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市场调查报告书
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2021742

2034年放射学人工智慧市场预测：按组件、技术、部署模式、成像方法、应用、最终用户和地区分類的全球分析

AI in Radiology Market Forecasts to 2034 - Global Analysis By Component (Software, Hardware, and Services), Technology, Deployment Mode, Imaging Modality, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的数据，预计到 2026 年，全球放射学人工智慧市场规模将达到 6 亿美元，到 2034 年将达到 32 亿美元，预测期内复合年增长率为 23.4%。

放射学中的人工智慧是指应用包括机器学习和深度学习在内的先进人工智慧技术，来辅助医学影像资料的分析、解读和管理。这能够实现自动异常检测、提升影像品质、优化工作流程，并为临床决策提供支援。透过处理大量从CT、MRI和X光等影像设备取得的影像数据，人工智慧能够帮助放射科医师提高诊断准确率、缩短解读时间，并透过更快速、更准确的医学影像分析来改善患者预后。

医学影像检查的增加和放射科医生的短缺

医学影像数量的指数级增长，加上全球放射科医生短缺，使得人工智慧驱动的工作流程解决方案的需求日益迫切。人工智慧演算法在严重病例分诊方面表现出色，能够帮助放射科医师优先处理颅内出血和肺动脉栓塞等危及生命的疾病。此外，精准医疗的日益普及也推动了对人工智慧所能提供的先进影像生物标记和定量分析的需求。人工智慧在缩短检测结果报告时间和提高诊断一致性方面已得到证实，这促使医疗机构将这些工具整合到标准诊疗流程中，从而进一步扩大了市场。

高昂的实施成本和互通性挑战

将人工智慧整合到临床放射学工作流程中面临许多挑战，包括高昂的实施成本以及确保与现有PACS和EHR系统互通性的必要性。资料隐私、网路安全以及与演算法偏差相关的伦理问题也构成重大挑战。此外，缺乏针对人工智慧医疗软体的标准化法规结构和报销模式，为开发商和实施机构带来了财务不确定性。临床检验和能够证明改善患者预后的积极证据仍然是广泛应用的主要障碍。

价值医疗和个人化医疗的进展

向价值医疗模式的转变，为放射学领域的人工智慧提供了展现其在降低成本和改善患者疗效方面影响的绝佳机会。人工智慧驱动的解决方案能够自动完成测量和记录等常规任务，使放射科医生能够专注于复杂病例和直接的患者互动。整合影像资料、基因组资讯和电子健康记录的多模态人工智慧模型的开发，有望在个人化医疗领域带来突破性进展。新兴市场也蓄势待发，因为可扩展的云端人工智慧解决方案有望克服传统基础设施的限制。

技术过时及网路安全风险

人工智慧领域的快速技术进步对现有软体解决方案构成了过时的威胁，因此需要持续的研发投入才能保持竞争力。过度依赖人工智慧而缺乏充分的人工监督可能导致误诊和法律责任问题，从而削弱人们对该技术的信心。此外，市场整合的加速趋势可能会限制竞争和创新。针对互联医疗设备和人工智慧系统的网路安全威胁也会对病患资料的完整性和医院运作构成风险，因此强有力的保护措施至关重要。

新冠疫情的影响：

新冠疫情加速了人工智慧在放射学领域的应用，因为医疗系统面临前所未有的胸部CT和X光影像量。人工智慧工具被迅速部署，用于检测和量化病毒相关的肺部异常，从而减轻了本已不堪重负的放射科医生的负担。这场危机加速了监管核准流程，促使基于人工智慧的诊断工具获得了紧急使用授权。它也凸显了远端和云端解决方案的需求，从根本上推动了市场向数位转型和分散式诊断工作流程发展。

在预测期内，软体领域预计将占据最大份额。

软体领域预计将占据最大的市场份额，这主要得益于演算法在影像分析、诊断支援和工作流程自动化中发挥的基础性作用。这些软体解决方案对于将原始影像资料转化为可操作的临床见解至关重要。用于病灶检测和器官分割等任务的先进深度学习模型的持续发展，进一步巩固了这一市场主导地位。由于医院希望在不进行重大硬体升级的情况下提高放射科医生的工作效率和诊断准确性，因此对复杂且整合化的软体平台的需求仍然非常旺盛。

在预测期内，基于云端的细分市场预计将呈现最高的复合年增长率。

在预测期内，由于其扩充性、成本效益和促进远端协作的能力，基于云端的采用领域预计将呈现最高的成长率。云端平台无需大规模的本地IT基础设施即可实现无缝更新、集中式资料管理和运算能力部署。这种模式对新兴地区寻求快速数位转型的中小型影像中心和医院尤其具有吸引力。远距放射诊断的兴起以及对可在多个站点使用的AI工具日益增长的需求，进一步加速了基于云端的解决方案的采用。

市占率最大的地区：

在整个预测期内，北美预计将保持最大的市场份额，这得益于其先进的医疗资讯IT基础设施、众多领先的人工智慧开发公司的强大影响力以及有利的报销环境。尤其值得一提的是，美国在大型医院网路和诊断影像中心采用人工智慧工具方面发挥着主导作用。大量的研发投入、拥有FDA核准的竞争环境以及对重视效率和准确性的价值医疗模式的高度重视，都巩固了该地区的领先地位。

复合年增长率最高的地区：

在预测期内，亚太地区预计将呈现最高的复合年增长率，这主要得益于医疗基础设施的快速扩张和医学影像诊断的日益普及。中国、印度和日本等国家正大力投资数位倡议和人工智慧研究。该地区庞大的人口基数、慢性病盛行率的上升以及放射科医生短缺问题的日益严峻，都推动了市场需求。政府对人工智慧应用的支援以及医疗设备产业的快速发展，为市场的快速扩张创造了有利条件。

免费客製化服务：

所有购买此报告的客户均可享受以下免费自订选项之一：

  • 企业概况
    • 对其他市场参与者（最多 3 家公司）进行全面分析
    • 对主要企业进行SWOT分析（最多3家公司）
  • 区域细分
    • 应客户要求，我们提供主要国家和地区的市场估算和预测，以及复合年增长率（註：需进行可行性检查）。
  • 竞争性标竿分析
    • 根据产品系列、地理覆盖范围和策略联盟对主要企业进行基准分析。

目录

第一章执行摘要

  • 市场概览及主要亮点
  • 驱动因素、挑战与机会
  • 竞争格局概述
  • 战略洞察与建议

第二章：研究框架

  • 研究目标和范围
  • 相关人员分析
  • 研究假设和限制
  • 调查方法

第三章 市场动态与趋势分析

  • 市场定义与结构
  • 主要市场驱动因素
  • 市场限制与挑战
  • 投资成长机会和重点领域
  • 产业威胁与风险评估
  • 技术与创新展望
  • 新兴市场/高成长市场
  • 监管和政策环境
  • 新冠疫情的影响及復苏前景

第四章：竞争环境与策略评估

  • 波特五力分析
    • 供应商的议价能力
    • 买方的议价能力
    • 替代品的威胁
    • 新进入者的威胁
    • 竞争公司之间的竞争
  • 主要企业市占率分析
  • 产品基准评效和效能比较

第五章：全球放射线人工智慧市场：按组件划分

  • 软体
    • 影像分析软体
    • 诊断支援软体
    • 工作流程自动化软体
  • 硬体
    • 人工智慧驱动的医学影像系统
    • 边缘运算设备
  • 服务
    • 整合与部署
    • 培训和咨询
    • 维护和支援

第六章：全球放射线人工智慧市场：按技术划分

  • 机器学习
  • 深度学习
  • 自然语言处理（NLP）
  • 电脑视觉
  • 情境感知计算

第七章：全球放射线人工智慧市场：依部署模式划分

  • 现场
  • 基于云端的
  • 混合实现

第八章：全球放射线人工智慧市场：依影像方法划分

  • X射线
  • 电脑断层扫描（CT）
  • 磁振造影（MRI）
  • 超音波
  • 乳房X光检查
  • 正子断层扫描（PET）

第九章：全球放射学人工智慧市场：按应用领域划分

  • 检测和诊断
  • 影像分割与量化
  • 工作流程优化和分诊
  • 预测和预后分析
  • 治疗方案
  • 监测和后续观察
  • 其他用途

第十章：全球放射学人工智慧市场：按最终用户划分

  • 医院和诊所
  • 诊断影像中心
  • 门诊手术中心
  • 学术研究机构
  • 其他最终用户

第十一章：全球放射线人工智慧市场：按地区划分

  • 北美洲
    • 我们
    • 加拿大
    • 墨西哥
  • 欧洲
    • 英国
    • 德国
    • 法国
    • 义大利
    • 西班牙
    • 荷兰
    • 比利时
    • 瑞典
    • 瑞士
    • 波兰
    • 其他欧洲国家
  • 亚太地区
    • 中国
    • 日本
    • 印度
    • 韩国
    • 澳洲
    • 印尼
    • 泰国
    • 马来西亚
    • 新加坡
    • 越南
    • 其他亚太国家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥伦比亚
    • 智利
    • 秘鲁
    • 其他南美国家
  • 世界其他地区（RoW）
    • 中东
      • 沙乌地阿拉伯
      • 阿拉伯聯合大公国
      • 卡达
      • 以色列
      • 其他中东国家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲国家

第十二章 策略市场资讯

  • 工业价值网络和供应链评估
  • 空白区域和机会地图
  • 产品演进与市场生命週期分析
  • 通路、经销商和打入市场策略的评估

第十三章 产业趋势与策略倡议

  • 併购
  • 伙伴关係、联盟和合资企业
  • 新产品发布和认证
  • 扩大生产能力和投资
  • 其他策略倡议

第十四章：公司简介

  • Siemens Healthineers
  • GE HealthCare
  • Philips Healthcare
  • Canon Medical Systems
  • IBM
  • NVIDIA
  • Aidoc
  • Arterys
  • Viz.ai
  • Qure.ai
  • Enlitic
  • Lunit
  • Zebra Medical Vision
  • iCAD
  • Infervision
Product Code: SMRC35004

According to Stratistics MRC, the Global AI in Radiology Market is accounted for $0.6 billion in 2026 and is expected to reach $3.2 billion by 2034, growing at a CAGR of 23.4% during the forecast period. AI in Radiology is the application of advanced artificial intelligence technologies, including machine learning and deep learning, to support the analysis, interpretation, and management of medical imaging data. It enables automated identification of abnormalities, image enhancement, workflow optimization, and clinical decision support. By processing large volumes of imaging data from modalities such as CT, MRI, and X-rays, AI helps radiologists improve diagnostic accuracy, shorten interpretation time, and enhance patient outcomes through faster and more precise medical imaging insights.

Market Dynamics:

Driver:

Rising medical imaging volumes and radiologist shortages

The exponential growth in medical imaging volumes, coupled with a global shortage of radiologists, is creating an urgent need for AI-powered workflow solutions. AI algorithms excel at triaging critical cases, allowing radiologists to prioritize life-threatening conditions like intracranial hemorrhages or pulmonary embolisms. Furthermore, the push for precision medicine is driving demand for advanced imaging biomarkers and quantitative analysis that AI can provide. The proven ability of AI to reduce turnaround times and improve diagnostic consistency is compelling healthcare providers to integrate these tools into their standard practice, fueling market expansion.

Restraint:

High implementation costs and interoperability challenges

The integration of AI into clinical radiology workflows faces significant hurdles due to high implementation costs and the need for seamless interoperability with existing PACS and EHR systems. Concerns regarding data privacy, cybersecurity, and the ethical implications of algorithmic bias also pose substantial challenges. Furthermore, the lack of standardized regulatory frameworks and reimbursement models for AI-based medical software creates financial uncertainty for developers and adopters. Clinical validation and the need for prospective evidence demonstrating improved patient outcomes remain critical barriers to widespread adoption.

Opportunity:

Value-based care and personalized medicine advancements

The shift toward value-based care presents a significant opportunity for AI in radiology to demonstrate its impact on cost reduction and patient outcomes. AI-driven solutions that automate routine tasks, such as measurement and documentation, free up radiologists to focus on complex cases and direct patient interaction. The development of multimodal AI models that integrate imaging data with genomics and electronic health records offers the potential for groundbreaking advancements in personalized medicine. Emerging markets are also primed for adoption, as they seek to leapfrog traditional infrastructure limitations with scalable, cloud-based AI solutions.

Threat:

Technological obsolescence and cybersecurity risks

The rapid pace of technological advancement in AI poses a threat of obsolescence for established software solutions, requiring continuous R&D investment to remain competitive. An over-reliance on AI without adequate human oversight could lead to diagnostic errors or liability issues, eroding trust in the technology. Additionally, the market is witnessing increasing consolidation, which could limit competition and innovation. Cybersecurity threats targeting interconnected medical devices and AI systems also pose a risk to patient data integrity and hospital operations, necessitating robust protective measures.

Covid-19 Impact:

The COVID-19 pandemic acted as a catalyst for AI adoption in radiology, as healthcare systems faced unprecedented imaging volumes for chest CTs and X-rays. AI tools were rapidly deployed to assist in the detection and quantification of lung abnormalities associated with the virus, alleviating the burden on overstretched radiologists. The crisis accelerated regulatory approvals, with agencies issuing emergency use authorizations for AI-based diagnostic tools. It also highlighted the necessity of remote, cloud-based solutions, fundamentally shifting the market toward digital transformation and decentralized diagnostic workflows.

The software segment is expected to be the largest during the forecast period

The software segment is anticipated to account for the largest market share, driven by the foundational role of algorithms in image analysis, diagnostic support, and workflow automation. These software solutions are essential for converting raw imaging data into actionable clinical insights. The continuous development of sophisticated deep learning models for tasks like lesion detection and organ segmentation is fueling this dominance. As hospitals seek to enhance radiologist efficiency and diagnostic accuracy without significant hardware overhauls, the demand for advanced, integrable software platforms remains exceptionally high.

The cloud-based segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the cloud-based deployment segment is predicted to witness the highest growth rate, attributed to its scalability, cost-effectiveness, and ability to facilitate remote collaboration. Cloud platforms enable seamless updates, centralized data management, and the deployment of computational power without substantial on-site IT infrastructure. This model is particularly attractive for smaller imaging centers and hospitals in emerging regions seeking rapid digital transformation. The shift toward teleradiology and the need for accessible AI tools across multiple facilities are further accelerating the adoption of cloud-based solutions.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, driven by its advanced healthcare IT infrastructure, strong presence of key AI developers, and favorable reimbursement landscape. The United States, in particular, leads in the adoption of AI tools across major hospital networks and imaging centers. High R&D investment, a competitive regulatory environment with FDA clearances, and a strong focus on value-based care models that reward efficiency and accuracy collectively solidify the region's dominant position.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, supported by rapidly expanding healthcare infrastructure and increasing medical imaging volumes. Countries like China, India, and Japan are investing heavily in digital health initiatives and AI research. The region's large population base, rising prevalence of chronic diseases, and a growing need to address radiologist shortages are driving demand. Government support for AI integration and a burgeoning medical device sector are creating a fertile ground for rapid market expansion.

Key players in the market

Some of the key players in AI in Radiology Market include Siemens Healthineers, GE HealthCare, Philips Healthcare, Canon Medical Systems, IBM, NVIDIA, Aidoc, Arterys, Viz.ai, Qure.ai, Enlitic, Lunit, Zebra Medical Vision, iCAD, and Infervision.

Key Developments:

In March 2026, IBM and ETH Zurich announced a 10-year collaboration to advance the next generation of algorithms at the intersection of AI and quantum computing. This initiative represents the latest milestone in the long-standing collaboration between the two institutions, further strengthening a scientific exchange that has helped create the future of information technology.

In March 2026, NVIDIA and Marvell Technology, Inc. announced a strategic partnership to connect Marvell to the NVIDIA AI factory and AI-RAN ecosystem through NVIDIA NVLink Fusion(TM), offering customers building on NVIDIA architectures greater choice and flexibility in developing next-generation infrastructure. The companies will also collaborate on silicon photonics technology.

Components Covered:

  • Software
  • Hardware
  • Services

Technologies Covered:

  • Machine Learning
  • Deep Learning
  • Natural Language Processing (NLP)
  • Computer Vision
  • Context-Aware Computing

Deployment Modes Covered:

  • On-Premises
  • Cloud-Based
  • Hybrid Deployment

Imaging Modalities Covered:

  • X-Ray
  • Computed Tomography (CT)
  • Magnetic Resonance Imaging (MRI)
  • Ultrasound
  • Mammography
  • Positron Emission Tomography (PET)

Applications Covered:

  • Detection & Diagnosis
  • Image Segmentation & Quantification
  • Workflow Optimization & Triage
  • Predictive & Prognostic Analytics
  • Treatment Planning
  • Monitoring & Follow-Up
  • Other Applications

End Users Covered:

  • Hospitals & Clinics
  • Diagnostic Imaging Centers
  • Ambulatory Surgical Centers
  • Academic & Research Institutes
  • Other End Users

Regions Covered:

  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Spain
    • Netherlands
    • Belgium
    • Sweden
    • Switzerland
    • Poland
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Australia
    • Indonesia
    • Thailand
    • Malaysia
    • Singapore
    • Vietnam
    • Rest of Asia Pacific
  • South America
    • Brazil
    • Argentina
    • Colombia
    • Chile
    • Peru
    • Rest of South America
  • Rest of the World (RoW)
    • Middle East
  • Saudi Arabia
  • United Arab Emirates
  • Qatar
  • Israel
  • Rest of Middle East
    • Africa
  • South Africa
  • Egypt
  • Morocco
  • Rest of Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2023, 2024, 2025, 2026, 2027, 2028, 2030, 2032 and 2034
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

  • 1.1 Market Snapshot and Key Highlights
  • 1.2 Growth Drivers, Challenges, and Opportunities
  • 1.3 Competitive Landscape Overview
  • 1.4 Strategic Insights and Recommendations

2 Research Framework

  • 2.1 Study Objectives and Scope
  • 2.2 Stakeholder Analysis
  • 2.3 Research Assumptions and Limitations
  • 2.4 Research Methodology
    • 2.4.1 Data Collection (Primary and Secondary)
    • 2.4.2 Data Modeling and Estimation Techniques
    • 2.4.3 Data Validation and Triangulation
    • 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

  • 3.1 Market Definition and Structure
  • 3.2 Key Market Drivers
  • 3.3 Market Restraints and Challenges
  • 3.4 Growth Opportunities and Investment Hotspots
  • 3.5 Industry Threats and Risk Assessment
  • 3.6 Technology and Innovation Landscape
  • 3.7 Emerging and High-Growth Markets
  • 3.8 Regulatory and Policy Environment
  • 3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

  • 4.1 Porter's Five Forces Analysis
    • 4.1.1 Supplier Bargaining Power
    • 4.1.2 Buyer Bargaining Power
    • 4.1.3 Threat of Substitutes
    • 4.1.4 Threat of New Entrants
    • 4.1.5 Competitive Rivalry
  • 4.2 Market Share Analysis of Key Players
  • 4.3 Product Benchmarking and Performance Comparison

5 Global AI in Radiology Market, By Component

  • 5.1 Software
    • 5.1.1 Image Analysis Software
    • 5.1.2 Diagnostic Support Software
    • 5.1.3 Workflow Automation Software
  • 5.2 Hardware
    • 5.2.1 AI-Enabled Imaging Systems
    • 5.2.2 Edge Computing Devices
  • 5.3 Services
    • 5.3.1 Integration & Deployment
    • 5.3.2 Training & Consulting
    • 5.3.3 Maintenance & Support

6 Global AI in Radiology Market, By Technology

  • 6.1 Machine Learning
  • 6.2 Deep Learning
  • 6.3 Natural Language Processing (NLP)
  • 6.4 Computer Vision
  • 6.5 Context-Aware Computing

7 Global AI in Radiology Market, By Deployment Mode

  • 7.1 On-Premises
  • 7.2 Cloud-Based
  • 7.3 Hybrid Deployment

8 Global AI in Radiology Market, By Imaging Modality

  • 8.1 X-Ray
  • 8.2 Computed Tomography (CT)
  • 8.3 Magnetic Resonance Imaging (MRI)
  • 8.4 Ultrasound
  • 8.5 Mammography
  • 8.6 Positron Emission Tomography (PET)

9 Global AI in Radiology Market, By Application

  • 9.1 Detection & Diagnosis
  • 9.2 Image Segmentation & Quantification
  • 9.3 Workflow Optimization & Triage
  • 9.4 Predictive & Prognostic Analytics
  • 9.5 Treatment Planning
  • 9.6 Monitoring & Follow-Up
  • 9.7 Other Applications

10 Global AI in Radiology Market, By End User

  • 10.1 Hospitals & Clinics
  • 10.2 Diagnostic Imaging Centers
  • 10.3 Ambulatory Surgical Centers
  • 10.4 Academic & Research Institutes
  • 10.5 Other End Users

11 Global AI in Radiology Market, By Geography

  • 11.1 North America
    • 11.1.1 United States
    • 11.1.2 Canada
    • 11.1.3 Mexico
  • 11.2 Europe
    • 11.2.1 United Kingdom
    • 11.2.2 Germany
    • 11.2.3 France
    • 11.2.4 Italy
    • 11.2.5 Spain
    • 11.2.6 Netherlands
    • 11.2.7 Belgium
    • 11.2.8 Sweden
    • 11.2.9 Switzerland
    • 11.2.10 Poland
    • 11.2.11 Rest of Europe
  • 11.3 Asia Pacific
    • 11.3.1 China
    • 11.3.2 Japan
    • 11.3.3 India
    • 11.3.4 South Korea
    • 11.3.5 Australia
    • 11.3.6 Indonesia
    • 11.3.7 Thailand
    • 11.3.8 Malaysia
    • 11.3.9 Singapore
    • 11.3.10 Vietnam
    • 11.3.11 Rest of Asia Pacific
  • 11.4 South America
    • 11.4.1 Brazil
    • 11.4.2 Argentina
    • 11.4.3 Colombia
    • 11.4.4 Chile
    • 11.4.5 Peru
    • 11.4.6 Rest of South America
  • 11.5 Rest of the World (RoW)
    • 11.5.1 Middle East
      • 11.5.1.1 Saudi Arabia
      • 11.5.1.2 United Arab Emirates
      • 11.5.1.3 Qatar
      • 11.5.1.4 Israel
      • 11.5.1.5 Rest of Middle East
    • 11.5.2 Africa
      • 11.5.2.1 South Africa
      • 11.5.2.2 Egypt
      • 11.5.2.3 Morocco
      • 11.5.2.4 Rest of Africa

12 Strategic Market Intelligence

  • 12.1 Industry Value Network and Supply Chain Assessment
  • 12.2 White-Space and Opportunity Mapping
  • 12.3 Product Evolution and Market Life Cycle Analysis
  • 12.4 Channel, Distributor, and Go-to-Market Assessment

13 Industry Developments and Strategic Initiatives

  • 13.1 Mergers and Acquisitions
  • 13.2 Partnerships, Alliances, and Joint Ventures
  • 13.3 New Product Launches and Certifications
  • 13.4 Capacity Expansion and Investments
  • 13.5 Other Strategic Initiatives

14 Company Profiles

  • 14.1 Siemens Healthineers
  • 14.2 GE HealthCare
  • 14.3 Philips Healthcare
  • 14.4 Canon Medical Systems
  • 14.5 IBM
  • 14.6 NVIDIA
  • 14.7 Aidoc
  • 14.8 Arterys
  • 14.9 Viz.ai
  • 14.10 Qure.ai
  • 14.11 Enlitic
  • 14.12 Lunit
  • 14.13 Zebra Medical Vision
  • 14.14 iCAD
  • 14.15 Infervision

List of Tables

  • Table 1 Global AI in Radiology Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI in Radiology Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global AI in Radiology Market Outlook, By Software (2023-2034) ($MN)
  • Table 4 Global AI in Radiology Market Outlook, By Image Analysis Software (2023-2034) ($MN)
  • Table 5 Global AI in Radiology Market Outlook, By Diagnostic Support Software (2023-2034) ($MN)
  • Table 6 Global AI in Radiology Market Outlook, By Workflow Automation Software (2023-2034) ($MN)
  • Table 7 Global AI in Radiology Market Outlook, By Hardware (2023-2034) ($MN)
  • Table 8 Global AI in Radiology Market Outlook, By AI-Enabled Imaging Systems (2023-2034) ($MN)
  • Table 9 Global AI in Radiology Market Outlook, By Edge Computing Devices (2023-2034) ($MN)
  • Table 10 Global AI in Radiology Market Outlook, By Services (2023-2034) ($MN)
  • Table 11 Global AI in Radiology Market Outlook, By Integration & Deployment (2023-2034) ($MN)
  • Table 12 Global AI in Radiology Market Outlook, By Training & Consulting (2023-2034) ($MN)
  • Table 13 Global AI in Radiology Market Outlook, By Maintenance & Support (2023-2034) ($MN)
  • Table 14 Global AI in Radiology Market Outlook, By Technology (2023-2034) ($MN)
  • Table 15 Global AI in Radiology Market Outlook, By Machine Learning (2023-2034) ($MN)
  • Table 16 Global AI in Radiology Market Outlook, By Deep Learning (2023-2034) ($MN)
  • Table 17 Global AI in Radiology Market Outlook, By Natural Language Processing (NLP) (2023-2034) ($MN)
  • Table 18 Global AI in Radiology Market Outlook, By Computer Vision (2023-2034) ($MN)
  • Table 19 Global AI in Radiology Market Outlook, By Context-Aware Computing (2023-2034) ($MN)
  • Table 20 Global AI in Radiology Market Outlook, By Deployment Mode (2023-2034) ($MN)
  • Table 21 Global AI in Radiology Market Outlook, By On-Premises (2023-2034) ($MN)
  • Table 22 Global AI in Radiology Market Outlook, By Cloud-Based (2023-2034) ($MN)
  • Table 23 Global AI in Radiology Market Outlook, By Hybrid Deployment (2023-2034) ($MN)
  • Table 24 Global AI in Radiology Market Outlook, By Imaging Modality (2023-2034) ($MN)
  • Table 25 Global AI in Radiology Market Outlook, By X-Ray (2023-2034) ($MN)
  • Table 26 Global AI in Radiology Market Outlook, By Computed Tomography (CT) (2023-2034) ($MN)
  • Table 27 Global AI in Radiology Market Outlook, By Magnetic Resonance Imaging (MRI) (2023-2034) ($MN)
  • Table 28 Global AI in Radiology Market Outlook, By Ultrasound (2023-2034) ($MN)
  • Table 29 Global AI in Radiology Market Outlook, By Mammography (2023-2034) ($MN)
  • Table 30 Global AI in Radiology Market Outlook, By Positron Emission Tomography (PET) (2023-2034) ($MN)
  • Table 31 Global AI in Radiology Market Outlook, By Application (2023-2034) ($MN)
  • Table 32 Global AI in Radiology Market Outlook, By Detection & Diagnosis (2023-2034) ($MN)
  • Table 33 Global AI in Radiology Market Outlook, By Image Segmentation & Quantification (2023-2034) ($MN)
  • Table 34 Global AI in Radiology Market Outlook, By Workflow Optimization & Triage (2023-2034) ($MN)
  • Table 35 Global AI in Radiology Market Outlook, By Predictive & Prognostic Analytics (2023-2034) ($MN)
  • Table 36 Global AI in Radiology Market Outlook, By Treatment Planning (2023-2034) ($MN)
  • Table 37 Global AI in Radiology Market Outlook, By Monitoring & Follow-Up (2023-2034) ($MN)
  • Table 38 Global AI in Radiology Market Outlook, By Other Applications (2023-2034) ($MN)
  • Table 39 Global AI in Radiology Market Outlook, By End User (2023-2034) ($MN)
  • Table 40 Global AI in Radiology Market Outlook, By Hospitals & Clinics (2023-2034) ($MN)
  • Table 41 Global AI in Radiology Market Outlook, By Diagnostic Imaging Centers (2023-2034) ($MN)
  • Table 42 Global AI in Radiology Market Outlook, By Ambulatory Surgical Centers (2023-2034) ($MN)
  • Table 43 Global AI in Radiology Market Outlook, By Academic & Research Institutes (2023-2034) ($MN)
  • Table 44 Global AI in Radiology Market Outlook, By Other End Users (2023-2034) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Rest of the World (RoW) are also represented in the same manner as above.