For instance, in April 2024, Exo launched FDA-cleared AI applications for heart failure diagnosis and lung assessment on its Exo Iris handheld ultrasound device. Thus, as healthcare systems focus on value-based care, the adoption of AI in radiology is becoming essential.
Technological advancements in machine learning, natural language processing, and computer vision fuel the growth of the AI in radiology industry. Deep learning algorithms are improving in accuracy, enabling more reliable interpretation of complex imaging modalities such as CT, MRI, and PET scans. For instance, iPETcertum, developed by Claritas NucMed Technologies, received FDA 510(k) clearance in August 2025. It is an AI-powered software device that enhances PET, PET-CT, and PET-MRI scans by automating the segmentation and quantification of tumors and lesions.
Integration of large language models and generative AI supports advanced clinical decision-making and structured reporting. For instance, in February 2025, RapidAI secured FDA 510(k) clearance for Lumina 3D, an AI-powered, automated 3D imaging reconstruction solution for head and neck CTA. Lumina 3D enables near real-time, high-quality 3D vascular images, reducing manual workflow burdens and improving diagnostic speed, accuracy, and procedural planning for neuroradiologists and neuro-interventionalists. Furthermore, AI tools are increasingly designed to integrate seamlessly with PACS and electronic health record systems. These innovations provide radiologists with actionable insights, creating a more connected and efficient diagnostic ecosystem.
Global AI In Radiology Market Report Segmentation
This report forecasts revenue growth at the global, regional, and country levels and provides an analysis of the latest industry trends in each of the sub-segments from 2021 to 2033. For this study, Grand View Research has segmented the global AI in radiology market report based on component, technology, modality, application, end use, and region:
The global AI in radiology market size was estimated at USD 10.57 billion in 2024 and is projected to reach USD 193.02 billion by 2033, growing at a CAGR of 38.12% from 2025 to 2033. Rising demand for accurate and early disease detection, rapid growth in imaging volumes, and government initiatives and regulatory support are significant factors contributing to market growth.
In addition, growing emphasis on cost-efficiency in healthcare delivery and technological advancements in machine learning, natural language processing, and computer vision are some other factors fueling market growth further. The rising demand for accurate and early disease detection is a primary driver of the AI in radiology market. Chronic conditions such as cancer, cardiovascular diseases, and neurological disorders are increasing globally, necessitating advanced imaging tools for early intervention. AI-enabled image analysis enhances diagnostic accuracy by detecting subtle abnormalities that might be missed by human interpretation. Automated quantification and risk stratification further improve diagnostic confidence. These capabilities reduce variability across radiologists and support personalized treatment planning. For instance, in April 2024, Exo launched FDA-cleared AI applications for heart failure diagnosis and lung assessment on its Exo Iris handheld ultrasound device. Thus, as healthcare systems focus on value-based care, the adoption of AI in radiology is becoming essential.
Technological advancements in machine learning, natural language processing, and computer vision fuel the growth of the AI in radiology industry. Deep learning algorithms are improving in accuracy, enabling more reliable interpretation of complex imaging modalities such as CT, MRI, and PET scans. For instance, iPETcertum, developed by Claritas NucMed Technologies, received FDA 510(k) clearance in August 2025. It is an AI-powered software device that enhances PET, PET-CT, and PET-MRI scans by automating the segmentation and quantification of tumors and lesions.
Integration of large language models and generative AI supports advanced clinical decision-making and structured reporting. For instance, in February 2025, RapidAI secured FDA 510(k) clearance for Lumina 3D, an AI-powered, automated 3D imaging reconstruction solution for head and neck CTA. Lumina 3D enables near real-time, high-quality 3D vascular images, reducing manual workflow burdens and improving diagnostic speed, accuracy, and procedural planning for neuroradiologists and neuro-interventionalists. Furthermore, AI tools are increasingly designed to integrate seamlessly with PACS and electronic health record systems. These innovations provide radiologists with actionable insights, creating a more connected and efficient diagnostic ecosystem.
Global AI In Radiology Market Report Segmentation
This report forecasts revenue growth at the global, regional, and country levels and provides an analysis of the latest industry trends in each of the sub-segments from 2021 to 2033. For this study, Grand View Research has segmented the global AI in radiology market report based on component, technology, modality, application, end use, and region:
- Component Outlook (Revenue, USD Million, 2021 - 2033)
- AI-enabled Devices
- Software & Platform
- Services
- Technology Outlook (Revenue, USD Million, 2021 - 2033)
- Machine Learning Algorithms
- Deep learning
- Supervised
- Unsupervised
- Others
- Natural Language Processing (NLP)
- Computer Vision Techniques
- Context-aware Computing
- Modality Outlook (Revenue, USD Million, 2021 - 2033)
- X-ray / Radiography
- Computed Tomography (CT)
- Magnetic Resonance Imaging (MRI)
- Ultrasound
- Mammography / Tomosynthesis
- Others
- Application Outlook (Revenue, USD Million, 2021 - 2033)
- Image Analysis
- Workflow Optimization
- Predictive Analytics
- Clinical Decision Support
- Others
- End Use Outlook (Revenue, USD Million, 2021 - 2033)
- Hospitals
- Clinics
- Diagnostic Imaging Centers
- Research and Academic Institutions
- Other
- Regional Outlook (Revenue, USD Million, 2021 - 2033)
- North America
- U.S.
- Canada
- Mexico
- Europe
- Germany
- UK
- France
- Italy
- Spain
- Denmark
- Sweden
- Norway
- Asia Pacific
- China
- Japan
- India
- South Korea
- Australia
- Thailand
- Latin America
- Brazil
- Argentina
- MEA
- South Africa
- Saudi Arabia
- UAE
- Kuwait
Table of Contents
100 Pages
- Chapter 1. Methodology and Scope
- 1.1. Market Segmentation & Scope
- 1.2. Market Definitions
- 1.2.1. Component Segment
- 1.2.2. Technology Segment
- 1.2.3. Modality Segment
- 1.2.4. Application Segment
- 1.2.5. End Use
- 1.3. Information analysis
- 1.3.1. Market formulation & data visualization
- 1.4. Data validation & publishing
- 1.5. Information Procurement
- 1.5.1. Primary Research
- 1.6. Information or Data Analysis
- 1.7. Market Formulation & Validation
- 1.8. Market Model
- 1.9. Total Market: CAGR Calculation
- 1.10. Objectives
- 1.10.1. Objective 1
- 1.10.2. Objective 2
- Chapter 2. Executive Summary
- 2.1. Market Outlook
- 2.2. Segment Snapshot
- 2.3. Competitive Insights Landscape
- Chapter 3. AI in Radiology Market Variables, Trends & Scope
- 3.1. Market Lineage Outlook
- 3.1.1. Parent market outlook
- 3.1.2. Related/ancillary market outlook.
- 3.2. Market Dynamics
- 3.2.1. Market driver analysis
- 3.2.1.1. Rising demand for accurate and early disease detection
- 3.2.1.2. Technological advancements in machine learning, natural language processing, and computer vision
- 3.2.1.3. Increasing government initiatives and regulatory support
- 3.2.2. Market restraint analysis
- 3.2.2.1. Data security and privacy concerns
- 3.2.2.2. High costs of advanced devices
- 3.2.3. Market opportunity analysis
- 3.2.4. Market challenges analysis
- 3.3. AI in Radiology Market Analysis Tools
- 3.3.1. Industry Analysis - Porter’s
- 3.3.1.1. Supplier power
- 3.3.1.2. Buyer power
- 3.3.1.3. Substitution threat
- 3.3.1.4. Threat of new entrant
- 3.3.1.5. Competitive rivalry
- 3.3.2. PESTEL Analysis
- 3.3.2.1. Political landscape
- 3.3.2.2. Technological landscape
- 3.3.2.3. Economic landscape
- 3.3.2.4. Environmental Landscape
- 3.3.2.5. Legal Landscape
- 3.3.2.6. Social Landscape
- 3.4. Case Studies
- 3.5. Investment & funding scenario
- 3.6. Technology Analysis: Key Use Cases and Application
- 3.6.1. Machine Learning (ML)/Deep Learning
- 3.6.2. Natural Language Processing (NLP)
- 3.6.3. Computer Vision Techniques
- 3.6.4. Context-aware Computing
- Chapter 4. AI in Radiology Market: Component Estimates & Trend Analysis
- 4.1. Segment Dashboard
- 4.2. Global AI in Radiology Market Component Movement Analysis
- 4.3. Global AI in Radiology Market Size & Trend Analysis, by Component, 2021 to 2033 (USD Million)
- 4.4. AI-enabled Devices
- 4.4.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
- 4.5. Software & Platform
- 4.5.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
- 4.6. Services
- 4.6.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
- Chapter 5. AI in Radiology Market: Technology Estimates & Trend Analysis
- 5.1. Segment Dashboard
- 5.2. Global AI in Radiology Market Technology Movement Analysis
- 5.3. Global AI in Radiology Market Size & Trend Analysis, by Technology, 2021 to 2033 (USD Million)
- 5.4. Machine Learning Algorithms
- 5.4.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
- 5.4.2. Deep learning
- 5.4.2.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
- 5.4.3. Supervised
- 5.4.3.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
- 5.4.4. Unsupervised
- 5.4.4.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
- 5.4.5. Others
- 5.4.5.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
- 5.5. Natural Language Processing (NLP)
- 5.5.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
- 5.6. Computer Vision Techniques
- 5.6.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
- 5.7. Context-aware Computing
- 5.7.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
- Chapter 6. AI in Radiology Market: Modality Estimates & Trend Analysis
- 6.1. Segment Dashboard
- 6.2. Global AI in Radiology Market Modality Movement Analysis
- 6.3. Global AI in Radiology Market Size & Trend Analysis, by Modality, 2021 to 2033 (USD Million)
- 6.4. X-ray / Radiography
- 6.4.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
- 6.5. Computed Tomography (CT)
- 6.5.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
- 6.6. Magnetic Resonance Imaging (MRI)
- 6.6.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
- 6.7. Ultrasound
- 6.7.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
- 6.8. Mammography / Tomosynthesis
- 6.8.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
- 6.9. Others
- 6.9.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
- Chapter 7. AI in Radiology Market: Application Estimates & Trend Analysis
- 7.1. Segment Dashboard
- 7.2. Global AI in Radiology Market Application Movement Analysis
- 7.3. Global AI in Radiology Market Size & Trend Analysis, by Application, 2021 to 2033 (USD Million)
- 7.4. Image Analysis
- 7.4.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
- 7.5. Workflow Optimization
- 7.5.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
- 7.6. Predictive Analytics
- 7.6.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
- 7.7. Clinical Decision Support
- 7.7.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
- 7.8. Others
- 7.8.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
- Chapter 8. AI in Radiology Market: End Use Estimates & Trend Analysis
- 8.1. Segment Dashboard
- 8.2. Global AI in Radiology Market End Use Movement Analysis
- 8.3. Global AI in Radiology Market Size & Trend Analysis, by End Use, 2021 to 2033 (USD Million)
- 8.4. Hospitals
- 8.4.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
- 8.5. Clinics
- 8.5.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
- 8.6. Diagnostic Imaging Centers
- 8.6.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
- 8.7. Research and Academic Institutions
- 8.7.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
- 8.8. Others
- 8.8.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
- Chapter 9. AI in Radiology Market: Regional Estimates & Trend Analysis
- 9.1. Regional Market Share Analysis, 2024 & 2033
- 9.2. Regional Market Dashboard
- 9.3. Market Size & Forecasts Trend Analysis, 2021 to 2033:
- 9.4. North America
- 9.4.1. U.S.
- 9.4.1.1. Key country dynamics
- 9.4.1.2. Regulatory framework
- 9.4.1.3. Competitive scenario
- 9.4.1.4. U.S. market estimates and forecasts, 2021 to 2033 (USD Million)
- 9.4.2. Canada
- 9.4.2.1. Key country dynamics
- 9.4.2.2. Regulatory framework
- 9.4.2.3. Competitive scenario
- 9.4.2.4. Canada market estimates and forecasts, 2021 to 2033 (USD Million)
- 9.4.3. Mexico
- 9.4.3.1. Key country dynamics
- 9.4.3.2. Regulatory framework
- 9.4.3.3. Competitive scenario
- 9.4.3.4. Mexico market estimates and forecasts, 2021 to 2033 (USD Million)
- 9.5. Europe
- 9.5.1. UK
- 9.5.1.1. Key country dynamics
- 9.5.1.2. Regulatory framework
- 9.5.1.3. Competitive scenario
- 9.5.1.4. UK market estimates and forecasts, 2021 to 2033 (USD Million)
- 9.5.2. Germany
- 9.5.2.1. Key country dynamics
- 9.5.2.2. Regulatory framework
- 9.5.2.3. Competitive scenario
- 9.5.2.4. Germany market estimates and forecasts, 2021 to 2033 (USD Million)
- 9.5.3. France
- 9.5.3.1. Key country dynamics
- 9.5.3.2. Regulatory framework
- 9.5.3.3. Competitive scenario
- 9.5.3.4. France market estimates and forecasts, 2021 to 2033 (USD Million)
- 9.5.4. Italy
- 9.5.4.1. Key country dynamics
- 9.5.4.2. Regulatory framework
- 9.5.4.3. Competitive scenario
- 9.5.4.4. Italy market estimates and forecasts, 2021 to 2033 (USD Million)
- 9.5.5. Spain
- 9.5.5.1. Key country dynamics
- 9.5.5.2. Regulatory framework
- 9.5.5.3. Competitive scenario
- 9.5.5.4. Spain market estimates and forecasts, 2021 to 2033 (USD Million)
- 9.5.6. Norway
- 9.5.6.1. Key country dynamics
- 9.5.6.2. Regulatory framework
- 9.5.6.3. Competitive scenario
- 9.5.6.4. Norway market estimates and forecasts, 2021 to 2033 (USD Million)
- 9.5.7. Sweden
- 9.5.7.1. Key country dynamics
- 9.5.7.2. Regulatory framework
- 9.5.7.3. Competitive scenario
- 9.5.7.4. Sweden market estimates and forecasts, 2021 to 2033 (USD Million)
- 9.5.8. Denmark
- 9.5.8.1. Key country dynamics
- 9.5.8.2. Regulatory framework
- 9.5.8.3. Competitive scenario
- 9.5.8.4. Denmark market estimates and forecasts, 2021 to 2033 (USD Million)
- 9.6. Asia Pacific
- 9.6.1. Japan
- 9.6.1.1. Key country dynamics
- 9.6.1.2. Regulatory framework
- 9.6.1.3. Competitive scenario
- 9.6.1.4. Japan market estimates and forecasts, 2021 to 2033 (USD Million)
- 9.6.2. China
- 9.6.2.1. Key country dynamics
- 9.6.2.2. Regulatory framework
- 9.6.2.3. Competitive scenario
- 9.6.2.4. China market estimates and forecasts, 2021 to 2033 (USD Million)
- 9.6.3. India
- 9.6.3.1. Key country dynamics
- 9.6.3.2. Regulatory framework
- 9.6.3.3. Competitive scenario
- 9.6.3.4. India market estimates and forecasts, 2021 to 2033 (USD Million)
- 9.6.4. Australia
- 9.6.4.1. Key country dynamics
- 9.6.4.2. Regulatory framework
- 9.6.4.3. Competitive scenario
- 9.6.4.4. Australia market estimates and forecasts, 2021 to 2033 (USD Million)
- 9.6.5. South Korea
- 9.6.5.1. Key country dynamics
- 9.6.5.2. Regulatory framework
- 9.6.5.3. Competitive scenario
- 9.6.5.4. South Korea market estimates and forecasts, 2021 to 2033 (USD Million)
- 9.6.6. Thailand
- 9.6.6.1. Key country dynamics
- 9.6.6.2. Regulatory framework
- 9.6.6.3. Competitive scenario
- 9.6.6.4. Thailand market estimates and forecasts, 2021 to 2033 (USD Million)
- 9.7. Latin America
- 9.7.1. Brazil
- 9.7.1.1. Key country dynamics
- 9.7.1.2. Regulatory framework
- 9.7.1.3. Competitive scenario
- 9.7.1.4. Brazil market estimates and forecasts, 2021 to 2033 (USD Million)
- 9.7.2. Argentina
- 9.7.2.1. Key country dynamics
- 9.7.2.2. Regulatory framework
- 9.7.2.3. Competitive scenario
- 9.7.2.4. Argentina market estimates and forecasts, 2021 to 2033 (USD Million)
- 9.8. MEA
- 9.8.1. South Africa
- 9.8.1.1. Key country dynamics
- 9.8.1.2. Regulatory framework
- 9.8.1.3. Competitive scenario
- 9.8.1.4. South Africa market estimates and forecasts, 2021 to 2033 (USD Million)
- 9.8.2. Saudi Arabia
- 9.8.2.1. Key country dynamics
- 9.8.2.2. Regulatory framework
- 9.8.2.3. Competitive scenario
- 9.8.2.4. Saudi Arabia market estimates and forecasts, 2021 to 2033 (USD Million)
- 9.8.3. UAE
- 9.8.3.1. Key country dynamics
- 9.8.3.2. Regulatory framework
- 9.8.3.3. Competitive scenario
- 9.8.3.4. UAE market estimates and forecasts, 2021 to 2033 (USD Million)
- 9.8.4. Kuwait
- 9.8.4.1. Key country dynamics
- 9.8.4.2. Regulatory framework
- 9.8.4.3. Competitive scenario
- 9.8.4.4. Kuwait market estimates and forecasts, 2021 to 2033 (USD Million)
- Chapter 10. Competitive Landscape
- 10.1. Company/Competition Categorization
- 10.2. Strategy Mapping
- 10.3. Company Market Position Analysis, 2024
- 10.4. Company Profiles/Listing
- 10.4.1. Siemens Healthineers AG
- 10.4.1.1. Company overview
- 10.4.1.2. Financial performance
- 10.4.1.3. Product benchmarking
- 10.4.1.4. Strategic initiatives
- 10.4.2. GE HealthCare
- 10.4.2.1. Company overview
- 10.4.2.2. Financial performance
- 10.4.2.3. Product benchmarking
- 10.4.2.4. Strategic initiatives
- 10.4.3. Koninklijke Philips N.V.
- 10.4.3.1. Company overview
- 10.4.3.2. Financial performance
- 10.4.3.3. Product benchmarking
- 10.4.3.4. Strategic initiatives
- 10.4.4. Canon Medical Systems
- 10.4.4.1. Company overview
- 10.4.4.2. Financial performance
- 10.4.4.3. Product benchmarking
- 10.4.4.4. Strategic initiatives
- 10.4.5. Fujifilm Holdings Corporation
- 10.4.5.1. Company overview
- 10.4.5.2. Financial performance
- 10.4.5.3. Product benchmarking
- 10.4.5.4. Strategic initiatives
- 10.4.6. Aidoc
- 10.4.6.1. Company overview
- 10.4.6.2. Financial performance
- 10.4.6.3. Product benchmarking
- 10.4.6.4. Strategic initiatives
- 10.4.7. Tempus AI
- 10.4.7.1. Company overview
- 10.4.7.2. Financial performance
- 10.4.7.3. Product benchmarking
- 10.4.7.4. Strategic initiatives
- 10.4.8. Lunit
- 10.4.8.1. Company overview
- 10.4.8.2. Financial performance
- 10.4.8.3. Product benchmarking
- 10.4.8.4. Strategic initiatives
- 10.4.9. Viz.ai
- 10.4.9.1. Company overview
- 10.4.9.2. Financial performance
- 10.4.9.3. Product benchmarking
- 10.4.9.4. Strategic initiatives
- 10.4.10. Riverain Technologies
- 10.4.10.1. Company overview
- 10.4.10.2. Financial performance
- 10.4.10.3. Product benchmarking
- 10.4.10.4. Strategic initiatives
- 10.4.11. Qure.ai
- 10.4.11.1. Company overview
- 10.4.11.2. Financial performance
- 10.4.11.3. Product benchmarking
- 10.4.11.4. Strategic initiatives
- 10.4.12. Infervision
- 10.4.12.1. Company overview
- 10.4.12.2. Financial performance
- 10.4.12.3. Product benchmarking
- 10.4.12.4. Strategic initiatives
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