Global AI in Medical Imaging Market 2026 – 2035

The global AI in medical imaging market size was estimated at USD 3.5 billion in 2026 and will grow with a CAGR of 20.9% from 2026 to 2035, reaching USD 19.4 billion by 2035. The North America region is the largest market at around 42% of the market share in 2025, driven by its advanced healthcare IT infrastructure, high density of radiology AI developers, large number of FDA-cleared imaging algorithms, and continuous investments in hospitals in the region for diagnostic automation, which is caused by a chronic lack of practicing radiologists in the region.

Market Highlight

• Software solutions accounted for roughly 68% of market revenue in 2025, and these algorithms for image analysis, triage and reporting will be licensed to and continually updated throughout the hospital and imaging center networks, unlike any one-time hardware purchase.

• About 55% of the technology segment is used for deep learning, which is the most common method for developing medical imaging AI; convolutional and transformer-based architectures are currently achieving the best accuracy for detection, segmentation and classification across radiology, pathology and cardiology images.

• CT leads the modality segment with approximately 34%, owing to the high number of scans conducted around the world for chest, abdomen and trauma-related disease indications and the ease with which AI can be applied to standardized cross-sectional image data.

• Hospitals represented about 58% of the end-use segment, with AI imaging tools integrated directly into PACS and radiology workflows, enabling them to enhance triage, quantification and structured reporting in high-volume diagnostic departments.

• North America was the leading region in 2025, with the U.S. market having the maximum number of FDA-approved AI imaging devices, whereas Asia Pacific was projected to witness the fastest growth, reflecting increased hospital digitization and growth in local AI imaging development in the region, particularly in China, Japan, South Korea and India.

Significant Growth Factors

Radiologist Workforce Shortage and Rising Imaging Volumes Driving Workflow Automation

The consistent worldwide shortages of trained radiologists, coupled with the increasing number of CT, MRI, X-ray and ultrasound procedures as the population is aging and cancer, cardiovascular disease and neurological conditions are on the rise, is the most structurally secure and commercially attractive demand drivers for AI-driven imaging software, which is expected to be a significant proportion of the total market revenue. In the emergency room and inpatient setting, AI tools that scan for critical findings, like a suspected fracture or a pulmonary embolus or intracranial hemorrhage, before routine readings can flag these types of cases for a radiologist, thereby shrinking the time between when the image is acquired and when a clinical action is taken. Meanwhile, quantitative AI measurement tools that automatically segment tumors, calculate EF from cardiac MRI, and score coronary calcium from CT scans are offsetting the growing burden of manual measurements among radiologists and cardiologists, helping to keep pace with increases in imaging volume even as staffing doesn't grow at the same rate.

Regulatory Clearance Momentum and Expanding Clinical Applications

This steady proliferation of regulatory approvals for AI-powered imaging software—evident in the U.S. FDA's ever-expanding list of medical devices cleared with AI and machine learning, the vast majority of which are radiology related—is establishing a whole new and rapidly expanding addressable market for vendors, with a growth curve that far outpaces the earlier generation of narrow, single-finding detection tools. AI is also clearing the path for various applications, including stroke and large-vessel-occlusion detection, breast-cancer risk scoring from mammography, and automated recommendations for follow-up procedures of pulmonary nodules, among others, that health systems can choose without having to invest the time and resources in building their own algorithm.

Reimbursement pathways have started to materialize for some AI imaging services in the United States, while the European Union's Medical Device Regulation (MDR) has set standards for the commercialization of AI-powered imaging software as a medical device, bringing much-needed commercial and regulatory clarity for hospital systems, group practices, and imaging networks to make multi-year budgets for enterprise-wide AI imaging deployments across their imaging departments.

Drivers Impact Analysis

Restraints Impact Analysis

What are the Major Advances Changing the AI in Medical Imaging Market Today

Foundation Models and Multimodal Imaging AI

The imaging AI industry has witnessed a paradigm shift from narrow, single-finding detection models into large foundation models trained on millions of de-identified images that can be applied to multiple imaging tasks, modalities and anatomical regions with a common underlying architecture, new categories of products, new commercialization models and new applications beyond the traditional single-task detection tool. These multimodal models, which fuse image data with text from radiology reports, structured electronic-health-record fields, and, in some instances, pathology or genomic data, are creating more context for diagnostics and making it possible to create automated draft radiology reports and to compare the patient's current studies with previous ones over time. As the efficiency of the models improves, with a smaller distilled architecture, which can be run on standard hospital hardware, it is becoming more viable to deploy more advanced imaging AI in community hospitals and outpatient imaging facilities that did not have access to the computing hardware needed to support deep-learning inference at scale.

Point-of-Care and Portable Imaging AI Expansion

The new and rapidly expanding sector of imaging AI is the embedding of AI functionality directly into portable and handheld ultrasound devices, echocardiography carts and mobile X-ray units to assist less-experienced operators with image acquisition and help them with real-time interpretation at the point of care. AI-backed point-of-care ultrasound is growing in use in the emergency department, primary care, obstetrics, and rural and resource-limited areas and is now enabling non-specialist clinicians to acquire diagnostic quality images and get automated guidance on a range of findings including reduced cardiac ejection fraction, free abdominal fluid, and fetal biometry. With the simultaneous development of cloud-based image sharing platforms, these point of care studies are now being transmitted to be reviewed remotely by specialist experts, bringing a level of subspecialist imaging and cardiology expertise to settings where it would normally not have been available during the interpretation of such studies.

Agentic Workflow Orchestration Across the Imaging Department

In the imaging workflow, the next generation of automation systems is replacing bio-based and rules-based systems with agentic AI systems that coordinate multiple steps to the workflow, including prioritizing the worklist in the scanner, selecting the appropriate protocol, automating measurements, creating structured reports, and identifying incidental findings and scheduling follow-up appointments. Health systems that are testing these orchestration layers are seeing real impact on the turnaround time for radiologists to read URGENT studies with the software constantly reprioritizing the reading list as new URGENT studies arrive. These orchestration features are now being packaged together by vendors as a platform and shifting the purchasing decision from an algorithmic licensure to a commitment for an enterprise imaging-AI platform over an extended period of time.

Category Wise Insights

By Component

Why Does Software Lead the Market?

In 2025, software occupied the largest market share in the AI in medical imaging market, accounting for approximately 68% of the market, driven by its recurring-license business model and being the primary detection, segmentation and reporting engine offered to hospitals, imaging centers and teleradiology networks. AI imaging software is generally licensed on a per-study or per-scanner or enterprise level, enabling vendors to update their models and expand their clinical indication portfolios with no need to buy new scanners; this enables rapid recurring-revenue growth as it is adopted in larger numbers by radiology departments.

By Technology

Why Does Deep Learning Lead the Market?

About 55% of the AI used in medical imaging technology in 2025 consisted of deep learning. The deep learning approach has become the current primary method for developing imaging AI, as CNN and, more recently, transformer-based architectures are materially superior across most imaging classification, segmentation and detection tasks compared to previous rule-based or classical machine learning approaches. The deep learning approach has taken advantage of the large and expanding number of publicly available and proprietary datasets annotated with images, of well-established open-source models, and special graphics-processing-unit (GPU) hardware that makes training and deployment of deep learning models more accessible to large imaging equipment and component manufacturers and smaller specialized imaging-AI vendors.

By Modality

Why Does CT Lead the Market?

In 2025, CT would account for approximately 34% of all modality scans, bolstered by a growing number of CT-based chest and abdominal scans and CT-based oncologic and trauma scanning in emergency departments, inpatient wards and outpatient imaging centers around the world. Compared to more operator-dependent modalities like ultrasound, CT is well-suited for deep learning analysis because of its cross-sectional nature and its clinical value proposition for time-critical applications like stroke, pulmonary embolus and trauma, which are treated on a regular basis.

By Application

Why Does Oncology Lead the Market?

Oncology was the largest single clinical application category, making up about 26% of applications in 2025; AI tools used in oncology are being applied to all modalities used in cancer studies, including mammography, CT, MRI and PET, in all stages of the cancer-care pathway, from screening to post-treatment surveillance.

By End Use

Why Do Hospitals Lead the Market?

With the high volumes of imaging they perform, prior PACS and EHR systems in place, and larger capital budgets, hospitals will be the first to adopt enterprise AI-imaging platforms, which will bring AI triage, quantification and reporting tools into the workflow of the radiology and cardiology departments.

Report Scope

Regional Analysis

How Big is the North America Market Size?

The AI in medical imaging market in North America is projected to reach approximately USD 6.9 billion by 2035 at a CAGR of approximately 18.9% between 2026 and 2035.

Why Did North America Dominate the Market in 2025?

The U.S. FDA's continually expanding list of cleared AI-enabled imaging devices, the advanced hospital IT infrastructure and a high concentration of specialized AI-imaging vendors and imaging-equipment OEMs based or operating major research and development centers in the U.S. and Canada are expected to drive North American dominance in 2025.

How Big is the Asia Pacific Market Size and Why is it Growing Fastest?

The fastest-growing market for AI enabled medical imaging is Asia Pacific, with the region's countries digitizing hospitals, developing domestic AI imaging platforms and having large and growing patient populations as compared to a pool of trained radiologists, and it is worth USD 0.78 billion in 2025 and USD 6.2 billion by 2035 at a CAGR of approximately 23.0% for the five-year forecast period 2026-2035.

Why is Europe a Strategically Important Market?

The European market will see a compound annual growth rate of approximately 17.9% rise from USD 0.64 billion in 2025 to USD 3.3 billion by 2035. The importance of Europe is further highlighted by the presence of key imaging-equipment manufacturers headquartered in the Netherlands and Germany, national health systems in the United Kingdom and France that are actively piloting the use of AI-triage tools in public imaging service, and the European Union's Medical Device Regulation, which is setting a clearer, albeit challenging, roadmap for the use of AI-based software as a medical device across all member states.

Why are Latin America and the Middle East & Africa Emerging Markets?

The Latin American AI in medical imaging market is projected to grow from USD 0.15 billion in 2025 to USD 0.90 billion by 2035. The growing private hospital networks in Brazil are leading the market, driving the modernization of their diagnostic-imaging systems. The Middle East & Africa market is projected to grow from USD 0.12 billion in 2025 to USD 0.66 billion in 2035. Major factors contributing to the growth of the market in this region include the ambitious healthcare infrastructure development initiatives taken by Saudi Arabia and investments in digital health initiatives in the United Arab Emirates.

Key Market Players

• GE HealthCare Technologies Inc.

• Siemens Healthineers AG

• Koninklijke Philips N.V.

• Canon Medical Systems Corporation

• Aidoc Medical Ltd.

• Viz.ai Inc.

• HeartFlow Inc.

• Butterfly Network Inc.

• Qure.ai Technologies Pvt. Ltd.

• RadNet Inc.

• Others

Key Developments

The market has been greatly developed as vendors extend their clinical applications, and cloud-based deployment in radiology, cardiology and point-of-care applications has become increasingly dominant, while strengthening imaging-OEM partnerships.

• January 2025 — GE HealthCare signed a multi-year strategic agreement with Sutter Health to provide advanced imaging systems, such as automated MRI reconstruction, PET/CT and point-of-care ultrasound systems in more than 300 facilities to enhance diagnostic and imaging precision, streamline workflow and boost patient access.

• May 2025 — Philips was working with NVIDIA to co-create an artificial-intelligence-based MRI reconstruction model that would deliver superior diagnostic images in less than half the time as the industry moves toward integrating generative and foundation-model AI into imaging-equipment platforms.

The AI in Medical Imaging Market is segmented as follows:

By Component

• Software

• Hardware

• Services

By Technology

• Deep Learning

o   Convolutional Neural Networks

o   Transformer-Based Vision Models

• Machine Learning (Non-Deep-Learning)

• Natural Language Processing

o   Radiology Report Structuring

• Others (Computer Vision, Predictive Analytics)

By Modality

• CT

• MRI

• X-ray

• Ultrasound

o   Point-of-Care Ultrasound

o   Echocardiography

• Mammography

• Others (PET, Nuclear Imaging, Endoscopy)

By Application

• Oncology

o   Tumor Detection & Characterization

o   Treatment Response Monitoring

• Neurology

o   Stroke & Large-Vessel-Occlusion Detection

o   Neurodegenerative Disease Assessment

• Cardiology

o   Cardiac CT & MRI Quantification

o   Coronary Calcium & Plaque Scoring

• Pulmonology

o   Lung Nodule Detection & Follow-Up

• Orthopedics

• Others (Abdominal, Musculoskeletal, Pediatric)

By End Use

• Hospitals

o   Academic & Tertiary Care Hospitals

o   Community Hospitals

• Diagnostic Imaging Centers

• Ambulatory Surgical Centers

• Others (Research & Academic Institutions)

Regional Coverage:

North America

• U.S.

• Canada

• Mexico

• Rest of North America 

Europe

• Germany

• France

• U.K.

• Russia

• Italy

• Spain

• Netherlands

• Rest of Europe

Asia Pacific

• China

• Japan

• India

• New Zealand

• Australia

• South Korea

• Taiwan

• Rest of Asia Pacific 

The Middle East & Africa

• Saudi Arabia

• UAE

• Egypt

• Kuwait

• South Africa

• Rest of the Middle East & Africa

Latin America

• Brazil

• Argentina

• Rest of Latin America