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市场调查报告书
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2018621

人工智慧在医学影像领域的市场：按组件、影像技术、应用和最终用户划分－2026-2032年全球市场预测

Artificial Intelligence in Medical Imaging Market by Component, Imaging Technology, Application, End-User - Global Forecast 2026-2032
出版日期: | 出版商: 360iResearch | 英文 185 Pages | 商品交期: 最快1-2个工作天内

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2025 年，医学影像领域的人工智慧 (AI) 市场价值为 28.7 亿美元，预计到 2026 年将成长至 36.7 亿美元，复合年增长率为 28.79%，到 2032 年将达到 169.1 亿美元。

主要市场统计数据
基准年 2025 28.7亿美元
预计年份：2026年 36.7亿美元
预测年份 2032 169.1亿美元
复合年增长率 (%) 28.79%

在医学影像领域策略性地应用人工智慧，将其定位为临床转型、加速研究和提高营运效率的催化剂。

人工智慧在医学影像领域的应用已从一个充满前景的研究方向发展成为提升诊断准确性、实现工作流程自动化和增强临床决策支援的关键基础。这项发展说明了为何人工智慧驱动的影像解决方案不再是边缘实验，而是现代医疗服务的核心要素。演算法效能的提升、计算资源的增强以及丰富影像资料集的积累，使得研究阶段的原型能够迅速实用化临床实用工具，从而辅助放射科医生的工作、支持多学科团队协作并简化行政管理任务。

新的人工智慧范式如何重塑整个医疗保健领域的诊断工作流程、影像解读和临床决策，从而为营运带来巨大变化。

近年来，医学影像的采集、处理和解读方式发生了变革性的变化。其中一项根本性的变化是深度学习架构的成熟，使其在病灶检测、量化和分诊优先排序等任务中展现出具有临床意义的表现。随着演算法变得更加稳健，资料密集型方法能够获得更大、更精心整理的资料集，临床接受度也在不断提高，使得医疗专业人员能够将人工智慧的输出结果整合到诊断工作流程中，作为决策支援而非黑箱结论。

评估美国关税政策在 2025 年对全球医疗影像供应链、筹资策略和医疗设备定价趋势的累积影响。

2025年的关税环境将为整个医疗影像生态系统中的供应商、买家和整合商带来更大的复杂性。进口关税和跨境贸易政策的变化正在对供应链策略、采购决策以及使用医疗影像硬体和软体的解决方案的总拥有成本产生连锁反应。供应商和医疗系统正在重新思考其筹资策略，以减轻关税相关成本波动的影响，同时确保获得关键技术。

基于细分的关键见解揭示了元件、成像技术、应用程式和最终用户的差异如何影响部署路径和解决方案策略。

为了解部署模式和商业化路径，必须仔细分析市场如何按组件、成像技术、应用和最终用户进行细分。组件细分区分硬体、软体和服务，服务进一步细分为託管服务和专业服务。这种区分明确了哪些参与者在创造经常性收入，哪些参与者专注于销售资本设备。影像技术细分区分CT扫描仪、MRI系统、超音波设备和X光系统。每种设备都有其独特的整合挑战、临床工作流程和监管要求，这些都会影响人工智慧开发的优先顺序。

美洲、欧洲、中东和非洲以及亚太地区招募、监管和伙伴关係生态系统的区域趋势和策略细微差别。

区域趋势正深刻影响人工智慧在医学影像领域的应用轨迹，导緻美洲、欧洲、中东、非洲和亚太地区在监管环境、报销机制和伙伴关係方面存在差异。在美洲，创新中心和早期采用人工智慧的医疗保健系统正在加速概念验证（PoC）专案的部署，而复杂的支付方环境则强调了证明临床和经济价值的证据的重要性。该地区的监管流程优先考虑安全性和有效性，跨境合作通常专注于数据协调，以进行多中心检验。

深入了解企业层面的竞争与合作，并专注于领先的医疗技术和人工智慧公司的创新策略、伙伴关係模式和技术融合。

医疗影像人工智慧领域的企业级发展趋势反映了竞争差异化与协作生态系统的融合。主要企业正采用多管齐下的策略，结合内部演算法开发、与影像设备製造商的合作以及与临床网路的协作，以加速检验和市场渗透。创新策略通常强调基于平台的方法，支援跨模态的模组化分析，使供应商能够提供整合硬体、软体和託管服务的捆绑式解决方案。

为行业领导者提供可操作的建议，以加速安全人工智慧整合、优化采购并增强临床和商业性价值提案。

产业领导者必须采取果断行动，将技术潜力转化为可持续的临床和商业性价值。首先，各机构应优先进行严格的临床检验，使绩效指标与真实世界的临床终点和临床医师的工作流程相符。这能确保人工智慧的输出在临床实务中实用可靠。投资前瞻性研究、多中心试验和实施后监测，将有助于赢得保险公司、监管机构和临床医生的信任。

严谨的调查方法，说明了用于得出基于证据的结论的资料来源、分析框架、检验协议和相关人员参与。

本研究采用多方面方法，结合与关键相关人员的对话、二手资讯的检验以及系统性的分析框架，以确保研究结果的稳健性和相关性。关键资讯输入包括对临床医生、放射科医生、采购经理和技术主管的结构化访谈，从而获得关于临床效用、营运限制和采购动机的第一手观点。二手分析则利用同行评审文献、监管指导文件和公开的临床检验研究来支持​​研究结论并追踪临床影响的证据。

这份权威总结整合了技术、监管、商业和临床等方面的内容，清楚地为整个生态系统中的相关人员提供了切实可行的见解。

总之，人工智慧正从技术、营运和商业性角度变革医学影像诊断。演算法能力的进步及其与影像平台的集成，催生了新的诊断工作流程并提高了效率。同时，监管的日趋成熟以及保险公司的严格审查，正促使供应商将工作重点转向透明的检验和可衡量的临床价值。组件、模式、应用和终端用户群体之间的相互作用表明，单一方法无法满足所有市场的需求。相反，针对学术研究中心、诊断机构和医院的特定需求量身定制的策略，最有可能实现可持续的普及应用。

目录

第一章：序言

第二章：调查方法

  • 调查设计
  • 研究框架
  • 市场规模预测
  • 数据三角测量
  • 调查结果
  • 调查的前提
  • 研究限制

第三章执行摘要

  • 首席体验长观点
  • 市场规模和成长趋势
  • 2025年市占率分析
  • FPNV定位矩阵，2025
  • 新的商机
  • 下一代经营模式
  • 产业蓝图

第四章 市场概览

  • 产业生态系与价值链分析
  • 波特五力分析
  • PESTEL 分析
  • 市场展望
  • 上市策略

第五章 市场洞察

  • 消费者洞察与终端用户观点
  • 消费者体验基准
  • 机会映射
  • 分销通路分析
  • 价格趋势分析
  • 监理合规和标准框架
  • ESG与永续性分析
  • 中断和风险情景
  • 投资报酬率和成本效益分析

第六章：美国关税的累积影响，2025年

第七章：人工智慧的累积影响，2025年

第八章：医疗影像人工智慧市场：按组件划分

  • 硬体
  • 服务
    • 託管服务
    • 专业服务
  • 软体

第九章：医学影像人工智慧市场：依诊断影像技术划分

  • CT扫描仪
  • 磁振造影系统
  • 超音波诊断设备
  • X射线系统

第十章：人工智慧在医学影像领域的市场：按应用划分

  • 循环系统
  • 神经病学
  • 肿瘤学
  • 病理
  • 放射科

第十一章：医学影像人工智慧市场：按最终用户划分

  • 学术和研究机构
  • 诊断中心
  • 医院和诊所

第十二章：人工智慧在医学影像领域的市场：按地区划分

  • 北美洲和南美洲
    • 北美洲
    • 拉丁美洲
  • 欧洲、中东和非洲
    • 欧洲
    • 中东
    • 非洲
  • 亚太地区

第十三章：医学影像人工智慧市场：按类别划分

  • ASEAN
  • GCC
  • EU
  • BRICS
  • G7
  • NATO

第十四章：人工智慧在医学影像领域的市场：按国家划分

  • 我们
  • 加拿大
  • 墨西哥
  • 巴西
  • 英国
  • 德国
  • 法国
  • 俄罗斯
  • 义大利
  • 西班牙
  • 中国
  • 印度
  • 日本
  • 澳洲
  • 韩国

第十五章：美国医疗影像领域的人工智慧市场

第十六章：中国医学影像人工智慧市场

第十七章 竞争格局

  • 市场集中度分析，2025年
    • 浓度比（CR）
    • 赫芬达尔-赫希曼指数 (HHI)
  • 近期趋势及影响分析，2025 年
  • 2025年产品系列分析
  • 基准分析，2025 年
  • aetherAI
  • Agfa-Gevaert Group
  • Behold.ai Technologies Limited
  • Brainomix Limited
  • Butterfly Network, Inc.
  • Cancer Center.ai
  • CureMetrix, Inc.
  • Dr CADx
  • EchoNous, Inc.
  • Enlitic, Inc.
  • Fujifilm Holdings Corporation
  • GE Healthcare
  • Intelerad Medical Systems Incorporated
  • Koninklijke Philips NV
  • Nano-X Imaging Ltd.
  • Nuance Communications, Inc.
  • NVIDIA Corporation
  • RetinAI
  • Shanghai United Imaging Healthcare Co., LTD
  • Siemens Healthineers AG
  • SigTuple Technologies Private Limited.
  • Subtle Medical
  • Tempus AI, Inc
  • Volpara Health Technologies Ltd.
  • Brainminer Ltd
Product Code: MRR-221461476A09

The Artificial Intelligence in Medical Imaging Market was valued at USD 2.87 billion in 2025 and is projected to grow to USD 3.67 billion in 2026, with a CAGR of 28.79%, reaching USD 16.91 billion by 2032.

KEY MARKET STATISTICS
Base Year [2025] USD 2.87 billion
Estimated Year [2026] USD 3.67 billion
Forecast Year [2032] USD 16.91 billion
CAGR (%) 28.79%

A strategic introduction that frames artificial intelligence in medical imaging as a catalyst for clinical transformation, research acceleration, and operational efficiency

Artificial intelligence in medical imaging has evolved from a promising research area into a critical enabler of diagnostic precision, workflow automation, and clinical decision support. This introduction sets the context for why AI-driven imaging solutions are no longer peripheral experiments but central components of modern care delivery. Advances in algorithmic performance, combined with improved computational resources and richer image datasets, have accelerated the translation of research prototypes into clinically relevant tools that augment radiologists, support multidisciplinary teams, and streamline administrative processes.

As care providers contend with growing imaging volumes, workforce shortages, and pressure to deliver value-based outcomes, AI offers tangible levers to enhance throughput and interpretive consistency while reducing time-to-diagnosis. At the same time, the intersection of imaging hardware, software algorithms, and services is creating new business models that span device manufacturers, software vendors, and service integrators. These convergences introduce both opportunities and complexities: while integration promises better end-to-end solutions, it also heightens the importance of interoperability, data governance, and rigorous clinical validation.

In this landscape, stakeholders must balance technical performance with regulatory compliance and clinical utility. Therefore, strategic planning for AI adoption requires not only technical appraisal but also operational readiness, workflow redesign, and stakeholder engagement. This executive summary will synthesize technological trends, policy influences, segmentation implications, regional dynamics, and company-level strategies to support informed decision-making across clinical, commercial, and policy domains.

How emerging AI paradigms are reshaping diagnostic workflows, image interpretation, and clinical decision-making across care settings with profound operational shifts

The last several years have witnessed transformative shifts that are redefining how medical images are acquired, processed, and interpreted. One fundamental change is the maturation of deep learning architectures that deliver clinically meaningful performance for tasks such as lesion detection, quantification, and triage prioritization. As algorithms become more robust and data-hungry approaches find access to larger curated datasets, clinical acceptance has increased, enabling practitioners to incorporate AI outputs into diagnostic workflows as decision support rather than black-box conclusions.

Concurrently, the integration of AI into imaging hardware and enterprise systems is changing procurement and operational models. Imaging devices are increasingly offered with embedded analytics, subscription-based software, and optional managed services that shift capital expenditures toward operational expenditure frameworks. This shift is accompanied by a growing emphasis on end-to-end interoperability: AI tools must integrate seamlessly with picture archiving and communication systems, electronic health records, and reporting platforms to preserve continuity of care.

Another pivotal shift is regulatory evolution. Regulators are developing frameworks for algorithm transparency, performance monitoring, and post-market surveillance, which in turn shapes vendor roadmaps and health system adoption strategies. Moreover, collaborative models between clinicians, data scientists, and vendors are becoming the norm, supporting iterative validation and local calibration of AI models. Taken together, these shifts accelerate clinical integration while underscoring the need for robust governance, transparent validation, and investment in clinical change management.

Evaluating the cumulative effects of United States tariff policies in 2025 on global medical imaging supply chains, procurement strategies, and device pricing dynamics

The tariff environment in 2025 has introduced additional layers of complexity for suppliers, purchasers, and integrators across the medical imaging ecosystem. Changes in import duties and cross-border trade policies have a cascading influence on supply chain strategies, sourcing decisions, and the total cost of ownership for imaging hardware and software-enabled solutions. Vendors and health systems are recalibrating procurement strategies to mitigate exposure to tariff-driven cost fluctuations while maintaining access to critical technologies.

In response to tariff pressures, many suppliers are accelerating diversification of manufacturing and component sourcing. Where feasible, production is relocated or expanded across multiple jurisdictions to reduce reliance on a single supply corridor and to preserve price stability. This geographic redistribution can lengthen supply chains in the near term while introducing resilience benefits over time. Simultaneously, procurement teams are renegotiating contract terms to reflect tariff contingencies and to secure more flexible maintenance and upgrade arrangements that limit capital risk.

Tariffs also influence partnership strategies: vendors increasingly explore local partnerships, licensing arrangements, and co-development agreements to penetrate tariff-sensitive markets. For clinical operators, strategic inventory management and stronger vendor-service relationships become essential to ensure uptime and continuity of care. Ultimately, while tariffs introduce short-term cost and logistical challenges, they are also catalyzing structural adjustments that prioritize supply chain resilience, local collaboration, and smarter procurement frameworks across the sector.

Key segmentation-driven insights revealing how component, imaging technology, application, and end-user distinctions influence adoption pathways and solution strategies

Understanding adoption patterns and commercialization pathways requires close attention to how the market is segmented along component, imaging technology, application, and end-user dimensions. Component segmentation differentiates hardware from software and services, with services further subdivided into managed and professional offerings, and this split clarifies which players derive recurring revenue versus those focused on capital equipment sales. Imaging technology segmentation distinguishes CT scanners, MRI systems, ultrasound devices, and X-ray systems, each with distinct integration challenges, clinical workflows, and regulatory expectations that influence AI development priorities.

Application segmentation reveals where clinical demand for AI is most concentrated: cardiology and neurology use cases often require high temporal and spatial fidelity and benefit from specialized analytics; oncology and radiology applications demand robust lesion detection and characterization; pathology-driven image analysis is advancing as digitization of slides accelerates. End-user segmentation highlights differing procurement cycles and adoption drivers among academic and research institutions, diagnostic centers, and hospitals and clinics. Academic centers frequently drive early validation and complex use cases, diagnostic centers emphasize throughput and turn-around time efficiencies, and hospitals and clinics prioritize interoperability, vendor support, and integration into broader care pathways.

These segmentation lenses are essential for vendors and health systems to match product design, service models, and validation strategies to the specific needs of each buyer cohort. Consequently, segmentation-aware planning enables more precise go-to-market approaches, targeted clinical studies, and differentiated support services that increase adoption likelihood and clinical impact.

Regional dynamics and strategic nuances across the Americas, Europe Middle East & Africa, and Asia-Pacific that shape adoption, regulation, and partnership ecosystems

Regional dynamics profoundly shape the trajectory of AI adoption in medical imaging, producing divergent regulatory, reimbursement, and partnership landscapes across the Americas, Europe Middle East & Africa, and Asia-Pacific. In the Americas, innovation hubs and early-adopter health systems accelerate proof-of-concept deployments, while a complex payer environment underscores the importance of evidence demonstrating clinical and economic value. Regulatory pathways in this region emphasize safety and efficacy, and cross-border collaborations often focus on data harmonization for multi-center validation.

In Europe Middle East & Africa, the regulatory mosaic introduces both constraints and incentives for adoption. Europe's evolving regulatory standards demand transparency and post-market vigilance, prompting vendors to emphasize explainability and real-world performance monitoring. In the Middle East and Africa, growth opportunities are shaped by investment in imaging infrastructure and strategic partnerships that can leapfrog traditional adoption curves, but success depends on local capacity-building and workforce training.

The Asia-Pacific region is characterized by rapid infrastructure expansion, large population-scale datasets, and proactive government initiatives to digitize healthcare. These factors create fertile ground for accelerated deployment of AI-enabled imaging solutions, though local regulatory and data sovereignty considerations necessitate careful compliance strategies. Across all regions, successful market entry combines clinical validation, regulatory alignment, and culturally attuned commercialization plans that reflect local care delivery models and reimbursement realities.

Competitive and collaborative company-level insights highlighting innovation strategies, partnership models, and technology convergence among leading medtech and AI firms

Company-level dynamics in the medical imaging AI space reflect a blend of competitive differentiation and collaborative ecosystems. Leading organizations deploy multi-pronged strategies that combine in-house algorithm development, partnerships with imaging device manufacturers, and alliances with clinical networks to accelerate validation and market reach. Innovation strategies often emphasize platform approaches that support modular analytics across modalities, enabling vendors to offer bundled solutions that integrate hardware, software, and managed services.

Strategic partnerships are increasingly common as companies recognize the value of combining algorithmic expertise with clinical domain knowledge and imaging hardware capabilities. Co-development agreements with clinical sites expedite access to annotated datasets and facilitate real-world performance assessments. Meanwhile, service-oriented models-particularly managed services-allow vendors to provide continuous optimization, model maintenance, and performance monitoring, enhancing long-term customer value and differentiation.

Mergers, acquisitions, and licensing arrangements remain a core route to scale, especially for firms seeking rapid access to complementary technologies or geographic markets. At the same time, emphasis on ethical AI practices, transparent validation, and robust post-market surveillance is becoming a competitive requirement, not just a regulatory checkbox. In this environment, companies that combine credible clinical evidence, scalable deployment models, and strong customer support will be best positioned to capture value and sustain adoption over time.

Actionable recommendations for industry leaders to accelerate safe AI integration, optimize procurement, and strengthen clinical and commercial value propositions

Industry leaders must take decisive steps to translate technological promise into sustained clinical and commercial value. First, organizations should prioritize rigorous clinical validation that aligns performance metrics with real-world endpoints and clinician workflows, thereby ensuring that AI outputs are actionable and trusted at the point of care. Investing in prospective studies, multi-center trials, and post-deployment monitoring will build credibility with payers, regulators, and clinicians alike.

Second, companies should design interoperable solutions that integrate smoothly with existing imaging modalities, hospital information systems, and cloud or on-premises infrastructures. Interoperability reduces friction during deployment and supports scalable rollouts across heterogeneous IT environments. Third, operational readiness is essential: leadership should allocate resources for clinician training, change management, and continuous model governance to maintain performance and address drift over time.

Fourth, supply chain and procurement strategies must incorporate contingency planning for tariff and trade disruptions, emphasizing diversified sourcing and local partnerships where appropriate. Fifth, ethical and regulatory compliance should be embedded from product design through post-market surveillance, with transparent reporting of limitations and performance. Finally, leaders should explore commercial models that balance upfront capital with subscription and managed services to align incentives and sustain long-term relationships with customers. Collectively, these actions will accelerate safe adoption and create durable competitive advantage.

A rigorous research methodology describing data sources, analytical frameworks, validation protocols, and stakeholder engagement used to ensure evidence-based conclusions

This research synthesis is grounded in a multi-method approach that combines primary stakeholder engagement, secondary source triangulation, and systematic analytical frameworks to ensure robustness and relevance. Primary inputs include structured interviews with clinicians, imaging technicians, procurement leaders, and technology executives, providing firsthand perspectives on clinical utility, operational constraints, and purchase drivers. Secondary analysis draws on peer-reviewed literature, regulatory guidance documents, and publicly available clinical validation studies to corroborate claims and trace evidence of clinical impact.

Analytical frameworks employed in the study include modality-specific evaluation matrices, risk and compliance assessments, and integration readiness scoring to compare solutions across technical, clinical, and operational dimensions. Data validation protocols encompass cross-verification of reported performance metrics with independent studies and examination of post-market surveillance mechanisms where available. Stakeholder engagement protocols ensure that diverse geographic and care-setting perspectives are represented, enabling a nuanced understanding of regional and end-user variations.

To enhance transparency, the methodology documents assumptions, inclusion criteria, and limitations, and it outlines how qualitative insights were synthesized with quantitative indicators. Sensitivity analyses were applied where appropriate to test the robustness of comparative judgments. This methodological rigor supports confidence in the conclusions and provides a replicable foundation for subsequent updates and extensions.

A conclusive synthesis drawing together technological, regulatory, commercial, and clinical threads to articulate practical implications for stakeholders across the ecosystem

In conclusion, artificial intelligence is reshaping medical imaging across technological, operational, and commercial dimensions. Advances in algorithmic capability and integration into imaging platforms are enabling new diagnostic workflows and efficiency gains, while regulatory maturation and payor scrutiny are redirecting vendor priorities toward transparent validation and measurable clinical value. The interplay of component, modality, application, and end-user segmentation highlights that no single approach will fit all markets; instead, tailored strategies that reflect the specific needs of academic research centers, diagnostic facilities, and hospitals yield the highest probability of sustained adoption.

Regionally, the market is characterized by differentiated adoption drivers and regulatory expectations, with each geography offering unique opportunities and constraints. Tariff dynamics in 2025 are introducing supply chain complexity but are also incentivizing more resilient procurement and localized partnerships. At the company level, the most successful organizations will be those that combine rigorous clinical evidence, interoperability, scalable service models, and strong post-market governance.

Ultimately, the path forward requires coordinated action across vendors, clinicians, payers, and regulators to ensure that AI-enabled imaging technologies deliver measurable improvements in diagnostic accuracy, workflow efficiency, and patient outcomes. By aligning technological innovation with clinical needs and robust governance, stakeholders can realize the full potential of AI while managing risk and fostering sustainable adoption across diverse healthcare systems.

Table of Contents

1. Preface

  • 1.1. Objectives of the Study
  • 1.2. Market Definition
  • 1.3. Market Segmentation & Coverage
  • 1.4. Years Considered for the Study
  • 1.5. Currency Considered for the Study
  • 1.6. Language Considered for the Study
  • 1.7. Key Stakeholders

2. Research Methodology

  • 2.1. Introduction
  • 2.2. Research Design
    • 2.2.1. Primary Research
    • 2.2.2. Secondary Research
  • 2.3. Research Framework
    • 2.3.1. Qualitative Analysis
    • 2.3.2. Quantitative Analysis
  • 2.4. Market Size Estimation
    • 2.4.1. Top-Down Approach
    • 2.4.2. Bottom-Up Approach
  • 2.5. Data Triangulation
  • 2.6. Research Outcomes
  • 2.7. Research Assumptions
  • 2.8. Research Limitations

3. Executive Summary

  • 3.1. Introduction
  • 3.2. CXO Perspective
  • 3.3. Market Size & Growth Trends
  • 3.4. Market Share Analysis, 2025
  • 3.5. FPNV Positioning Matrix, 2025
  • 3.6. New Revenue Opportunities
  • 3.7. Next-Generation Business Models
  • 3.8. Industry Roadmap

4. Market Overview

  • 4.1. Introduction
  • 4.2. Industry Ecosystem & Value Chain Analysis
    • 4.2.1. Supply-Side Analysis
    • 4.2.2. Demand-Side Analysis
    • 4.2.3. Stakeholder Analysis
  • 4.3. Porter's Five Forces Analysis
  • 4.4. PESTLE Analysis
  • 4.5. Market Outlook
    • 4.5.1. Near-Term Market Outlook (0-2 Years)
    • 4.5.2. Medium-Term Market Outlook (3-5 Years)
    • 4.5.3. Long-Term Market Outlook (5-10 Years)
  • 4.6. Go-to-Market Strategy

5. Market Insights

  • 5.1. Consumer Insights & End-User Perspective
  • 5.2. Consumer Experience Benchmarking
  • 5.3. Opportunity Mapping
  • 5.4. Distribution Channel Analysis
  • 5.5. Pricing Trend Analysis
  • 5.6. Regulatory Compliance & Standards Framework
  • 5.7. ESG & Sustainability Analysis
  • 5.8. Disruption & Risk Scenarios
  • 5.9. Return on Investment & Cost-Benefit Analysis

6. Cumulative Impact of United States Tariffs 2025

7. Cumulative Impact of Artificial Intelligence 2025

8. Artificial Intelligence in Medical Imaging Market, by Component

  • 8.1. Hardware
  • 8.2. Services
    • 8.2.1. Managed Services
    • 8.2.2. Professional Services
  • 8.3. Software

9. Artificial Intelligence in Medical Imaging Market, by Imaging Technology

  • 9.1. CT Scanners
  • 9.2. MRI Systems
  • 9.3. Ultrasound Devices
  • 9.4. X-ray Systems

10. Artificial Intelligence in Medical Imaging Market, by Application

  • 10.1. Cardiology
  • 10.2. Neurology
  • 10.3. Oncology
  • 10.4. Pathology
  • 10.5. Radiology

11. Artificial Intelligence in Medical Imaging Market, by End-User

  • 11.1. Academic & Research Institutions
  • 11.2. Diagnostic Centers
  • 11.3. Hospitals & Clinics

12. Artificial Intelligence in Medical Imaging Market, by Region

  • 12.1. Americas
    • 12.1.1. North America
    • 12.1.2. Latin America
  • 12.2. Europe, Middle East & Africa
    • 12.2.1. Europe
    • 12.2.2. Middle East
    • 12.2.3. Africa
  • 12.3. Asia-Pacific

13. Artificial Intelligence in Medical Imaging Market, by Group

  • 13.1. ASEAN
  • 13.2. GCC
  • 13.3. European Union
  • 13.4. BRICS
  • 13.5. G7
  • 13.6. NATO

14. Artificial Intelligence in Medical Imaging Market, by Country

  • 14.1. United States
  • 14.2. Canada
  • 14.3. Mexico
  • 14.4. Brazil
  • 14.5. United Kingdom
  • 14.6. Germany
  • 14.7. France
  • 14.8. Russia
  • 14.9. Italy
  • 14.10. Spain
  • 14.11. China
  • 14.12. India
  • 14.13. Japan
  • 14.14. Australia
  • 14.15. South Korea

15. United States Artificial Intelligence in Medical Imaging Market

16. China Artificial Intelligence in Medical Imaging Market

17. Competitive Landscape

  • 17.1. Market Concentration Analysis, 2025
    • 17.1.1. Concentration Ratio (CR)
    • 17.1.2. Herfindahl Hirschman Index (HHI)
  • 17.2. Recent Developments & Impact Analysis, 2025
  • 17.3. Product Portfolio Analysis, 2025
  • 17.4. Benchmarking Analysis, 2025
  • 17.5. aetherAI
  • 17.6. Agfa-Gevaert Group
  • 17.7. Behold.ai Technologies Limited
  • 17.8. Brainomix Limited
  • 17.9. Butterfly Network, Inc.
  • 17.10. Cancer Center.ai
  • 17.11. CureMetrix, Inc.
  • 17.12. Dr CADx
  • 17.13. EchoNous, Inc.
  • 17.14. Enlitic, Inc.
  • 17.15. Fujifilm Holdings Corporation
  • 17.16. GE Healthcare
  • 17.17. Intelerad Medical Systems Incorporated
  • 17.18. Koninklijke Philips N.V.
  • 17.19. Nano-X Imaging Ltd.
  • 17.20. Nuance Communications, Inc.
  • 17.21. NVIDIA Corporation
  • 17.22. RetinAI
  • 17.23. Shanghai United Imaging Healthcare Co., LTD
  • 17.24. Siemens Healthineers AG
  • 17.25. SigTuple Technologies Private Limited.
  • 17.26. Subtle Medical
  • 17.27. Tempus AI, Inc
  • 17.28. Volpara Health Technologies Ltd.
  • 17.29. Brainminer Ltd

LIST OF FIGURES

  • FIGURE 1. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, 2018-2032 (USD MILLION)
  • FIGURE 2. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SHARE, BY KEY PLAYER, 2025
  • FIGURE 3. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET, FPNV POSITIONING MATRIX, 2025
  • FIGURE 4. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY COMPONENT, 2025 VS 2026 VS 2032 (USD MILLION)
  • FIGURE 5. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY IMAGING TECHNOLOGY, 2025 VS 2026 VS 2032 (USD MILLION)
  • FIGURE 6. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY APPLICATION, 2025 VS 2026 VS 2032 (USD MILLION)
  • FIGURE 7. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY END-USER, 2025 VS 2026 VS 2032 (USD MILLION)
  • FIGURE 8. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY REGION, 2025 VS 2026 VS 2032 (USD MILLION)
  • FIGURE 9. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY GROUP, 2025 VS 2026 VS 2032 (USD MILLION)
  • FIGURE 10. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY COUNTRY, 2025 VS 2026 VS 2032 (USD MILLION)
  • FIGURE 11. UNITED STATES ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, 2018-2032 (USD MILLION)
  • FIGURE 12. CHINA ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, 2018-2032 (USD MILLION)

LIST OF TABLES

  • TABLE 1. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, 2018-2032 (USD MILLION)
  • TABLE 2. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY COMPONENT, 2018-2032 (USD MILLION)
  • TABLE 3. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY HARDWARE, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 4. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY HARDWARE, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 5. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY HARDWARE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 6. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY SERVICES, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 7. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY SERVICES, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 8. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY SERVICES, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 9. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY SERVICES, 2018-2032 (USD MILLION)
  • TABLE 10. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY MANAGED SERVICES, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 11. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY MANAGED SERVICES, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 12. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY MANAGED SERVICES, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 13. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY PROFESSIONAL SERVICES, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 14. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY PROFESSIONAL SERVICES, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 15. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY PROFESSIONAL SERVICES, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 16. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY SOFTWARE, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 17. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY SOFTWARE, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 18. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY SOFTWARE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 19. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY IMAGING TECHNOLOGY, 2018-2032 (USD MILLION)
  • TABLE 20. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY CT SCANNERS, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 21. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY CT SCANNERS, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 22. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY CT SCANNERS, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 23. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY MRI SYSTEMS, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 24. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY MRI SYSTEMS, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 25. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY MRI SYSTEMS, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 26. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY ULTRASOUND DEVICES, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 27. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY ULTRASOUND DEVICES, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 28. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY ULTRASOUND DEVICES, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 29. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY X-RAY SYSTEMS, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 30. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY X-RAY SYSTEMS, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 31. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY X-RAY SYSTEMS, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 32. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 33. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY CARDIOLOGY, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 34. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY CARDIOLOGY, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 35. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY CARDIOLOGY, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 36. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY NEUROLOGY, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 37. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY NEUROLOGY, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 38. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY NEUROLOGY, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 39. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY ONCOLOGY, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 40. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY ONCOLOGY, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 41. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY ONCOLOGY, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 42. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY PATHOLOGY, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 43. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY PATHOLOGY, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 44. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY PATHOLOGY, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 45. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY RADIOLOGY, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 46. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY RADIOLOGY, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 47. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY RADIOLOGY, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 48. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY END-USER, 2018-2032 (USD MILLION)
  • TABLE 49. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY ACADEMIC & RESEARCH INSTITUTIONS, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 50. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY ACADEMIC & RESEARCH INSTITUTIONS, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 51. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY ACADEMIC & RESEARCH INSTITUTIONS, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 52. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY DIAGNOSTIC CENTERS, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 53. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY DIAGNOSTIC CENTERS, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 54. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY DIAGNOSTIC CENTERS, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 55. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY HOSPITALS & CLINICS, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 56. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY HOSPITALS & CLINICS, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 57. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY HOSPITALS & CLINICS, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 58. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 59. AMERICAS ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY SUBREGION, 2018-2032 (USD MILLION)
  • TABLE 60. AMERICAS ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY COMPONENT, 2018-2032 (USD MILLION)
  • TABLE 61. AMERICAS ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY SERVICES, 2018-2032 (USD MILLION)
  • TABLE 62. AMERICAS ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY IMAGING TECHNOLOGY, 2018-2032 (USD MILLION)
  • TABLE 63. AMERICAS ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 64. AMERICAS ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY END-USER, 2018-2032 (USD MILLION)
  • TABLE 65. NORTH AMERICA ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 66. NORTH AMERICA ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY COMPONENT, 2018-2032 (USD MILLION)
  • TABLE 67. NORTH AMERICA ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY SERVICES, 2018-2032 (USD MILLION)
  • TABLE 68. NORTH AMERICA ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY IMAGING TECHNOLOGY, 2018-2032 (USD MILLION)
  • TABLE 69. NORTH AMERICA ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 70. NORTH AMERICA ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY END-USER, 2018-2032 (USD MILLION)
  • TABLE 71. LATIN AMERICA ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 72. LATIN AMERICA ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY COMPONENT, 2018-2032 (USD MILLION)
  • TABLE 73. LATIN AMERICA ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY SERVICES, 2018-2032 (USD MILLION)
  • TABLE 74. LATIN AMERICA ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY IMAGING TECHNOLOGY, 2018-2032 (USD MILLION)
  • TABLE 75. LATIN AMERICA ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 76. LATIN AMERICA ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY END-USER, 2018-2032 (USD MILLION)
  • TABLE 77. EUROPE, MIDDLE EAST & AFRICA ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY SUBREGION, 2018-2032 (USD MILLION)
  • TABLE 78. EUROPE, MIDDLE EAST & AFRICA ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY COMPONENT, 2018-2032 (USD MILLION)
  • TABLE 79. EUROPE, MIDDLE EAST & AFRICA ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY SERVICES, 2018-2032 (USD MILLION)
  • TABLE 80. EUROPE, MIDDLE EAST & AFRICA ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY IMAGING TECHNOLOGY, 2018-2032 (USD MILLION)
  • TABLE 81. EUROPE, MIDDLE EAST & AFRICA ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 82. EUROPE, MIDDLE EAST & AFRICA ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY END-USER, 2018-2032 (USD MILLION)
  • TABLE 83. EUROPE ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 84. EUROPE ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY COMPONENT, 2018-2032 (USD MILLION)
  • TABLE 85. EUROPE ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY SERVICES, 2018-2032 (USD MILLION)
  • TABLE 86. EUROPE ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY IMAGING TECHNOLOGY, 2018-2032 (USD MILLION)
  • TABLE 87. EUROPE ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 88. EUROPE ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY END-USER, 2018-2032 (USD MILLION)
  • TABLE 89. MIDDLE EAST ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 90. MIDDLE EAST ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY COMPONENT, 2018-2032 (USD MILLION)
  • TABLE 91. MIDDLE EAST ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY SERVICES, 2018-2032 (USD MILLION)
  • TABLE 92. MIDDLE EAST ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY IMAGING TECHNOLOGY, 2018-2032 (USD MILLION)
  • TABLE 93. MIDDLE EAST ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 94. MIDDLE EAST ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY END-USER, 2018-2032 (USD MILLION)
  • TABLE 95. AFRICA ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 96. AFRICA ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY COMPONENT, 2018-2032 (USD MILLION)
  • TABLE 97. AFRICA ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY SERVICES, 2018-2032 (USD MILLION)
  • TABLE 98. AFRICA ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY IMAGING TECHNOLOGY, 2018-2032 (USD MILLION)
  • TABLE 99. AFRICA ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 100. AFRICA ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY END-USER, 2018-2032 (USD MILLION)
  • TABLE 101. ASIA-PACIFIC ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 102. ASIA-PACIFIC ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY COMPONENT, 2018-2032 (USD MILLION)
  • TABLE 103. ASIA-PACIFIC ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY SERVICES, 2018-2032 (USD MILLION)
  • TABLE 104. ASIA-PACIFIC ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY IMAGING TECHNOLOGY, 2018-2032 (USD MILLION)
  • TABLE 105. ASIA-PACIFIC ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 106. ASIA-PACIFIC ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY END-USER, 2018-2032 (USD MILLION)
  • TABLE 107. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 108. ASEAN ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 109. ASEAN ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY COMPONENT, 2018-2032 (USD MILLION)
  • TABLE 110. ASEAN ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY SERVICES, 2018-2032 (USD MILLION)
  • TABLE 111. ASEAN ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY IMAGING TECHNOLOGY, 2018-2032 (USD MILLION)
  • TABLE 112. ASEAN ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 113. ASEAN ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY END-USER, 2018-2032 (USD MILLION)
  • TABLE 114. GCC ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 115. GCC ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY COMPONENT, 2018-2032 (USD MILLION)
  • TABLE 116. GCC ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY SERVICES, 2018-2032 (USD MILLION)
  • TABLE 117. GCC ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY IMAGING TECHNOLOGY, 2018-2032 (USD MILLION)
  • TABLE 118. GCC ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 119. GCC ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY END-USER, 2018-2032 (USD MILLION)
  • TABLE 120. EUROPEAN UNION ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 121. EUROPEAN UNION ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY COMPONENT, 2018-2032 (USD MILLION)
  • TABLE 122. EUROPEAN UNION ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY SERVICES, 2018-2032 (USD MILLION)
  • TABLE 123. EUROPEAN UNION ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY IMAGING TECHNOLOGY, 2018-2032 (USD MILLION)
  • TABLE 124. EUROPEAN UNION ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 125. EUROPEAN UNION ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY END-USER, 2018-2032 (USD MILLION)
  • TABLE 126. BRICS ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 127. BRICS ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY COMPONENT, 2018-2032 (USD MILLION)
  • TABLE 128. BRICS ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY SERVICES, 2018-2032 (USD MILLION)
  • TABLE 129. BRICS ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY IMAGING TECHNOLOGY, 2018-2032 (USD MILLION)
  • TABLE 130. BRICS ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 131. BRICS ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY END-USER, 2018-2032 (USD MILLION)
  • TABLE 132. G7 ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 133. G7 ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY COMPONENT, 2018-2032 (USD MILLION)
  • TABLE 134. G7 ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY SERVICES, 2018-2032 (USD MILLION)
  • TABLE 135. G7 ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY IMAGING TECHNOLOGY, 2018-2032 (USD MILLION)
  • TABLE 136. G7 ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 137. G7 ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY END-USER, 2018-2032 (USD MILLION)
  • TABLE 138. NATO ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 139. NATO ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY COMPONENT, 2018-2032 (USD MILLION)
  • TABLE 140. NATO ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY SERVICES, 2018-2032 (USD MILLION)
  • TABLE 141. NATO ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY IMAGING TECHNOLOGY, 2018-2032 (USD MILLION)
  • TABLE 142. NATO ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 143. NATO ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY END-USER, 2018-2032 (USD MILLION)
  • TABLE 144. GLOBAL ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 145. UNITED STATES ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, 2018-2032 (USD MILLION)
  • TABLE 146. UNITED STATES ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY COMPONENT, 2018-2032 (USD MILLION)
  • TABLE 147. UNITED STATES ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY SERVICES, 2018-2032 (USD MILLION)
  • TABLE 148. UNITED STATES ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY IMAGING TECHNOLOGY, 2018-2032 (USD MILLION)
  • TABLE 149. UNITED STATES ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 150. UNITED STATES ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY END-USER, 2018-2032 (USD MILLION)
  • TABLE 151. CHINA ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, 2018-2032 (USD MILLION)
  • TABLE 152. CHINA ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY COMPONENT, 2018-2032 (USD MILLION)
  • TABLE 153. CHINA ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY SERVICES, 2018-2032 (USD MILLION)
  • TABLE 154. CHINA ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY IMAGING TECHNOLOGY, 2018-2032 (USD MILLION)
  • TABLE 155. CHINA ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 156. CHINA ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING MARKET SIZE, BY END-USER, 2018-2032 (USD MILLION)