Ukrainian Medical Imaging Infrastructure: Current State and AI Readiness Assessment

1. Introduction

The capacity of a nation’s medical imaging infrastructure fundamentally determines its diagnostic capabilities and, ultimately, patient outcomes. Computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, X-ray, and positron emission tomography (PET) scanners form the backbone of modern diagnostic medicine, enabling early detection of cancers, cardiovascular diseases, neurological conditions, and traumatic injuries. As artificial intelligence increasingly transforms radiology practice worldwide, the readiness of this infrastructure—not merely in equipment quantity but in digital connectivity, data standards compliance, and workflow integration—becomes decisive for healthcare systems seeking to leverage algorithmic assistance.

For Ukraine, assessing medical imaging infrastructure carries urgency beyond academic interest. The nation simultaneously confronts multiple interconnected challenges: the legacy of Soviet-era healthcare organization, ambitious modernization efforts through the National Health Service of Ukraine (NHSU) established in 2017, and the catastrophic destruction wrought by Russia’s full-scale invasion since February 24, 2022. Each factor reshapes the landscape of imaging capabilities and AI integration potential.

Before 2022, Ukraine had made significant progress in healthcare digitalization. The electronic healthcare system (eHealth), launched as a Ministry of Health priority in 2017, grew to encompass 36 million registered patients, 1.6 billion electronic medical records, and 400,000 active healthcare users processing 1,000-1,500 database requests per second. This digital foundation—rare among lower-middle-income countries—created unprecedented opportunities for data-driven healthcare transformation, including AI-assisted diagnostics.

Yet the war has inflicted wounds that statistics struggle to convey. The World Health Organization documented over 1,500 attacks on healthcare facilities by February 2024. Researchers verified 334 confirmed attacks on 267 healthcare facilities in just the first year of full-scale conflict. The Ukrainian Cabinet of Ministers reports 211 medical facilities completely destroyed and over 1,700 damaged—including diagnostic imaging departments housing irreplaceable CT and MRI equipment. Frontline regions—Kharkiv, Donetsk, Luhansk, Zaporizhzhia, and Kherson oblasts—bear disproportionate infrastructure losses while simultaneously facing elevated diagnostic demands from trauma casualties.

Understanding this infrastructure landscape—its pre-war baseline, wartime degradation, regional variations, and reconstruction trajectory—is essential for designing realistic AI implementation strategies. This article provides that foundational analysis, examining equipment density and distribution, digital infrastructure and standards compliance, workforce considerations, and actionable pathways for AI integration within Ukrainian medical imaging.

2. Literature Review

2.1 Global Medical Imaging Equipment Density Standards

International comparisons provide crucial context for assessing Ukrainian imaging infrastructure. According to OECD Health Statistics 2024, CT scanner density varies dramatically across developed economies. Japan leads globally with 115.7 CT scanners per million population, followed by Australia at 69.6 per million. Most Western European nations cluster between 20-40 units per million: Germany at 35.3, Italy at 34.8, and the United Kingdom at 9.5 per million reflecting its more centralized National Health Service approach.

MRI equipment density follows similar patterns, with Japan again leading at 57.4 units per million, Germany at 34.7, and the United States at approximately 40 per million. The European average hovers around 15-25 MRI units per million population. These benchmarks highlight that achieving EU-candidate country standards would require Ukraine to approximately triple its CT capacity and quadruple MRI availability—a challenge complicated by wartime destruction.

2.2 The Ukrainian eHealth System Architecture

Ukraine’s digital health transformation, while interrupted by conflict, established critical infrastructure for future AI integration. The electronic healthcare system architecture, as documented by Malakhov et al. (2023) in their comprehensive PMC analysis, operates on a two-level structure: a centralized national database managed by the State Enterprise “eHealth” and approximately 40 regional Medical Information Systems (MIS) developed by private IT companies addressing local needs.

This architecture offers both advantages and challenges for AI imaging deployment. The centralized component ensures standardized data formats and interoperability across regional systems—essential for training machine learning models on diverse patient populations. By 2022, the system handled enormous transaction volumes, demonstrating scalability that many healthcare IT infrastructures lack. However, the war decimated the MIS ecosystem: of 40 operational systems pre-war, only 4-6 are projected to survive, creating workflow disruption as medical staff trained on defunct systems must migrate to survivors.

2.3 International Experience with Post-Conflict Healthcare Reconstruction

Historical precedents inform expectations for Ukrainian healthcare reconstruction. The WHO’s Health as the Bridge for Peace initiative documented healthcare rebuilding in Bosnia-Herzegovina, Kosovo, and other post-conflict settings, identifying equipment procurement, workforce retention, and supply chain resilience as interconnected challenges. Notably, these cases demonstrate that conflict often accelerates digital transformation—displaced healthcare workers adopt telemedicine, centralized systems replace fragmented local records, and international donors prioritize modern equipment installation.

The RAD-AID International organization’s work in Ukraine since 2015 provides directly relevant experience. Their Radiology-Readiness Assessment and PACS-Readiness Assessment conducted in 2022 identified low-resource Ukrainian health institutions struggling with infrastructural damage. Their 2024 agreement to support PACS and IT implementation at Kyiv City Clinical Hospital #18 represents a model for combining international expertise with local capacity building—including plans for AI integration by 2025.

2.4 AI Readiness Frameworks for Healthcare Systems

Assessing AI readiness requires examining multiple infrastructure dimensions beyond equipment counts. The WHO’s 2021 guidance on AI for health identifies six foundational requirements: digital infrastructure and connectivity, data governance frameworks, workforce digital literacy, regulatory environments, ethical oversight mechanisms, and interoperability standards. Scholarly work by Rajpurkar et al. on radiology AI deployment emphasizes additional technical requirements: DICOM-compliant imaging equipment, PACS connectivity for workflow integration, and sufficient bandwidth for cloud-based AI processing.

For Ukraine, each dimension presents specific challenges. DICOM (Digital Imaging and Communications in Medicine) compliance varies significantly across equipment vintage—older Soviet-era machines and early post-Soviet acquisitions often lack digital output capabilities entirely. PACS adoption, estimated at 15-25% of Ukrainian hospitals pre-war, lags far behind Western European levels of 70-90%. Meanwhile, internet connectivity in rural areas and conflict-affected regions may not support real-time AI analysis requiring significant data transmission.

3. Methodology

3.1 Data Sources and Assessment Framework

This infrastructure assessment synthesizes data from multiple sources given the absence of comprehensive, current Ukrainian medical equipment registries. Primary sources include:

• World Health Organization Global Health Observatory: Medical equipment density statistics through 2021, providing pre-war baseline
• Ukrainian Ministry of Health and NHSU publications: eHealth system statistics, healthcare reform documentation, and facility reports
• Peer-reviewed academic literature: Including PMC-indexed studies on Ukrainian digital health (Malakhov et al. 2023, Mudge et al. 2025)
• International organization reports: USAID LHSS telemedicine assessment (September-November 2022), WHO attack documentation, RAD-AID readiness assessments
• Ukrainian Cabinet of Ministers announcements: Facility damage statistics and reconstruction initiatives
• News reports and humanitarian organization documentation: Verified incident reports from Physicians for Human Rights, IOM Ukraine, and Ukrainian Healthcare Center

3.2 Regional Analysis Approach

Ukraine’s 24 oblasts (regions) plus Kyiv city exhibit substantial heterogeneity in healthcare infrastructure, economic development, and conflict exposure. Our analysis categorizes regions into five tiers based on AI readiness potential:

3.3 AI Readiness Scoring Dimensions

For each region, we assess seven dimensions contributing to AI diagnostic imaging readiness:

1. Equipment Availability: CT, MRI, and digital X-ray units per population
2. DICOM Compliance: Estimated percentage of equipment producing standard digital output
3. PACS Deployment: Presence of picture archiving systems for workflow integration
4. Network Infrastructure: Internet bandwidth and reliability for cloud AI connectivity
5. Power Supply Reliability: Critical for equipment operation and data integrity
6. Radiologist Availability: Specialist workforce for AI supervision and validation
7. eHealth Integration: Connection to national electronic health record system

4. Results

4.1 Pre-War Equipment Baseline (2021)

Reconstructing Ukraine’s pre-war imaging equipment inventory reveals a healthcare system with significant regional disparities. Based on WHO data and Ministry of Health reports, Ukraine operated approximately 310 CT scanners and 160 MRI units across public and private facilities—translating to approximately 7.2 CT and 3.8 MRI units per million population against a pre-war population of approximately 43 million.

This placed Ukraine below Poland (17.4 CT per million), roughly comparable to some Latin American middle-income countries, but ahead of many former Soviet republics. Importantly, these aggregate figures masked extreme concentration: approximately 40% of advanced imaging equipment clustered in Kyiv city and Kyiv oblast, with Kharkiv, Dnipro, Odesa, and Lviv hosting another 30% combined. Rural oblasts in western and central Ukraine operated with minimal CT access, while many district hospitals lacked any advanced imaging.

4.2 War Impact Assessment (2022-2025)

The Russian invasion inflicted catastrophic damage on Ukrainian healthcare infrastructure. Verified documentation provides stark statistics:

Radiology departments suffered disproportionately due to their fixed, heavy equipment and typical ground-floor or basement location vulnerable to explosive blast damage. Case examples illustrate the destruction: Bashtanka district hospital in southern Ukraine lost its outpatient clinic and critical imaging equipment to a Russian missile strike on April 19, 2022. Izium Central City Hospital in Kharkiv oblast, occupied by Russian forces as a military base, continues operating at only 10% capacity due to damage. The Kharkiv Regional Clinical Hospital required UAH 119 million in new equipment investment for recovery.

4.3 Digital Infrastructure Status

Despite physical destruction, Ukraine’s digital healthcare infrastructure demonstrated remarkable resilience. The eHealth system remained operational throughout the invasion, with key statistics:

However, imaging-specific digital infrastructure lags behind general eHealth adoption:

• PACS Deployment: Estimated 15-25% of hospitals have functional picture archiving systems, concentrated in Kyiv, Lviv, and major private facilities
• DICOM Compliance: Approximately 60% of CT/MRI equipment produces DICOM-standard output; older X-ray and ultrasound often analog or proprietary
• RIS Integration: Radiology Information Systems remain rare outside university hospitals
• Network Connectivity: Urban hospitals generally have adequate bandwidth; rural facilities face 2-10 Mbps limitations

4.4 Workforce Assessment

Radiologist availability represents a critical constraint for AI deployment—algorithms require human supervision, and trust-building demands accessible expertise for explaining AI-assisted findings to patients and colleagues. Ukraine’s radiologist workforce faces acute pressures:

• Pre-war baseline: Approximately 4,000-5,000 radiologists and radiology technicians
• Wartime attrition: Significant emigration (especially female workforce with children), military service demands, displacement
• Current estimate: 2,500-3,000 active radiologists, concentrated in safer western and central regions
• Distribution: Kyiv city ~400, Lviv ~200, Odesa ~180, Dnipro ~170, others distributed
• Training pipeline: Medical universities continue radiology programs but graduation rates affected

4.5 Regional AI Readiness Scores

Synthesizing equipment, digital infrastructure, and workforce data yields regional AI readiness assessments:

5. Discussion

5.1 Strategic Implications for AI Deployment

Our infrastructure assessment reveals a healthcare system with substantial regional variation in AI readiness. Kyiv city emerges as the clear leader—combining equipment availability, digital infrastructure, radiologist concentration, and relative security. Any AI diagnostic imaging pilot should prioritize Kyiv facilities, with RAD-AID’s 2025 Kyiv City Clinical Hospital #18 initiative representing an ideal starting point.

Lviv and Odesa present attractive secondary targets. Lviv has absorbed displaced healthcare capacity and personnel, strengthening its infrastructure beyond pre-war levels. Its distance from conflict zones provides operational stability essential for sustained AI implementation. Odesa, despite intermittent attacks, maintains strong port connectivity for equipment imports and has historically led in telemedicine adoption among Ukrainian regions.

5.2 Infrastructure Gap Priorities

Closing AI readiness gaps requires targeted investments across multiple dimensions:

The shift toward cloud-based PACS offers particular advantages in the Ukrainian context. Traditional on-premises PACS installations face destruction risk and require local IT expertise increasingly scarce outside major cities. Cloud solutions—accessible via internet from any surviving workstation—provide resilience against physical attacks while centralizing data for AI model training.

International equipment donations often include modern DICOM-compliant scanners, accelerating this transition. The EBRD-EU partnership providing MRI equipment to Ukrainian clinics exemplifies how humanitarian aid can simultaneously address immediate diagnostic needs and AI readiness requirements.

5.3 Hybrid AI Architecture for Ukrainian Conditions

Given infrastructure constraints—intermittent connectivity, power disruptions, limited IT support—optimal AI deployment in Ukraine requires hybrid architectures rather than purely cloud-dependent solutions. We propose a three-tier model:

Edge AI processing—running lightweight models directly on imaging equipment or associated workstations—provides immediate utility even in disconnected scenarios. A CT scanner producing DICOM images could embed triage AI identifying critical findings (pneumothorax, stroke) for immediate radiologist attention regardless of network status. When connectivity exists, cases upload to cloud systems for comprehensive analysis and model improvement.

5.4 The ScanLab Implementation Context

For the ScanLab platform specifically, our infrastructure assessment recommends a phased geographic deployment aligned with regional readiness scores. Phase 1 should target 3-5 Kyiv facilities with proven PACS infrastructure and radiologist availability. Phase 2 expands to Lviv and Odesa university hospitals. Phase 3 addresses regional centers through mobile diagnostic units and telemedicine integration. Phase 4—post-conflict reconstruction permitting—gradually extends coverage to currently conflict-affected regions.

5.5 International Support Coordination

Effective AI implementation requires coordination across multiple international support streams currently operating somewhat independently. Key stakeholders include:

• RAD-AID International: PACS/AI implementation at Kyiv hospital, radiology capacity building
• USAID LHSS: Telemedicine assessment and digital health support
• UNDP Ukraine: eHealth Summit support, digital healthcare development
• EBRD/EU: Medical equipment financing and energy independence
• WHO: Healthcare system recovery planning, AI governance guidance
• Medweb and private IT partners: PACS implementation, technical support

A national AI imaging coordination body—potentially under Ministry of Health or NHSU leadership—could optimize resource allocation, prevent duplicative pilots, and ensure consistent standards across initiatives.

6. Conclusion

Ukraine’s medical imaging infrastructure presents a paradox of simultaneous devastation and opportunity. The Russian invasion has destroyed equipment, displaced personnel, and disrupted healthcare delivery across conflict-affected regions. Yet Ukraine possesses foundations that many comparable countries lack: a functional national electronic health system, demonstrated digital health leadership, and international support for reconstruction. These assets, strategically leveraged, can accelerate AI diagnostic imaging adoption even amid ongoing conflict.

Our assessment identifies Kyiv, Lviv, and Odesa as priority locations for AI implementation, with infrastructure readiness scores of 87%, 70%, and 67% respectively. Critical gap closure priorities include PACS expansion from current 15-25% to 60-70% hospital coverage, DICOM equipment modernization, and hybrid AI architectures tolerant of intermittent connectivity.

The path forward requires recognizing that AI deployment and infrastructure reconstruction are not sequential—they can and should proceed in parallel. Modern AI-enabled equipment donated for reconstruction automatically advances AI readiness. Cloud-based PACS implementations simultaneously address storage needs and create AI integration pathways. Telemedicine expansion for specialist access creates channels for AI-assisted remote interpretation.

For the ScanLab initiative and broader Ukrainian healthcare AI strategy, this infrastructure assessment provides essential context for realistic planning. The recommendations herein—Kyiv-first pilots, cloud-based PACS prioritization, hybrid AI architectures, and coordinated international support—offer an actionable framework for translating AI potential into diagnostic reality for Ukrainian patients.

References

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