Artificial Intelligence in Medical Imaging Market Size,
Share, Growth Report - 2035

The Artificial Intelligence in Medical Imaging Market
reached USD 2.1 billion in 2025 and is projected to grow at 23.9% annually from
2026 to 2035. The market encompasses software and platforms that analyze
medical images, prioritize worklists, support diagnosis, improve image
acquisition or reconstruction, automate reporting, and assist treatment
planning. Deployments span cloud-based and on-premises environments across
X-ray, computed tomography, magnetic resonance imaging, ultrasound, molecular
imaging, nuclear imaging, and other modalities.

Technological progress in artificial intelligence,
radiologist shortages, improved diagnostic accuracy and treatment planning, and
rising research and development investment are expanding the market. Deep
learning, transformer architectures, and computer vision are broadening use in
image analysis, reconstruction, workflow automation, and clinical decision
support. These capabilities increase the range of deployable products available
to hospitals and outpatient imaging centers. Radiologist capacity pressures
strengthen the business case for AI as a workflow-support tool. Triage software
can surface suspected urgent cases before routine reading begins, while
automated reporting can reduce repetitive documentation. Advances in diagnostic
performance make AI increasingly relevant to clinical decisions and treatment
planning, especially where early detection or consistent measurements affect
the next step in care. Investment in reconstruction, cardiac imaging, and
multimodal clinical-data integration broadens the available technology base,
with activity centered on oncology and cardiovascular applications where
clinical pathways and reimbursement potential are more mature.

By deployment, cloud-based solutions held 57.6% of market
revenue in 2025. Cloud delivery supports rapid software updates, scalable
computing for large imaging datasets, and lower upfront capital requirements
for hospitals and diagnostic networks. It also enables vendors to distribute
multi-algorithm workflows across hospital networks, standardize software
releases, and centralize performance monitoring. These attributes suit health
systems seeking consistent governance and data controls across more than one
site. The operating model supports workflow integration across image
interpretation, prioritization, reporting, and care coordination. Cloud
adoption nevertheless depends on the fit between local data controls,
cybersecurity expectations, and organizational requirements. Hybrid approaches
can keep latency-sensitive processing local while moving updates, training, and
noncritical analytics to cloud environments.

By modality, X-ray held the largest share at 27.1% in 2025.
Its broad installed base, high examination volumes, and extensive datasets
create a foundation for AI applications in chest imaging, fracture detection,
dental imaging, and tuberculosis screening. The modality supports varied
commercial activity across image-based AI tools and provides an established
environment for deployment at scale. X-ray software can address diagnostic
workflows that require efficient review of high study volumes while retaining
clinical oversight. Demand is also supported by the relevance of AI to
screening and routine imaging settings. The segment's scale makes it a core setting
for platforms that connect image acquisition with actionable clinical workflow
outputs, while providing a route for vendors to establish broader enterprise
relationships.

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The North America artificial intelligence in medical imaging
market held 43.8% market share in 2025. The United States anchors demand
through regulatory clearance activity, hospital IT spending, mature imaging
infrastructure, and reimbursement pathways for selected applications.
Validation frameworks support more consistent assessment by health systems,
while Canadian digital-health adoption and an integrated health system
contribute to regional demand. Procurement remains exacting, as hospital buyers
seek evidence of workflow fit, cybersecurity controls, local validation, and
reimbursement relevance before extending deployments beyond pilots. These
conditions favor suppliers that can integrate their tools within established
imaging and clinical systems while addressing organizational governance
requirements.

GE HealthCare held over 23% market share in 2025. Its
position is supported by an imaging hardware installed base, the Edison
platform, and an AI portfolio covering CT, MRI, X-ray, ultrasound, and nuclear
medicine. The company targets hospital and integrated delivery-network accounts
through platform relationships and longer-term service arrangements. This reach
allows GE HealthCare to align AI software with established imaging workflows
across multiple modalities. Its portfolio approach addresses image analysis,
clinical workflow support, and enterprise deployment needs, which are
increasingly important as providers assess how to manage algorithms within
their existing technology environments.

Some of the major players in this market are Siemens
Healthineers, Philips Healthcare, Aidoc, Viz.ai, RapidAI, Canon Medical
Systems, Fujifilm Holdings, Tempus Radiology, Lunit, Qure.ai, Annalise.ai,
Subtle Medical, Rad AI, and Cleerly. Siemens Healthineers embeds AI across
diagnostic and therapeutic workflows, while Philips Healthcare emphasizes
enterprise imaging informatics and AI-guided imaging tools. AI-native platforms
including Aidoc and Viz.ai focus on detection, prioritization, communication, and
care coordination. Other competitors extend the market into acute-care imaging,
reconstruction, quantitative analysis, reporting automation, precision
oncology, and resource-limited settings. Their ability to integrate with
established imaging systems and support clinical workflow requirements shapes
competitive positioning.

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