Global Artificial Intelligence (AI) in Medical Imaging Market
Published Date:Jun 2025
Industry: Healthcare
Regions:
North America,
Europe,
Asia-Pacific,
Latin America,
Middle East & Africa
Format: PDF
Page: 200
Forecast Period: 2025-2033
Historical Range: 2020-2024
Global Artificial Intelligence
(AI) in Medical Imaging Market Segmentation, By Components (Software, Hardware,
Services), By Technology (Machine Learning (ML), Deep Learning (DL), Natural
Language Processing (NLP), Computer Vision), By Deployment Model (Cloud-Based
Solutions, On-Premises Solutions, Edge Computing Solutions), By Modality (X-ray,
Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Ultrasound, Mammography,
Positron Emission Tomography (PET), Single Photon Emission Computed Tomography
(SPECT), Other Imaging Modalities (e.g., Fluoroscopy, Thermography), By Application
(Neurology, Cardiology, Oncology, Pulmonology, Orthopaedics, Breast Imaging, Abdominal
& Pelvic Imaging, Others), By End Users (Hospitals, Diagnostic Imaging
Centres, Academic & Research Institutions, Pharmaceutical &
Biotechnology Companies, Contract Research Organizations (CROs), Outpatient
Clinics)- Industry Trends and Forecast to 2033
Global Artificial Intelligence
(AI) in Medical Imaging Market size was valued at USD 1384.7 million in 2024
and is expected to grow at a CAGR of 23.8% during the forecast period of 2025 to
2033.
Global Artificial Intelligence (AI) in Medical Imaging
Market Overview
The global Artificial
Intelligence (AI) in Medical Imaging Market is growing rapidly because of
greater needs for advanced diagnostic devices, which provide improved imaging
accuracy and optimized clinical procedures. X-rays and MRI scans leverage AI
through machine learning and computer vision technologies to enhance disease
detection and enable automated report generation. Market support grows from the
increased chronic disease rates, while healthcare providers enhance their AI
investments, and cloud-based imaging solutions broaden their reach. Hospitals
and diagnostic imaging centres, together with research institutions, quickly
implement AI-powered tools to lower patient care costs and reduce diagnostic
mistakes while improving patient care results. The global AI medical imaging
market will experience rapid growth throughout the next decade because of
enhanced regulatory approval procedures and improved system interoperability,
which will transform diagnostic medicine and radiology.
Global Artificial Intelligence (AI) in Medical Imaging
Market Scope
|
Factors |
Description |
|
Years
Considered |
·
Historical Period: 2020-2023 ·
Base Year: 2024 ·
Forecast Period: 2025-2033 |
|
Segments |
·
By Components: (Software, Hardware, Services) ·
By Technology: (Machine Learning (ML), Deep
Learning (DL), Natural Language Processing (NLP), Computer Vision) ·
By Deployment Model: (Cloud-Based Solutions,
On-Premises Solutions, Edge Computing Solutions) ·
By Modality: (X-ray, Magnetic Resonance
Imaging (MRI), Computed Tomography (CT), Ultrasound, Mammography, Positron
Emission Tomography (PET), Single Photon Emission Computed Tomography
(SPECT), Other Imaging Modalities (e.g., Fluoroscopy, Thermography) ·
By Application: (Neurology, Cardiology,
Oncology, Pulmonology, Orthopaedics, Breast Imaging, Abdominal & Pelvic
Imaging, Others) ·
By End Users: (Hospitals, Diagnostic Imaging
Centres, Academic & Research Institutions, Pharmaceutical &
Biotechnology Companies, Contract Research Organizations (CROs), Outpatient
Clinics) |
|
Countries
Catered |
North America ·
United States ·
Canada ·
Mexico Europe ·
United Kingdom ·
Germany ·
France ·
Spain ·
Italy ·
Rest of Europe Asia Pacific ·
China ·
India ·
Japan ·
Australia ·
South Korea ·
Rest of Asia Pacific Latin America ·
Brazil ·
Argentina ·
Rest of Latin America Middle East
& Africa ·
Saudi Arabia ·
South Africa ·
Rest of MEA |
|
Key Companies |
·
Enlitic ·
Qure.ai ·
Aidoc ·
Arterys ·
Oxipit ·
Quibim |
|
Market Trends |
·
Integration with PACS and Cloud Infrastructure ·
Adoption of AI for Workflow Automation |
Global Artificial Intelligence
(AI) in Medical Imaging Market Dynamics
The Global Artificial
Intelligence (AI) in Medical Imaging Market is experiencing transformative
growth is occurring within due to the advancement of technology and increasing
clinical needs. The growing number of chronic diseases like cancer, alongside cardiovascular
and neurological disorders, requires more accurate early diagnostic methods.
Deep learning and computer vision technologies help radiologists achieve
quicker and more accurate abnormality diagnoses. Due to healthcare system
overload and trained radiologist shortages, the deployment of AI automation
tools for better imaging workflows and reporting accuracy has been accelerated
to minimize diagnostic errors. MRI, CT, and ultrasound imaging technologies now
produce superior image reconstruction and segmentation by integrating AI, which
facilitates faster clinical decision-making.
The healthcare market develops
strength from the growth of cloud-based systems and government backing.
Companies like Siemens Healthiness, GE Healthcare, and IBM Watson Health fund
AI technology development through their financial investments. The main obstacles
to progress include substantial initial costs for implementation and data
privacy concerns that need clinical validation. AI-generated diagnostic systems
struggle to gain acceptance because healthcare professionals distrust them,
while regulatory frameworks remain unclear. Sustained research efforts combined
with increasing collaborations between tech firms and healthcare entities, plus
expanded clinical applications, will propel substantial market growth.
Global Artificial Intelligence
(AI) in Medical Imaging Market Segment Analysis
The Global Artificial
Intelligence (AI) in Medical Imaging Market, as well as its adoption
environment. Three fundamental parts make up the market structure, which
includes software, hardware, and services. Software solutions serve as the core
foundation for AI applications, including image recognition, data analytics,
and diagnostics. The hardware part of the system contains AI-integrated imaging
equipment with computing infrastructure and services that offer support for
implementation and maintenance, together with integration. The market employs
Machine Learning (ML) along with its branches, such as Deep Learning (DL), to
develop models capable of delivering precise image analysis and accurate
disease prediction. Natural Language Processing (NLP) processes radiology
reports and patient records automatically alongside Computer Vision, which
identifies imaging data patterns to deliver real-time diagnostic assistance.
The deployment model options
exist for organizations that consist of cloud-based systems together with
on-premises infrastructure and edge computing solutions. Cloud models gain
wider adoption through their scalable nature and ability to support real-time
collaboration, but organizations needing tighter data control opt for
on-premises systems. Edge computing technology evolves to provide real-time
diagnostic solutions for areas with restricted internet access. The imaging
modality category consists of various techniques, including X-ray, MRI, CT
scans, Ultrasound, and Mammography, that function as standard non-invasive
diagnostic methods. PET and SPECT imaging, along with Fluoroscopy and
Thermography, use artificial intelligence systems to enhance early disease
detection precision and diagnostic accuracy.
AI-enabled medical imaging
technology extends its application support to multiple medical specialties.
Medical professionals utilize AI technology extensively in neurology,
cardiology, and oncology for complex condition detection. AI systems produce
accurate diagnostic outcomes while optimizing workflows in pulmonology,
orthopaedics, breast imaging, and abdominal and pelvic imaging domains.
Dermatology and dental imaging applications represent the remaining emerging
use cases. The end users of the technology consist of hospitals as well as
diagnostic imaging centres and academic institutions, together with research
facilities and pharmaceutical companies, and biotechnology firms, alongside
contract research organizations (CROs) and outpatient clinics. Hospitals and
imaging centres take the lead in AI adoption due to their large-scale imaging
needs combined with efficiency demands, which set them apart from CROs and
academic institutions using AI for clinical research and innovation.
Global Artificial Intelligence
(AI) in Medical Imaging Market Regional Analysis
Regional adoption levels of
Global Artificial Intelligence (AI) in Medical Imaging Market vary according to
local healthcare infrastructure development status, technology readiness levels
and regulatory framework support. North America dominates the medical imaging
AI market because its advanced medical imaging facilities and robust AI
research networks host key industry players such as GE Healthcare and IBM
Watson Health. Through Europe Germany and the UK and France have made
substantial investments in AI healthcare solutions to improve diagnostic
accuracy and minimize healthcare costs. The Asia Pacific region has become the
fastest-growing market from China to Japan due to healthcare digitization
improvements and AI benefits awareness that regional government initiatives
support. The healthcare sector in major cities throughout Latin America along
with the Middle East and Africa maintains its growth trajectory through the
implementation of sophisticated AI imaging solutions and broadening access to
medical services. The worldwide expansion of AI adoption stems from the
necessity of current diagnostic methods alongside the need for personalized
patient treatment plans.
Global Artificial Intelligence (AI) in Medical Imaging Market Key Players
·
Siemens Healthineers
·
GE Healthcare
·
Philips Healthcare
·
IBM Watson Health
·
NVIDIA Corporation
·
Microsoft
·
Butterfly Network
·
Enlitic
·
Qure.ai
·
Aidoc
·
Arterys
·
Zebra Medical Vision
·
Oxipit
· Quibim
Recent Developments:
In May 2024, Samsung
Medison, a leading medical equipment subsidiary of Samsung Electronics,
acquired Sonio SAS. This strategic acquisition is aimed at enhancing Samsung
Medison’s capabilities in the field of AI-driven ultrasound diagnostics,
particularly in prenatal and fetal care. Sonio’s advanced software solutions
use artificial intelligence to assist clinicians in detecting abnormalities
during pregnancy through ultrasound imaging. The deal underscores Samsung’s
commitment to expanding its AI portfolio in medical imaging, with a strong
focus on integrating cutting-edge software to improve diagnostic accuracy,
workflow efficiency, and patient outcomes.
In January 2025, GE
HealthCare entered partnership with Sutter Health. The collaboration aims
to deploy GE’s suite of AI-powered imaging technologies, including PET/CT,
SPECT/CT, MRI, CT, ultrasound, and X-ray across Sutter’s network of over 300
facilities. The partnership includes workforce development initiatives such as
training programs for technologists, nurses, and physicians via Sutter Health
University to address staffing needs and support clinical adoption
Research Methodology
At Foreclaro Global Research, our
research methodology is firmly rooted in a comprehensive and systematic
approach to market research. We leverage a blend of reliable public and
proprietary data sources, including industry reports, government publications,
company filings, trade journals, investor presentations, and credible online
databases. Our analysts critically evaluate and triangulate information to
ensure accuracy, consistency, and depth of insights. We follow a top-down and
bottom-up data modelling framework to estimate market sizes and forecasts,
supplemented by competitive benchmarking and trend analysis. Each research
output is tailored to client needs, backed by transparent data validation
practices, and continuously refined to reflect dynamic market conditions.
Table of Contents
Chapter 1. Introduction
1.1. Report Description
1.2. Key Market Segments
1.3. Regulatory Scenario
1.4. Executive Summary
Chapter 2. Research Methodology
2.1. Secondary Research
2.2. Primary Research
2.3. Secondary Analyst Tools and Models
Chapter 3. Market Dynamics
3.1. Market
driver analysis
3.1.1. Advancements
in deep learning and image recognition
3.1.2. Rising
demand for early and accurate diagnosis
3.2. Market
restraint analysis
3.2.1. High
implementation and training costs
3.3. Market
Opportunity
3.3.1. Integration
of AI with cloud-based imaging platforms
3.4. Market
Challenges
3.4.1. Interoperability
between AI tools and existing PACS/RIS systems
Chapter 4. Market Variables and Outlook
4.1. SWOT
Analysis
4.1.1. Strengths
4.1.2. Weaknesses
4.1.3. Opportunities
4.1.4. Threats
4.2. PESTEL
Analysis
4.2.1. Political
Landscape
4.2.2. Economic Landscape
4.2.3. Social
Landscape
4.2.4. Technological
Landscape
4.2.5. Environmental
Landscape
4.2.6. Legal
Landscape
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 Substitute
4.3.4. Threat
of New Entrant
4.3.5. Competitive
Rivalry
4.4. Value
Chain Analysis
4.5. Covid
Impact Analysis
Chapter 5. Artificial Intelligence (AI) in Medical
Imaging Market: Components Estimates & Trend Analysis
5.1. Artificial
Intelligence (AI) in Medical Imaging Market value share and forecast, (2020 to
2033)
5.2. Incremental
Growth Analysis and Infographic Presentation
5.3. Software
5.4. Hardware
5.5. Services
Chapter 6. Artificial Intelligence (AI) in Medical
Imaging Market: Technology Estimates & Trend Analysis
6.1. Artificial
Intelligence (AI) in Medical Imaging Market value share and forecast, (2020 to
2033)
6.2. Incremental
Growth Analysis and Infographic Presentation
6.3. Machine
Learning (ML)
6.4. Deep
Learning (DL)
6.5. Natural
Language Processing (NLP)
6.6. Computer
Vision
Chapter 7. Artificial Intelligence (AI) in Medical
Imaging Market: Deployment Estimates & Trend Analysis
7.1. Artificial
Intelligence (AI) in Medical Imaging Market value share and forecast, (2020 to
2033)
7.2. Incremental
Growth Analysis and Infographic Presentation
7.3. Cloud-Based
Solutions
7.4. On-Premises
Solutions
7.5. Edge
Computing Solutions
Chapter 8. Artificial Intelligence (AI) in Medical
Imaging Market: Modality Estimates & Trend Analysis
8.1. Artificial
Intelligence (AI) in Medical Imaging Market value share and forecast, (2020 to
2033)
8.2. Incremental
Growth Analysis and Infographic Presentation
8.3. X-ray
8.4. Magnetic
Resonance Imaging (MRI)
8.5. Computed
Tomography (CT)
8.6. Ultrasound
8.7. Mammography
8.8. Positron
Emission Tomography (PET)
8.9. Single
Photon Emission Computed Tomography (SPECT)
8.10. Other
Imaging Modalities (e.g., Fluoroscopy, Thermography)
Chapter 9. Artificial Intelligence (AI) in Medical
Imaging Market: Application Estimates & Trend Analysis
9.1. Artificial
Intelligence (AI) in Medical Imaging Market value share and forecast, (2020 to
2033)
9.2. Incremental
Growth Analysis and Infographic Presentation
9.3. Neurology
9.4. Cardiology
9.5. Oncology
9.6. Pulmonology
9.7. Orthopedics
9.8. Breast
Imaging
9.9. Abdominal
& Pelvic Imaging
9.10. Others
Chapter 10. Artificial Intelligence (AI) in Medical
Imaging Market: End User Estimates & Trend Analysis
10.1. Artificial
Intelligence (AI) in Medical Imaging Market value share and forecast, (2020 to
2033)
10.2. Incremental
Growth Analysis and Infographic Presentation
10.3. Hospitals
10.4. Diagnostic
Imaging Centers
10.5. Academic
& Research Institutions
10.6. Pharmaceutical
& Biotechnology Companies
10.7. Contract
Research Organizations (CROs)
10.8. Outpatient
Clinics
Chapter 11. Artificial Intelligence (AI) in Medical
Imaging Market: Regional Estimates & Trend Analysis
11.1. Artificial
Intelligence (AI) in Medical Imaging Market value share and forecast, (2020 to
2033)
11.2. Incremental
Growth Analysis and Infographic Presentation
11.3. North
America
11.4. Europe
11.5. Asia
Pacific
11.6. Middle
East & Africa
11.7. Latin
America
Chapter 12. Artificial Intelligence (AI) in Medical
Imaging Market: Country Estimates & Trend Analysis
12.1. Artificial
Intelligence (AI) in Medical Imaging Market value share and forecast, (2020 to
2033)
12.2. Incremental
Growth Analysis and Infographic Presentation
12.3. United
States
12.4. Canada
12.5. Mexico
12.6. United
Kingdom
12.7. France
12.8. Germany
12.9. Italy
12.10. Spain
12.11. China
12.12. India
12.13. Japan
12.14. South
Korea
12.15. Australia
12.16. Brazil
12.17. Argentina
12.18. Saudi
Arabia
12.19. South
Africa
Chapter 13. Competitive Landscape
13.1. Company
Market Share Analysis
13.2. Vendor
Landscape
13.3. Competition
Dashboard
Chapter 14. Company Profiles
14.1. Siemens
Healthineers
14.1.1. Company
Overview
14.1.2. Financial
Details
14.1.3. Product
Analysis
14.1.4. Recent
Developments
14.2. GE
Healthcare
14.2.1. Company
Overview
14.2.2. Financial
Details
14.2.3. Product
Analysis
14.2.4. Recent
Developments
14.3. Philips
Healthcare
14.3.1. Company
Overview
14.3.2. Financial
Details
14.3.3. Product
Analysis
14.3.4. Recent
Developments
14.4. IBM
Watson Health
14.4.1. Company
Overview
14.4.2. Financial
Details
14.4.3. Product
Analysis
14.4.4. Recent
Developments
14.5. NVIDIA
Corporation
14.5.1. Company
Overview
14.5.2. Financial
Details
14.5.3. Product
Analysis
14.5.4. Recent
Developments
14.6. Microsoft
14.6.1. Company
Overview
14.6.2. Financial
Details
14.6.3. Product
Analysis
14.6.4. Recent
Developments
14.7. Butterfly
Network
14.7.1. Company
Overview
14.7.2. Financial
Details
14.7.3. Product
Analysis
14.7.4. Recent
Developments
14.8. Enlitic
14.8.1. Company
Overview
14.8.2. Financial
Details
14.8.3. Product
Analysis
14.8.4. Recent
Developments
14.9. Qure.ai
14.9.1. Company
Overview
14.9.2. Financial
Details
14.9.3. Product
Analysis
14.9.4. Recent
Developments
14.10. Aidoc
14.10.1.
Company Overview
14.10.2.
Financial Details
14.10.3.
Product Analysis
14.10.4.
Recent Developments
14.11. Arterys
14.11.1.
Company Overview
14.11.2.
Financial Details
14.11.3.
Product Analysis
14.11.4.
Recent Developments
14.12. Zebra
Medical Vision
14.12.1.
Company Overview
14.12.2.
Financial Details
14.12.3.
Product Analysis
14.12.4.
Recent Developments
14.13. Oxipit
14.13.1.
Company Overview
14.13.2.
Financial Details
14.13.3.
Product Analysis
14.13.4.
Recent Developments
14.14. Quibim
14.14.1.
Company Overview
14.14.2.
Financial Details
14.14.3.
Product Analysis
14.14.4.
Recent Developments
Segmentation
Artificial
Intelligence (AI) in Medical Imaging Market, By Components
Software
Hardware
Services
Artificial
Intelligence (AI) in Medical Imaging Market, By Technology
Machine
Learning (ML)
Deep
Learning (DL)
Natural
Language Processing (NLP)
Computer
Vision
Artificial
Intelligence (AI) in Medical Imaging Market, By Deployment Model
Cloud-Based
Solutions
On-Premises
Solutions
Edge
Computing Solutions
Artificial
Intelligence (AI) in Medical Imaging Market, By Modality
X-ray
Magnetic
Resonance Imaging (MRI)
Computed
Tomography (CT)
Ultrasound
Mammography
Positron
Emission Tomography (PET)
Single
Photon Emission Computed Tomography (SPECT)
Other
Imaging Modalities (e.g., Fluoroscopy, Thermography)
Artificial
Intelligence (AI) in Medical Imaging Market, By Application
Neurology
Cardiology
Oncology
Pulmonology
Orthopedics
Breast
Imaging
Abdominal
& Pelvic Imaging
Others
Artificial
Intelligence (AI) in Medical Imaging Market, By End User
Hospitals
Diagnostic
Imaging Centers
Academic
& Research Institutions
Pharmaceutical
& Biotechnology Companies
Contract
Research Organizations (CROs)
Outpatient
Clinics
Artificial
Intelligence (AI) in Medical Imaging Market, By Region
North
America
Europe
Asia Pacific
Latin America
Middle East & Africa
Methodology
Review our research methodology and quality standards for details about source selection, validation, forecasting, and review.
At Foreclaro Global Research, our research methodology is built to deliver clear, data-backed intelligence that supports confident decision-making. By combining rigorous secondary research, primary validations, and advanced forecasting models, we produce insights that are not only reliable but also strategically relevant for our clients.
1. Defining the Research Framework
Every study begins with a clear understanding of our client’s goals. We establish the market scope, define critical variables, and build a research framework tailored to the specific project. This upfront clarity ensures that our findings are sharply aligned with the strategic questions being addressed.
2. Robust Data Collection
Our analysts extract high-integrity data from a broad mix of credible sources including government databases, annual reports, regulatory filings, trade publications, scientific journals, and trusted industry portals. This secondary research is then supported with targeted primary inputs through interviews with key industry stakeholders—such as executives, subject matter experts, and channel partners—to capture real-world insights and contextual depth.
3. Advanced Forecasting and Modeling
To estimate market size and growth, we employ a hybrid of top-down and bottom-up modeling techniques. Our analysts apply proven forecasting models using historical data trends, economic indicators, technology adoption rates, and demand patterns. Sensitivity analysis and scenario modeling (base, optimistic, conservative) are incorporated to account for market volatility and uncertainty.
4. Data Triangulation and Validation
Accuracy is non-negotiable. We cross-validate every data point by triangulating it across three dimensions: source credibility, numerical consistency, and contextual alignment. This ensures our insights are not just statistically correct but strategically dependable. Discrepancies are resolved using subject expertise and multi-perspective reviews to deliver a balanced, unbiased analysis.
5. Quality Assurance and Final Review
Before delivery, each report undergoes a stringent quality assurance process. Our research output is reviewed for structure, clarity, consistency, and compliance with global standards. The final deliverable is tailored for decision-makers, whether it's a comprehensive industry report, data dashboard, or strategic presentation.
Why Our Methodology Works
What sets us apart is our adaptive data architecture and our commitment to analytical clarity. Every study is built with flexibility to accommodate dynamic markets, while our team blends quantitative rigor with domain-specific expertise. This allows us to deliver research that goes beyond information, we deliver intelligence that leads to action.
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