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Medical imaging volumes are growing faster than the radiologist workforce, and artificial intelligence is becoming a core tool for triage, detection and reporting. According to IG Transformation Partners (IGTPS), the global AI in medical imaging market was valued at USD 2.05 billion in 2025 and is projected to reach USD 23.60 billion by 2034, growing at a CAGR of 31.2% during 2026 to 2034. The complete AI in medical imaging market report provides the full forecast, segmentation and competitive analysis.

This article covers the market’s definition, growth drivers, regulatory environment, segments, regions and key companies, and what they mean for vendors, hospitals and imaging networks.

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What Is the AI in Medical Imaging Market?

The AI in medical imaging market covers software, hardware and services that apply machine learning, deep learning and natural language processing to the acquisition, reconstruction, interpretation and reporting of X-ray, CT, MRI, ultrasound and nuclear imaging studies.

IGTPS segments the market by component (software, hardware, services), technology (machine learning, deep learning, natural language processing, computer vision, others), modality (CT scan, MRI, X-ray, ultrasound, nuclear imaging), application (neurology, cardiology, breast screening, pulmonology, orthopedics, others), end user (hospitals, diagnostic imaging centers, ambulatory care centers, research institutes, others) and region.

The market is shifting from single-finding, single-modality algorithms toward foundation-model platforms that triage multiple acute conditions from one CT or X-ray study within one regulatory submission. Vendors are bundling detection, prioritization, quantification and draft reporting into unified platforms that plug into hospital PACS.

AI in Medical Imaging Market Size, Share and Forecast

IGTPS values the market at USD 2.05 billion in 2025 and USD 2.69 billion in 2026, rising to USD 23.60 billion by 2034, about 11.5 times the 2025 base.

Key Market Trends and Developments

Foundation Models and Generative AI

Vendors are moving away from single-finding algorithms toward foundation models trained on millions of scans that can flag more than a dozen acute conditions from one study. This reduces the number of point solutions hospitals must integrate and validate.

Draft Report Generation

Generative AI is being layered onto detection to draft preliminary radiology report text from chest radiographs and other studies. Early deployments position these tools as an assistive second step after triage, not a replacement for physician sign-off.

AI Embedded in Scanner Hardware

Large imaging manufacturers embed AI reconstruction and enhancement into scanners, allowing shorter acquisition times and reduced radiation or contrast dose. This convergence expands the addressable market beyond stand-alone software into bundled equipment.

Growing Authorization Volume

FDA data for the first quarter of 2026 shows a cumulative 1,524 AI-enabled medical devices authorized since tracking began in 1995, of which roughly three-quarters are radiology applications.

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What Is Driving AI in Medical Imaging Market Growth?

Rising Imaging Volumes

Hospitals perform a growing number of CT, MRI and X-ray exams each year, accelerated by ageing populations and expanding cancer and cardiovascular screening. Imaging volume is outpacing growth in trained radiologists.

Radiologist Shortages and Turnaround Times

A widening shortage of subspecialty radiologists lengthens report turnaround and raises the risk of delayed diagnosis for conditions such as stroke and pulmonary embolism. A Neiman Health Policy Institute analysis found that outpatient imaging interpretation turnaround times more than doubled between 2014 and 2023. Aidoc cited the finding in its June 2026 announcement of FDA Breakthrough Device Designation for an AI reporting tool.

Regulatory Pathways

Regulators have accelerated clearance pathways for computer-aided detection and triage software. The FDA’s predetermined change control plan framework lets cleared algorithms be updated without repeat 510(k) submissions. The EU AI Act classifies diagnostic imaging AI as high-risk, requiring conformity assessment and pushing vendors toward rigorous validation and post-market surveillance.

Market Restraints and Challenges

IGTPS analysis highlights the following challenges.

• Fragmented competition. Equipment manufacturers and specialist software vendors both compete, and consolidation is accelerating.
• Integration with PACS. Integration depth is a key success factor, and weak integration limits enterprise adoption.
• Compliance burden. High-risk classification under the EU AI Act and post-market surveillance expectations raise validation costs.
• Multi-vendor governance. Health systems running multi-vendor AI portfolios need onboarding, workflow customization and governance to manage continuous model updates.
• Evidence expectations. Published clinical evidence and clearance breadth influence procurement.

Market Opportunities and Future Outlook

Underserved and Emerging Markets

Handheld and portable ultrasound devices with AI guidance lower the skill barrier for diagnostic-quality images in rural clinics. Chest X-ray AI for tuberculosis and pneumonia triage is being deployed in community screening programs in Asia and Africa. In October 2025, Qure.ai’s qXR became the first AI-enabled tuberculosis screening solution to receive regulatory clearance for children aged zero to three.

Reimbursement Momentum

As more AI imaging tools earn dedicated reimbursement codes and value-based payment recognition, vendors gain a path to per-scan revenue rather than one-time licensing, which supports investment in narrower, high-precision applications.

Platform and Services Growth

Enterprise-wide rollouts need implementation, validation and monitoring support, creating a services layer that is scaling faster than software licensing.

AI in Medical Imaging Market Segmentation Analysis

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By Component

Software led because diagnostic algorithms, triage engines and reporting platforms are licensed per scan or by subscription. Vendors such as Aidoc, Viz.ai and Lunit earn most revenue from software. Services are projected to grow fastest as hospitals need PACS integration, algorithm validation and performance monitoring for multi-vendor AI portfolios.

By Technology

Deep learning led because convolutional neural networks underpin the large majority of FDA-cleared imaging algorithms. Natural language processing is projected to grow fastest as vendors extend AI from pixel-level detection into structured report generation, moving it from a peripheral feature to a core differentiator.

By Modality

CT led because of its central role in emergency, oncology and cardiovascular imaging, and because most FDA-cleared triage algorithms for stroke, pulmonary embolism and intracranial hemorrhage are built for CT. Ultrasound is projected to grow fastest with handheld, chip-based probes paired with AI-guided acquisition and interpretation, supported by clearances such as automated gestational-age estimation.

By Application

Neurology led because of the clinical urgency of ischemic stroke, intracranial hemorrhage and large-vessel occlusion, with one of the longest evidence bases among imaging AI categories. Breast screening is projected to grow fastest on rising mammography volumes, evidence that AI-assisted second reading improves detection while reducing workload, and consolidation among breast imaging AI vendors. Cardiology, pulmonology and orthopedics complete the segment.

By End User

Hospitals led as the earliest and most extensive adopters of stroke, trauma and multi-condition triage. Diagnostic imaging centers are projected to grow fastest as consolidated outpatient networks standardize AI tools across hundreds of sites at once. Ambulatory care centers and research institutes complete the segment.

Regional Analysis

North America leads because of the largest concentration of FDA-cleared algorithms and rapid adoption across academic and community hospitals. The FDA’s predetermined change control plan framework supports continued investment, Canada is adopting AI triage within provincial networks facing radiologist shortages, and outpatient imaging consolidation reinforces leadership.

Asia-Pacific is projected to grow fastest on expanding imaging infrastructure and rising patient volumes in China, India, Japan and South Korea. China’s national AI strategy channels investment into domestic imaging AI developers, India is a hub for chest X-ray and tuberculosis-screening AI vendors, South Korea’s Lunit has broad global deployment, and Japan’s manufacturers embed AI into next-generation scanners.

Europe, Latin America, and the Middle East and Africa are also covered, with Germany, Brazil and Saudi Arabia identified as the largest country markets in their regions.

Competitive Landscape and Key Companies

IGTPS describes the market as fragmented, combining large equipment manufacturers such as GE HealthCare, Siemens Healthineers, Philips and Canon Medical Systems with specialist software vendors including Aidoc, Viz.ai, Lunit and RapidAI. Other profiled players include United Imaging Healthcare, RadNet, Qure.ai, Harrison.ai, HeartFlow, Cleerly, Butterfly Network, Tempus AI, PathAI, Nano-X Imaging, Hologic and Subtle Medical.

Recent developments cited by IGTPS:

• June 2025: PathAI received FDA 510(k) clearance for its AISight Dx whole-slide imaging platform.
• July 2025: RadNet’s DeepHealth completed its acquisition of iCAD, combining breast health portfolios across more than 1,500 provider locations.
• January 2026: Aidoc received FDA clearance for CARE Multi-Triage CT Body, combining eleven newly cleared indications with three existing ones in one submission.
• March 2026: GE HealthCare received FDA 510(k) clearance for Photonova Spectra, a photon-counting CT platform.
• March 2026: Butterfly Network received FDA clearance for a fully automated Gestational Age Tool.

Strategic Implications for Industry Participants

Vendors should prioritize foundation models, international regulatory expansion and acquisitions that broaden clinical coverage, and bundle detection, triage and reporting into single enterprise contracts.

Equipment manufacturers can use embedded AI to differentiate scanners and bundle software with equipment sales.

Hospitals and imaging networks should consolidate AI vendors around platforms with central governance and invest in services for validation and monitoring.

Investors should watch breast screening, ultrasound, diagnostic imaging centers and Asia-Pacific, the fastest-growing areas in IGTPS’s analysis.

Why Choose IG Transformation Partners

IG Transformation Partners is a market research and industry intelligence provider serving life science, ICT, consumer products and other industries. This report offers 165 to 175 pages and 55 to 65 market tables, delivered as a PDF report with a data sheet and analyst support. IGTPS also offers customized research and consulting services. Its published strengths include strong industry focus, comprehensive reports, value chain analysis, potential market opportunities, quality assurance and regular report updates.

Conclusion

The AI in medical imaging market is projected to grow from USD 2.05 billion in 2025 to USD 23.60 billion by 2034 at a 31.2% CAGR. Rising imaging volumes, radiologist shortages and clearer regulatory pathways drive adoption, while foundation models, draft reporting and portable ultrasound AI open new opportunities. Vendors that combine regulatory breadth, PACS integration and published clinical evidence are best placed to win enterprise contracts.

Frequently Asked Questions

What is the AI in medical imaging market?

It covers software, hardware and services that apply machine learning, deep learning and natural language processing to the acquisition, reconstruction, interpretation and reporting of X-ray, CT, MRI, ultrasound and nuclear imaging studies.

How big is the AI in medical imaging market?

IGTPS values the market at USD 2.05 billion in 2025 and USD 2.69 billion in 2026. It is projected to reach USD 23.60 billion by 2034, driven by rising imaging volumes and radiologist shortages.

What is the CAGR of the AI in medical imaging market?

IGTPS projects a CAGR of 31.2% for 2026 to 2034. Growth is supported by expanding regulatory clearances for AI detection, triage and reporting across radiology, cardiology and pathology workflows.

What is the forecast for AI in medical imaging through 2034?

The market is forecast to grow from USD 2.05 billion in 2025 to USD 23.60 billion by 2034. IGTPS expects services, natural language processing, ultrasound, breast screening and diagnostic imaging centers to grow fastest.

What are the key trends in AI in medical imaging?

Key trends include foundation-model platforms that flag many conditions from one study, generative AI for draft reports, AI embedded in scanner hardware and a growing authorization base, with 1,524 AI-enabled devices cleared through Q1 2026.

What is driving AI in medical imaging market growth?

The main drivers are rising imaging volumes and persistent radiologist shortages. Outpatient imaging turnaround times more than doubled between 2014 and 2023 according to a Neiman Health Policy Institute analysis cited by IGTPS.

Which segments lead the AI in medical imaging market?

Software, deep learning, CT, neurology and hospitals led in 2025. Services, natural language processing, ultrasound, breast screening and diagnostic imaging centers are projected to grow at the fastest CAGR in their segments.

Which region dominates the AI in medical imaging market?

North America held the largest share in 2025 at 45%, supported by the largest concentration of FDA-cleared algorithms. Asia-Pacific, with a 24% share, is projected to grow fastest.

Who are the key companies in AI in medical imaging?

IGTPS profiles GE HealthCare, Siemens Healthineers, Philips, Canon Medical Systems, United Imaging Healthcare, Aidoc, RadNet, Viz.ai, Lunit, Qure.ai, Harrison.ai, HeartFlow, Cleerly, RapidAI, Butterfly Network, Tempus AI, PathAI, Nano-X Imaging, Hologic and Subtle Medical.

What challenges does the AI in medical imaging market face?

Challenges include fragmented competition, PACS integration depth, compliance requirements such as high-risk classification under the EU AI Act, governance of multi-vendor AI portfolios and the need for strong published clinical evidence.

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