AI-Powered Whole-Body Imaging Market : Global Industry Analysis and Opportunity Assessment, 2036

How big is AI-Powered Whole-Body Imaging Market in 2026?

USD 1.5 billion in 2026 and USD 7.2 billion by 2036 at a 16.6% CAGR.

Demand for AI-powered whole-body imaging is projected to rise at 16.6% CAGR from 2026 to 2036, taking valuation from USD 1.5 billion to USD 7.2 billion. Radiology departments purchase these systems to shorten scan preparation and reconstruction while preserving diagnostic quality across multiple anatomical regions. NHS England reported in November 2025 that providers completed 49.9 million imaging tests during 2024/25, representing annual growth of 5.9%. Rising workload strengthens the economic case for diagnostic imaging services that standardize acquisition and reporting without expanding specialist staffing at the same pace. Software that reduces repeat scans and reporting delays creates commercial value; dependable service agreements must also protect scanner availability across demanding schedules.

National adoption routes differ according to equipment density and the purchasing role assigned to preventive imaging within each health system. United States providers can combine private-pay screening with large hospital networks, whereas United Kingdom deployment faces stronger evidence and public-capacity scrutiny. Japan's exceptionally dense scanner base supports technical availability, although staffing and interpretation capacity can remain restrictive across routine clinical services. Germany and South Korea offer substantial imaging infrastructure through different financing and service models that shape deployment economics. Providers therefore compare medical imaging software compatibility with local reimbursement and maintenance coverage rather than treating scanner access as sufficient proof of adoption. A credible purchase case connects multi-region imaging performance with a defined care pathway and a budget owner responsible for incidental findings.

Summary of the AI-Powered Whole-Body Imaging Market

Market Signal	Commercial Impact
Demand and Growth Drivers	Imaging organizations invest when AI shortens examination preparation and reconstruction without weakening clinical confidence across complex multi-region protocols. Rising diagnostic workload increases spending on standardized acquisition that can extend scarce technologist capacity across existing imaging systems. Accelerated reconstruction improves scanner utilization because shorter protocols create additional appointment capacity without immediate magnet replacement. Consistent protocol execution reduces repeat examinations and protects radiologist time that would otherwise be spent resolving avoidable quality variation. Service coverage remains commercially important because prolonged downtime can erase throughput gains produced by faster imaging software.
Product and Segment View	The market combines imaging hardware with reconstruction algorithms and workflow software that must function across several anatomical regions during one examination. MRI is projected to represent 33.2% of imaging modality in 2026, attributable to radiation-free soft-tissue coverage and broad protocol flexibility. Preventive health screening is estimated to hold 41.5% of application in 2026, driven by private-pay access and structured multi-organ review. Hospitals are forecast to account for 31.5% of end user spending in 2026, owing to installed equipment and specialist interpretation capacity. Deep learning algorithms are expected to capture 35.7% of technology in 2026, supported by reconstruction speed and image-consistency improvements.
Geography and Growth Outlook	Country growth differs through equipment density and service access together with reimbursement rules that determine who pays for broad imaging examinations. The USA is projected at 19.7% CAGR by 2036, reflecting private-pay screening channels and large integrated imaging networks. South Korea is forecast at 18.8% CAGR during the assessment period, supported by dense tertiary infrastructure and rapid digital workflow evaluation. Japan is anticipated at 16.2% CAGR through 2036 despite broad equipment availability and persistent workforce constraints. Germany and the UK require market entry plans that align training and maintenance with different public purchasing structures.
Competitive Landscape	Competition separates modality manufacturers from preventive-screening operators because each group controls a different part of the patient and imaging workflow. Siemens Healthineers and GE HealthCare combine broad MRI portfolios with integrated reconstruction and workflow technologies across global service networks. Royal Philips and Canon Medical Systems compete through AI reconstruction and upgrade paths that can improve installed-system productivity. United Imaging Healthcare and Fujifilm Healthcare support multi-anatomy imaging through modality platforms and regionally structured commercial service organizations. Prenuvo and Function Health connect whole-body protocols with consumer access and radiologist reporting through managed screening pathways.
Analyst Perspective	Executives should separate technical image acceleration from the clinical governance required to manage incidental findings and downstream referrals. Investment approval should require protocol-level evidence showing scan duration and repeat rates across representative patient groups. Commercial contracts should define responsibility for software updates and cybersecurity controls together with scanner uptime and application support. Preventive-screening operators need referral pathways that convert unexpected findings into clinically appropriate follow-up without overwhelming local specialists. - Anurag Sharma, Principal Analyst at Future Market Insights

Source: FMI's proprietary forecasting model and primary research

How is the AI-powered whole-body imaging market segmented?

The AI-Powered Whole-Body Imaging Market is segmented by Imaging Modality, Application, End User, Technology, and Region.

The segmentation framework separates imaging equipment from clinical use and operating ownership across the defined revenue boundary. Imaging modality distinguishes MRI from CT and other systems that support commercially observable multi-region acquisition workflows. Application separates preventive screening from disease-focused assessment pathways that create different referral and interpretation requirements. End user separates hospitals from imaging centers and specialist clinics that control equipment or service spending. Technology distinguishes deep learning algorithms from computer vision and deployment architectures that support image reconstruction and interpretation. Regional analysis compares adoption conditions across health systems with different equipment density and purchasing models.

What supports demand for MRI within the Imaging Modality category?

Whole-body MRI must capture several anatomical regions during a session that patients and radiology teams can complete reliably. The modality avoids ionizing radiation and supports repeated soft-tissue evaluation, but long protocols can restrict daily appointment capacity. NHS England reported in November 2025 that MRI activity reached 4.9 million tests during 2024/25, increasing 10.1% from the previous year. Demand therefore favors magnetic resonance imaging platforms that combine accelerated acquisition with consistent reconstruction across changing body regions.

• MRI is projected to account for 33.2% of imaging modality in 2026, supported by radiation-free soft-tissue coverage across several body regions. Its position also reflects installed scanner availability and established radiologist familiarity with complex multi-sequence examinations across routine clinical services.
• Hospital radiology departments select MRI protocols that reduce table time without compromising lesion visibility across diverse anatomical areas. Adoption depends on reliable coils and sequence automation together with service support that prevents longer examinations from disrupting daily schedules across busy departments.

How do preventive screening providers evaluate Preventive Health Screening?

Preventive health screening converts broad imaging coverage into a private-pay service that must manage incidental findings and clinical follow-up responsibly. Prenuvo announced in April 2025 that its first Las Vegas clinic offered radiation-free whole-body MRI examinations lasting less than one hour. The operating model connects preventive medicine with standardized protocols and specialist interpretation outside symptom-led referral pathways. Commercial viability depends on transparent limitations and dependable referral routes rather than broad detection claims alone.

• Preventive health screening is estimated to hold 41.5% of application in 2026, attributable to structured multi-organ assessment within one scheduled visit. Private-pay channels also let operators package imaging and reporting with predictable scheduling without relying entirely on conventional referral reimbursement.
• Self-pay screening clinics use whole-body protocols to provide broad anatomical coverage for clients seeking an imaging baseline. Adoption strengthens through clear consent and referral pathways that address incidental findings with documented risk communication and accountable clinical review during subsequent follow-up.

What makes Hospitals central to the End User category?

Hospitals combine scanners and radiologists with referral pathways that can manage unexpected findings from multi-region examinations. Royal Philips reported in February 2025 that SmartSpeed supported diagnostic-quality whole-body examinations lasting less than sixty minutes at a preventive imaging provider. Hospital investment also depends on radiology information systems that route images and findings across clinical departments. Integrated governance gives hospitals an advantage for complex cases that require rapid specialist review and additional testing.

• Hospitals are forecast to represent 31.5% of end user spending in 2026, due to installed imaging capacity and direct access to multidisciplinary specialists. Their capital budgets can support scanner upgrades together with software and enterprise integration across multiple service lines.
• Integrated delivery networks evaluate whole-body imaging against throughput and referral capacity across oncology and cardiovascular services. Adoption requires clear ownership of follow-up testing because faster acquisition can increase downstream work across connected hospital specialist departments if reporting standards remain inconsistent.

How do radiology teams assess Deep Learning Algorithms within Technology?

Deep learning algorithms improve whole-body imaging through reconstruction and protocol automation that reduce noise across accelerated acquisitions. Radiology teams must validate performance across body regions because an algorithm trained for one anatomy may not transfer safely to another. The FDA listed Canon Medical Systems AiCE reconstruction for Vantage Fortian and Orian MRI systems with a final decision dated March 2026. Broader AI-enabled medical devices adoption therefore depends on anatomy-specific evidence and controlled software change management.

• Deep learning algorithms are expected to capture 35.7% of technology in 2026, driven by their role in image reconstruction and protocol consistency. Software upgrades can improve existing scanner productivity across connected service networks without requiring every provider to replace the complete imaging platform.
• Radiology departments adopt deep learning reconstruction where validation confirms stable image quality across accelerated sequences and patient types. Purchasing teams also require cybersecurity and version controls that preserve performance evidence during software and infrastructure changes across connected clinical networks at multiple sites.

What are the drivers, restraints, and opportunities in the AI-powered whole-body imaging market?

Diagnostic workload supports demand for faster multi-region imaging; uncertain screening benefit constrains routine adoption; governed remote workflows create a credible service opportunity.

• Driver: Diagnostic backlogs increase the value of faster acquisition and reconstruction across imaging departments with limited specialist capacity.
• Restraint: Incidental findings and uncertain population benefit restrict routine screening without clear consent and follow-up governance.
• Opportunity: Remote protocol support can extend specialist expertise across imaging sites while preserving documented safety responsibilities.

Radiology backlogs create direct purchasing pressure for systems that increase appointment capacity without reducing diagnostic confidence. NHS England reported in November 2025 that the median MRI request-to-test interval reached twenty-one days during 2024/25. The workload supports investment in autonomous imaging functions that standardize preparation and reconstruction for technologists working across varied protocols. Hospital finance teams still need measured throughput gains because faster software creates limited value if reporting or follow-up remains congested.

Uncertain clinical benefit limits preventive deployment among asymptomatic people even where whole-body MRI avoids ionizing radiation. The Royal College of Radiologists stated in June 2025 that elective MRI screening can produce overdiagnosis and unnecessary treatment while adding pressure to NHS services. Screening operators must therefore explain examination limitations and maintain clinically governed referral pathways for incidental findings. Wider adoption requires evidence that defined populations gain useful outcomes without transferring unmanaged work into public diagnostic systems.

Remote scanning creates a practical opening for imaging networks that have scanners available but uneven technologist expertise across locations. The American College of Radiology updated its position in March 2025 and supported remote CT and MRI scanning with explicit supervision safeguards. Networks can combine remote acquisition support with structured reporting automation to improve consistency across distributed sites. Service providers must define local emergency coverage and credentialing responsibilities before centralized support can expand safely.

Which country CAGRs are profiled in the AI-powered whole-body imaging market?

Country	CAGR
USA	19.7%
UK	13.8%
Germany	14.8%
Japan	16.2%
South Korea	18.8%

How do country-level CAGRs compare in the AI-powered whole-body imaging market?

The country comparison spans 5.9 percentage points and separates two upper positions from a more moderate European pair. USA and South Korea are divided by 0.9 percentage points despite different reimbursement and provider structures. Japan sits 2.6 percentage points below the USA while retaining a broad imaging equipment base. Germany and the UK form a closer European pair with a measured 1.0-percentage-point separation between their forecasts. Similar forecast rates can therefore reflect different combinations of scanner availability and service readiness rather than comparable market size.

• The USA position reflects private-pay screening channels and integrated hospital networks that can fund software upgrades through several budget routes.
• South Korea remains close to the USA through dense tertiary infrastructure and rapid evaluation of digitally integrated clinical technologies.
• Japan occupies the middle of the comparison because extensive equipment availability coexists with workforce and interpretation constraints.
• Germany records a moderate position through strong hospital infrastructure and formal purchasing processes that can extend implementation schedules.
• The UK sits outside the central cluster as public capacity pressures increase scrutiny of evidence and downstream service consequences.

Comparable CAGRs create different entry conditions because regulatory evidence and maintenance coverage influence usable capacity more directly than equipment counts alone. Commercial plans must therefore align financing and training with each country’s referral pathway rather than applying one deployment model across all five locations. The full report provides country-level CAGR analysis across North America, Latin America, Europe, East Asia, South Asia, Oceania and the Middle East and Africa.

Country-wise Analysis

• United States imaging networks combine large hospital systems with private diagnostic centers and direct-pay preventive services. Demand for the AI-powered whole-body imaging market in the USA is forecast to rise at 19.7% CAGR over the forecast period, supported by several purchasing routes. OECD data published in November 2025 placed combined CT and MRI equipment at eighty-six units per million residents while hospital beds stood at 2.8 per thousand. The installed base enables computed tomography systems and MRI software upgrades across diverse provider settings. Reimbursement uncertainty and state-level privacy requirements remain material frictions for preventive services that generate incidental findings. Commercial contracts should define referral ownership and maintenance response standards before deployment expands across additional imaging locations.
• United Kingdom imaging purchasing is concentrated in public pathways that face long queues and formal data-standard requirements. NHS England published Diagnostic Imaging Data Set version 2.0 in October 2025 for implementation across England’s radiology departments and independent NHS providers. The UK AI-powered whole-body imaging market is estimated to expand at 13.8% CAGR by 2036, shaped by capacity pressure and evidence scrutiny. Standardized imaging records can support workflow integration and stronger auditability across public and independent diagnostic service settings. Public procurement remains a material friction because preventive examinations can add downstream referrals without a funded clinical pathway. Market entry requires clear information-system integration and a service model that does not displace urgent diagnostic capacity.
• German hospitals operate within a dense provider network that supports complex imaging through formal regional purchasing and reimbursement processes. Existing scanner fleets create an upgrade route, although technical approval and funding decisions remain institution-specific across federal states. Germany’s AI-powered whole-body imaging market is projected to record 14.8% CAGR during the assessment period, underpinned by established imaging infrastructure. OECD figures released in November 2025 recorded seventy-four combined CT and MRI units per million residents and 7.7 hospital beds per thousand. Equipment density enables software upgrades, yet fragmented decision authority can separate technical validation from funding approval across hospital groups. Manufacturers need regional service coverage and documented interoperability evidence to shorten procurement review across German hospital networks.
• Japanese providers have exceptional scanner availability across hospitals and diagnostic facilities with strict expectations for protocol consistency. OECD data published in November 2025 reported 184 combined CT and MRI units per million residents together with 12.5 hospital beds per thousand. Adoption of AI-powered whole-body imaging in Japan is anticipated to advance at 16.2% CAGR through 2036, reflecting technical readiness. The equipment base enables broad access to multi-region imaging and creates upgrade opportunities across existing systems. Radiologist and technologist capacity remains a practical friction because higher scanner density does not guarantee timely interpretation. Japanese providers should prioritize automation that reduces setup variation and supports local-language workflow integration across established imaging systems.
• South Korean tertiary hospitals combine advanced imaging departments with digital infrastructure that supports rapid software evaluation. South Korea’s AI-powered whole-body imaging market is predicted to post 18.8% CAGR over the assessment period, reinforced by concentrated clinical technology capacity. OECD country data issued in November 2025 placed combined CT and MRI equipment at eighty-seven units per million residents and hospital beds at 12.6 per thousand. Dense tertiary capacity creates an enabler for AI reconstruction and enterprise deployment across major urban systems. A material friction remains the concentration of specialist resources because smaller providers may lack training and local application support. Commercial expansion therefore requires distributor-led technical service and validated Korean-language interfaces across smaller provider organizations.

Who are the notable companies in the AI-powered whole-body imaging market?

Siemens Healthineers, GE HealthCare, Royal Philips, Canon Medical Systems, Fujifilm Healthcare, United Imaging Healthcare, Prenuvo, and Function Health are notable companies in this market.

Competition combines global modality manufacturers with preventive-screening operators that control different stages of acquisition and patient follow-up. United Imaging Healthcare reported in April 2025 that its commercial network covered more than eighty-five countries with seven regional spare-parts hubs and thirty-two national warehouses. Comparable algorithm validation platforms matter because reconstruction claims need evidence across scanners and anatomical regions. Manufacturers compete through installed-base upgrades and service depth while screening operators compete through protocol design and access. Provider selection depends on current regulatory status and accountable workflow ownership rather than corporate scale or parent-market reputation.

• Siemens Healthineers and GE HealthCare provide broad MRI portfolios with AI reconstruction and workflow tools across international service organizations. Royal Philips adds portfolio-wide acceleration software that supports installed systems and multi-anatomy protocols across several clinical applications.
• Canon Medical Systems and Fujifilm Healthcare provide MRI platforms with deep learning reconstruction and workflow automation. United Imaging Healthcare adds dedicated whole-body systems and a global imaging portfolio supported by regional service infrastructure.
• Prenuvo operates managed whole-body MRI screening clinics that combine standardized protocols with specialist reporting and defined patient access pathways. Function Health owns Ezra and combines fast MRI access with AI-supported image quality and reporting across partner locations.

Competitive Benchmarking: AI-Powered Whole-Body Imaging Market

Company	Whole-Body Imaging	AI Reconstruction and Analysis	Workflow Integration	Geographic Reach
Siemens Healthineers	High	High	High	Global
GE HealthCare	High	High	High	Global
Royal Philips	High	High	High	Global
Canon Medical Systems	Medium	High	Medium	Global
Fujifilm Healthcare	Medium	Medium	Medium	North America, Europe, and Asia
United Imaging Healthcare	High	High	High	Global
Prenuvo	High	Medium	High	North America, Europe, and Australia
Function Health (Ezra)	High	Medium	Medium	United States

Scoring basis: Whole-Body Imaging receives High for a dedicated service or documented full-body system; Medium reflects broad multi-anatomy coverage and Low reflects a narrower documented role. AI Reconstruction and Analysis receives High for several integrated AI functions; Medium reflects a direct narrower function and Low reflects documented limited analytical support. Workflow Integration receives High for end-to-end acquisition and review; Medium reflects partial integrated support and Low reflects a restricted scan or reporting pathway.

Key Developments in the AI-Powered Whole-Body Imaging Market

• In June 2025, Siemens Healthineers received FDA clearance for the MAGNETOM Flow.Ace 1.5T MRI platform. The system uses AI reconstruction and automated workflow across the complete MR application range while requiring 0.7 liters of helium. The clearance expands a commercially available pathway for facilities seeking lower installation requirements and broad anatomical coverage.
• In February 2026, GE HealthCare announced FDA clearances for SIGNA Sprint and SIGNA Bolt together with the SIGNA One workflow ecosystem. The integrated platform supports examinations from protocol planning through image acquisition and downstream review across connected radiology workflows. The development gives imaging departments an upgrade route that combines new magnet designs with AI-driven workflow controls.
• In June 2025, United Imaging Healthcare introduced the CE-marked uMR Ultra as a 3T system for real-time whole-body diagnostics. Its uAIFI.LIVE platform combines AI algorithms with motion-optimized acquisition across structural and functional imaging. The launch broadens competition among hospitals evaluating advanced whole-body MRI systems with dynamic visualization and clinically responsive motion-sensitive acquisition capabilities.
• In November 2025, Prenuvo opened its first European clinic in London and extended its preventive whole-body MRI service beyond North America and Australia. Prenuvo reported more than 150,000 completed scans across twenty-seven clinics at the announcement date. The opening establishes a local screening and specialist reporting route for clients across the United Kingdom and continental Europe.

Key Players in the AI-Powered Whole-Body Imaging Market

Global Imaging System Providers

• Siemens Healthineers
• GE HealthCare
• Royal Philips
• Canon Medical Systems

Multi-Anatomy Imaging Providers

• Fujifilm Healthcare
• United Imaging Healthcare

Preventive Imaging Service Providers

• Prenuvo
• Function Health (Ezra)

AI-Powered Whole-Body Imaging Market - Report Scope

Report scope	Coverage field
Market breakdown	Imaging modality, application, end user, technology, and region.
Quantitative Units	USD Billion
Market Definition	AI-enabled imaging systems, reconstruction software, analysis tools, and managed services used directly for multi-region or whole-body diagnostic imaging.
Regions Covered	North America, Latin America, Western Europe, Eastern Europe, East Asia, South Asia and Pacific, and Middle East and Africa.
Countries Covered	USA, UK, Germany, Japan, and South Korea.
Key Companies Profiled	Siemens Healthineers, GE HealthCare, Royal Philips, Canon Medical Systems, Fujifilm Healthcare, United Imaging Healthcare, Prenuvo, and Function Health.
Forecast Period	2026 to 2036.
Approach	Hybrid bottom-up and top-down market sizing supported by primary interviews and official desk research.

AI-Powered Whole-Body Imaging Market - Research Methodology

Method	Approach
Primary Research	FMI analysts gathered input from manufacturers, service providers, technology developers, distributors, end users, procurement teams, and subject-matter experts. Interviews examined purchasing decisions, product or service evaluation, adoption barriers, approval requirements, pricing considerations, and expectations for technical or commercial support. Respondents were also asked what evidence is required before a trial, pilot, or initial order develops into regular purchasing.
Desk Research	Desk research covered government statistics, regulatory publications, trade data, industry associations, technical literature, standards, company filings, product information, and official corporate announcements. Sources were reviewed for relevance, publication date, geographic coverage, and consistency with the defined market scope. Claims relating to performance, applications, approvals, capacity, investment, and commercial activity were retained only when supported by credible public evidence.
Market Sizing and Forecasting	The market model combined the baseline value with historical performance, segment structure, pricing and volume indicators, adoption levels, company participation, and country-level demand conditions. Forecast assumptions considered economic activity, investment trends, regulatory developments, technology adoption, purchasing cycles, supply availability, and barriers to wider market use. Segment and regional estimates were reconciled before the final market total was calculated.
Data Validation	Estimates were checked against multiple independent indicators, including public data, company activity, trade patterns, industry developments, and findings from primary interviews. Validation also tested whether products, services, applications, and company revenues fell within the defined market boundaries. Adjacent categories, unsupported claims, overlapping revenues, and activities without direct market relevance were excluded to reduce double counting and maintain consistency across segments and countries.

AI-Powered Whole-Body Imaging Market by Segments

AI-Powered Whole-Body Imaging Market segmented by Imaging Modality:

• MRI
• Computed Tomography (CT)
• X-ray
• PET-CT
• Hybrid Imaging Systems

AI-Powered Whole-Body Imaging Market segmented by Application:

• Preventive Health Screening
• Oncology Diagnostics
• Cardiovascular Assessment
• Neurological Assessment
• Precision Medicine

AI-Powered Whole-Body Imaging Market segmented by End User:

• Hospitals
• Diagnostic Imaging Centers
• Preventive Health Clinics
• Research Institutes

AI-Powered Whole-Body Imaging Market segmented by Technology:

• Deep Learning Algorithms
• Computer Vision Systems
• Cloud-based AI Platforms
• Edge AI Imaging Solutions

AI-Powered Whole-Body Imaging Market by Region:

• North America
  • United States
  • Canada
• Latin America
  • Brazil
  • Mexico
  • Argentina
  • Chile
• Western Europe
  • Germany
  • France
  • United Kingdom
  • Italy
  • Spain
  • Benelux
  • Nordics
• Eastern Europe
  • Poland
  • Czech Republic
  • Romania
  • Hungary
• East Asia
  • China
  • Japan
  • South Korea
• South Asia and Pacific
  • India
  • ASEAN
  • Australia and New Zealand
• Middle East and Africa
  • GCC Countries
  • South Africa
  • Türkiye
  • Israel

Research Sources and Bibliography

• NHS England. (2025, November 27). Diagnostic Imaging Dataset Annual Statistical Release 2024/25.
• NHS England. (2025, October 23). Diagnostic Imaging Data Set.
• Organisation for Economic Co-operation and Development. (2025, November 13). Health at a Glance 2025: United States.
• Organisation for Economic Co-operation and Development. (2025, November 13). Health at a Glance 2025: Germany.
• Organisation for Economic Co-operation and Development. (2025, November 13). Health at a Glance 2025: Japan.
• Organisation for Economic Co-operation and Development. (2025, November 13). Health at a Glance 2025: Korea.
• Royal College of Radiologists. (2025, June 12). RCR statement on elective MRI health screening.
• American College of Radiology. (2025, March 20). ACR Position Statement on Remote Scanning.
• Prenuvo. (2025, April 7). Prenuvo Launches First Whole-Body MRI Clinic in Las Vegas, Ushering in a New Era of Preventative Health.
• Royal Philips. (2025, February 26). Philips accelerates precise imaging with unique AI technologies in MRI to improve patient outcomes.
• USA Food and Drug Administration. (2026). Artificial Intelligence-Enabled Medical Devices.
• United Imaging Healthcare. (2025, April 28). United Imaging Healthcare Releases 2024 Annual and Q1 2025 Results, Highlights Strong Global Expansion and AI Innovation.
• Siemens Healthineers. (2025, June 26). Siemens Healthineers Receives FDA Clearance for Magnetom Flow.Ace, Company’s First Helium-Free 1.5 Tesla Magnetic Resonance Scanner.
• GE HealthCare. (2026, February 19). GE HealthCare achieves MRI portfolio milestone with FDA clearances for next-generation SIGNA MRI technology designed to enhance precision imaging and clinical efficiency.
• United Imaging Healthcare. (2025, June 6). United Imaging Introduces Two Groundbreaking CE-Marked Systems: uMI Panvivo and uMR Ultra.
• Prenuvo. (2025, November 24). Prenuvo opens first European clinic, bringing advanced whole body MRI screening to London.
• Ezra. (2025, May 5). Function Health Acquires Ezra, Introduces $499 MRI Scan.

This bibliography is provided for reader reference and is not exhaustive. The full report contains the complete reference list and detailed citations.

This Report Answers

• How large is the AI-powered whole-body imaging market in 2026 and 2036?
• Which operating pressures support investment in AI-assisted multi-region imaging workflows?
• Why does MRI account for the largest imaging modality share in 2026?
• How does preventive health screening influence service design and clinical follow-up?
• Why do hospitals represent a major end user for AI-powered whole-body imaging?
• How do country growth conditions differ across the USA, UK, Germany, Japan, and South Korea?
• Which companies provide imaging systems, reconstruction software, or managed whole-body screening services?
• What limits routine preventive adoption among people without symptoms?