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人工智慧在医学影像领域的市场：产业趋势及全球预测（至2030年）－按应用、影像类型和地区划分

AI in Medical Imaging Market, till 2030: Distribution by Application Area, Type of Image Processed, and Key Geographical Regions: Industry Trends and Global Forecasts
出版日期: | 出版商: Roots Analysis | 英文 389 Pages | 商品交期: 最快1-2个工作天内

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简介目录

人工智慧在医学影像领域的市场展望

全球人工智慧在医学影像领域的市场规模预计将从目前的17.5亿美元增长至2040年的85.6亿美元，在预测期内（至2040年）的复合年增长率预计为30%。本研究对人工智慧在医学影像领域的市场进行了全面分析，内容涵盖市场分析、产品线分析、合作伙伴关係现状、融资和投资趋势分析、公司估值分析、专利分析、成本降低分析以及详细的市场分析。

未来十年，人工智慧在医学影像领域的市场成长预计将受到以下因素的推动：人工智慧（AI）技术（尤其是深度学习演算法）的日益普及、对个人化和精准医疗的日益关注、目标人群未被满足的需求以及风险投资的支持。 深度学习是一种机器学习技术，它利用先进的演算法和人工神经网络，从大量非结构化资料中实现无监督的模式识别和洞察生成。这项技术正逐步融入医疗保健产业的各个领域，包括诊断影像和基于数据分析的医学诊断。

众多产业利害关係人已开发出各自的医学影像处理深度学习演算法。许多创新公司声称已开发出能够训练电脑检查医学影像并对其进行优先排序的系统，从而识别出人眼可能无法察觉的与时间和空间变化相关的模式。

AI in Medical Imaging Market-IMG1

高阶主管的策略洞察

推动AI在医学影像市场成长的关键因素

推动AI在医学影像市场成长的关键因素包括诊断程序的增加、合格放射科医师的短缺、AI在远距医疗和远距诊断中日益广泛的应用，以及高效管理海量医疗资料集的需求。深度学习、卷积神经网路和生成对抗网路等技术创新正在提高AI在医学影像分析中的应用准确性和有效性。此外，政府的支持、有利的监管环境以及医院与AI解决方案提供者之间的合作正在加速AI的普及应用。

另一个关键成长因素是人工智慧在各种医学影像技术中的应用不断扩展，包括乳房X光检查、超音波检查、磁振造影和病理学检查。人工智慧有助于降低影像杂讯、减少扫描过程中的辐射暴露，并改善临床决策。

人工智慧在医学影像产业的技术进步

人工智慧 (AI) 的技术进步正在提高医学影像诊断的准确性、效率和个人化程度。人工智慧驱动的演算法现在可以准确地检测和识别各种影像技术（包括 CT、MRI 和 X 光）中的异常情况，例如肿瘤和骨折。这些系统接管了常规任务，优化了工作流程，减轻了放射科医生的疲劳，使临床医生能够专注于更复杂的病例。此外，人工智慧将影像资讯与临床和基因组数据结合，为疾病风险评估提供个人化见解和预测分析。生成式人工智慧等创新技术正在扩展影像资料集，而即时人工智慧工具正在辅助手术导航并提高手术精度。

此外，可解释人工智慧和联邦学习正在提升临床环境的透明度和可扩展性。这些进步共同推动医学影像的变革，实现更早、更精准的诊断，并最终改善全球病患的治疗效果。

医学影像人工智慧市场：竞争格局

医学影像人工智慧市场的竞争格局十分激烈，大型企业和小型企业并存。领先的一级企业（GE医疗、西门子医疗、飞利浦、佳能医疗）透过将整合人工智慧生态系统融入其影像系统来保持优势。二级企业（Aidoc、Arterys、Qure.ai、Gleamer、Viz.ai）则以专为特定疾病应用设计的敏捷云端原生演算法颠覆市场。未来的竞争优势将取决于模型的可解释性、互通性和临床验证程度等因素。 与人工智慧市场、开放式人工智慧平台和厂商中立的整合框架相关的新兴趋势预计将进一步重塑竞争格局。

人工智慧在医学影像领域的演进－新兴产业趋势

产业关键趋势包括：监管审批速度加快，从而推动更广泛的临床应用；利用人工智慧根据患者资讯优化扫描方案；以及采用人工智慧工具优化工作流程，尤其是在放射学领域，人工智慧正越来越多地承担常规诊断工作。人工智慧辅助超音波、即时术中影像评估和云端解决方案等创新发展正在改善人们获得先进诊断成像服务的机会，即使在农村和资源匮乏的地区也是如此。 这些进步正将诊断成像从定性领域转向数据驱动领域，以患者为中心，提高准确性、效率和医疗公平性。

主要市场挑战

在医学影像领域采用人工智慧的主要挑战包括高昂的实施和持续维护成本，以及对敏感病患资料隐私和安全的担忧。此外，与新技术相关的监管挑战、与现有系统无缝整合的需求以及医疗专业人员的接受度也阻碍了其发展。

其他主要挑战包括缺乏与现有工作流程的整合、医疗数据分散以及孤立的IT系统，这些都阻碍了人工智慧解决方案的无缝应用。此外，有关资料隐私、人工智慧模型中的偏见以及问责制的伦理和监管问题也是临床应用的障碍。 此外，合格的人工智慧人才匮乏以及监管指导不明确，进一步阻碍了人工智慧技术在日常临床实践中的应用和整合。

区域分析－亚洲占最大市场占有率

据我们估计，北美目前在人工智慧医疗影像市场占显着占有率。这得归功于其先进的医疗保健体系、对医疗技术的大力财政支持以及活跃的研发活动，尤其是在美国。众多顶尖科技公司和创新新创企业的存在，促进了尖端人工智慧应用的发展和应用。

此外，北美监管环境的特点是FDA审批流程严格，并提供报销奖励措施，这也有利于人工智慧解决方案的推广应用。该地区慢性病盛行率较高，推动了对透过人工智慧增强影像技术进行早期精准诊断的需求。

人工智慧在医学影像市场的应用：主要市场区隔

应用领域

  • 肺部感染/呼吸系统疾病
  • 脑损伤/脑部疾病
  • 肺癌
  • 心臟病/心血管疾病
  • 骨骼畸形/骨科疾病
  • 乳癌
  • 其他

处理后的影像类型

  • X射线
  • 磁振造影 (MRI)
  • 电脑断层扫描 (CT)
  • 超音波

地理区域

  • 北美
  • 美国
  • 加拿大
  • 墨西哥
  • 其他北美地区国家/地区
  • 欧洲
  • 奥地利
  • 比利时
  • 丹麦
  • 法国
  • 德国
  • 爱尔兰
  • 义大利
  • 荷兰
  • 挪威
  • 俄罗斯
  • 西班牙
  • 瑞典
  • 瑞士
  • 英国
  • 其他欧洲国家
  • 亚洲
  • 中国
  • 印度
  • 日本
  • 新加坡
  • 韩国
  • 其他亚洲国家
  • 拉丁美洲
  • 巴西
  • 智利
  • 哥伦比亚
  • 委内瑞拉
  • 其他拉丁美洲国家
  • 中东和北非非洲
  • 埃及
  • 伊朗
  • 伊拉克
  • 以色列
  • 科威特
  • 沙乌地阿拉伯
  • 阿拉伯联合大公国
  • 其他中东和北非国家
  • 世界其他地区
  • 澳大利亚
  • 纽西兰
  • 其他国家

医学影像人工智慧市场代表性参与者

  • Artelus
  • Arterys
  • Butterfly Network
  • ContextVision
  • Enlitic
  • Echonous
  • GE Healthcare
  • InferVision
  • VUNO

医学影像人工智慧市场：报告内容

本报告深入分析了医学影像人工智慧市场的各个面向。 章节：

  • 市场规模与机会分析：对医学影像人工智慧市场进行详细分析，重点关注关键市场细分（[A] 应用领域，[B] 处理影像类型，以及 [C] 主要地区）。
  • 竞争格局：基于多个相关参数（包括成立年份、公司规模、总部所在地和所有权结构），对医学影像人工智慧市场中的公司进行全面分析。
  • 公司简介：详细介绍医学影像人工智慧市场主要公司的概况。报告涵盖：[A] 总部所在地，[B] 公司规模，[C] 企业理念，[D] 业务区域，[E] 管理团队，[F] 联繫方式，[G] 财务信息，[H] 业务板块，[I] 产品组合，以及 [J] 近期发展和未来展望。
  • 宏观趋势：评估医学影像人工智慧产业的当前宏观趋势。
  • 专利分析：基于相关参数（例如[A]专利类型、[B]专利公开年份、[C]专利年龄和[D]主要参与者）对已提交和已授权的医学影像人工智慧相关专利进行深入分析。
  • 近期发展：概述医学影像人工智慧市场近期发展情况，并基于相关参数（例如[A]计画启动年份、[B]计画类型、[C]地理分布和[D]主要参与者）进行分析。
  • 波特五力分析：分析医学影像人工智慧市场的五种竞争力量，包括新进入者的威胁、买方的议价能力、供应商的议价能力、替代品的威胁以及现有竞争对手之间的竞争。
  • SWOT分析：一个深入的SWOT框架，突显该领域的优势、劣势、机会和威胁。此外，我们也提供哈维鲍尔分析，重点阐述每个 SWOT 参数的相对影响。
  • 价值链分析：我们提供全面的分析，涵盖人工智慧在医疗影像市场的各个阶段和利害关係人。

目录

第一部分：报告概述

第一章：引言

第二章：研究方法

第三章：市场动态

第四章：宏观经济指标

第二部分：质性分析

第五章：摘要整理

第六章：引言

  • 章节概述
  • 人工智慧在医学影像领域的市场概述
  • 未来展望

第七章：监理环境

第三部分：市场概况

第八章 主要参与者综合资料库

第九章：竞争格局

  • 章节概述
  • 医学影像人工智慧市场：市场格局

第十章：市场空白分析

第十一章：竞争分析

第十二章：医学影像人工智慧新创企业生态系统

  • 医学影像人工智慧市场：市场格局
  • 主要发现

第四部分：公司简介

第十三章：公司简介

  • 章节概述
  • 阿特鲁斯*
  • 动脉
  • 蝴蝶网
  • 语境愿景
  • 精英
  • 迴声
  • 通用电气医疗保健
  • 推论视觉
  • 武诺

第五部分：市场趋势

第 14 章：大趋势分析

第 15 章：专利分析

第 16 章：最新进展

  • 章节概述
  • 近期融资
  • 最近的合作关係
  • 其他近期举措

第六部分：市场机会分析

第17章：全球人工智慧在医学影像领域的市场

第18章：依应用领域划分的市场机会

第19章：依图像类型划分的市场机会

第20章：人工智慧在北美医学影像领域的市场机会

第21章：人工智慧在欧洲医学影像领域的市场机会

第22章：人工智慧在亚洲医学影像领域的市场机会

第23章：人工智慧在中东和北非（MENA）医学影像领域的市场机会

第24章：人工智慧在拉丁美洲医学影像领域的市场机会

第25章：人工智慧在其他地区诊断医学影像领域的市场机会

第26章：市场集中度分析：主要参与者的分布

第27章：邻近市场分析

第七部分：策略工具

第28章：关键制胜策略

第29章：波特五力分析

第30章：SWOT分析

第31章：ROOT的策略建议

第八部分：其他独家见解

第32章：来自主要参与者的见解

第33章：报告结论

第十一节：附录

第34章：表格资料

第35章：公司与组织清单

第36章：ROOT的订阅服务

第37章：作者资讯

简介目录
Product Code: RA100220

AI in Medical Imaging Market Outlook

As per Roots Analysis, the global AI in medical imaging market size is estimated to grow from USD 1.75 billion in current year to USD 8.56 billion by 2040, at a CAGR of 30% during the forecast period, till 2040. The new study provides a comprehensive AI in medical imaging market analysis, pipeline analysis, partnerships and collaborations, funding and investments analysis, company valuation analysis, patent analysis, cost saving analysis and detailed market analysis.

The growth of the AI in medical imaging market over the next ten years is expected to stem from the rising implementation of artificial intelligence (AI) technology, especially in deep learning algorithms, a growing emphasis on personalized and precision medicine, unmet needs within the target demographic, and backing from venture capital. Deep learning is an approach to machine learning that utilizes sophisticated algorithms and artificial neural networks to enable unsupervised pattern recognition and insight generation from large quantities of unstructured data. This technology is progressively being integrated into various areas of the healthcare industry, including medical diagnosis based on imaging and data analysis.

Over time, various stakeholders in the industry have developed proprietary deep learning algorithms for medical image processing. At present, numerous innovators assert they have created systems that can teach computers to examine and prioritize medical images, identifying patterns connected to both temporal and spatial changes that may not even be discernible to the human eye.

AI in Medical Imaging Market - IMG1

Strategic Insights for Senior Leaders

Key Drivers Propelling Growth of AI in Medical Imaging Market

The primary factors propelling the AI in medical imaging market include the increasing number of diagnostic procedures, lack of qualified radiologists, rising integration of AI in telemedicine and remote diagnostics, and the necessity to efficiently manage large medical datasets. Innovations in technology such as deep learning, convolutional neural networks, and generative adversarial networks have enhanced the accuracy and effectiveness of AI applications in medical image analysis. Furthermore, support from governments, beneficial regulatory environments, and collaborations between hospitals and AI solution providers are speeding up the adoption process.

Other significant growth drivers include the expansion of AI applications across different medical imaging techniques, such as mammography, ultrasound, MRI, and pathology. AI assists in minimizing image noise, reducing radiation exposure during scans, and enhancing clinical decision-making processes.

Technological Advancements in AI in Medical Imaging Industry

Technological advancements in artificial intelligence (AI) have improved technology in medical imaging, enhancing the precision, efficiency, and customization of diagnostics. AI-driven algorithms can now accurately detect and identify abnormalities such as tumors and fractures across various imaging techniques, including CT, MRI, and X-ray. These systems take over routine tasks, optimize workflows, and alleviate radiologist fatigue, enabling clinicians to focus on more complex cases. Additionally, AI combines imaging information with clinical and genomic data to provide tailored insights and predictive analytics for assessing disease risk. Innovations like generative AI amplify image datasets, while real-time AI tools aid in surgical navigation, enhancing procedural accuracy.

Moreover, explainable AI and federated learning improve transparency and scalability within clinical environments. Collectively, these developments are transforming medical imaging by facilitating earlier and more precise diagnoses, ultimately enhancing patient outcomes globally.

AI in Medical Imaging Market: Competitive Landscape of Companies in this Industry

The competitive landscape of AI in medical imaging market is characterized by intense competition, featuring a combination of large and smaller firms. Leading Tier I companies (GE Healthcare, Siemens Healthineers, Philips, Canon Medical) maintain their dominance by incorporating integrated AI ecosystems into their imaging systems. Tier II companies (Aidoc, Arterys, Qure.ai, Gleamer, Viz.ai) are transforming the market through agile, cloud-native algorithms designed for disease-specific applications. The future differentiation in competition will depend on factors like model explainability, interoperability, and the extent of clinical validation. Emerging trends related to AI marketplaces, open AI platforms, and vendor-neutral integration frameworks are anticipated to further reshape competitive dynamics.

AI in Medical Imaging Evolution: Emerging Trends in the Industry

Key trends in this industry include faster regulatory approvals that facilitate broader clinical implementation, the utilization of AI to tailor scanning protocols according to patient information, and the introduction of AI-powered tools for optimizing workflows, particularly in radiology where routine diagnostics are increasingly handled by AI. Innovative developments like AI-assisted ultrasound, real-time image evaluation during surgeries, and cloud-based solutions are improving access to advanced imaging, even in rural and underserved regions. These advancements are shifting imaging from a qualitative discipline to one driven by data, focusing on patient-centric approaches with improved accuracy, efficiency, and healthcare equality.

Key Market Challenges

Key obstacles in the AI in medical imaging include significant costs for implementation and ongoing maintenance, concerns regarding the privacy and security of sensitive patient data. Additionally, regulatory challenges related to new technologies, and the necessity for smooth integration with established systems along with acceptance from healthcare professionals also hinders the growth.

Other significant challenges include insufficient integration into existing workflows, fragmented healthcare data, and isolated IT systems, which hinder the seamless deployment of AI solutions. Moreover, ethical and regulatory issues regarding data privacy, biases in AI models, and ensuring accountability impede clinical adoption. Additionally, there is a lack of a qualified AI workforce and vague regulatory guidance, which further complicates growth and integration into everyday clinical practice.

Regional Analysis: Asia to Hold the Largest Share in the Market

According to our estimates North America currently captures a significant share of the AI in medical imaging market. This is due to its sophisticated healthcare system, considerable financial support for healthcare technology, and vigorous research and development efforts, particularly in the US. The presence of many top technology companies and innovative startups promotes the advancement and implementation of state-of-the-art AI applications.

Furthermore, the regulatory environment in North America, characterized by proactive FDA approvals and reimbursement incentives, facilitates the introduction of AI solutions. This region also has a high prevalence of chronic diseases, which boosts the demand for early and precise diagnoses through AI-enhanced imaging.

AI in Medical Imaging Market: Key Market Segmentation

Application Area

  • Lung Infections / Respiratory Disorders
  • Brain Injuries / Disorders
  • Lung Cancer
  • Cardiac Conditions / Cardiovascular Disorders
  • Bone Deformities / Orthopedic Disorders
  • Breast Cancer
  • Other Application Areas

Type of Image Processed

  • X-ray
  • MRI
  • CT
  • Ultrasound

Geographical Regions

  • North America
  • US
  • Canada
  • Mexico
  • Other North American countries
  • Europe
  • Austria
  • Belgium
  • Denmark
  • France
  • Germany
  • Ireland
  • Italy
  • Netherlands
  • Norway
  • Russia
  • Spain
  • Sweden
  • Switzerland
  • UK
  • Other European countries
  • Asia
  • China
  • India
  • Japan
  • Singapore
  • South Korea
  • Other Asian countries
  • Latin America
  • Brazil
  • Chile
  • Colombia
  • Venezuela
  • Other Latin American countries
  • Middle East and North Africa
  • Egypt
  • Iran
  • Iraq
  • Israel
  • Kuwait
  • Saudi Arabia
  • UAE
  • Other MENA countries
  • Rest of the World
  • Australia
  • New Zealand
  • Other countries

Example Players in AI in Medical Imaging Market

  • Artelus
  • Arterys
  • Butterfly Network
  • ContextVision
  • Enlitic
  • Echonous
  • GE Healthcare
  • InferVision
  • VUNO

AI in Medical Imaging Market: Report Coverage

The report on the AI in medical imaging market features insights on various sections, including:

  • Market Sizing and Opportunity Analysis: An in-depth analysis of the AI in medical imaging market, focusing on key market segments, including [A] application area, [B] type of image processed, and [C] key geographical regions.
  • Competitive Landscape: A comprehensive analysis of the companies engaged in the AI in medical imaging market, based on several relevant parameters, such as [A] year of establishment, [B] company size, [C] location of headquarters and [D] ownership structure.
  • Company Profiles: Elaborate profiles of prominent players engaged in the AI in medical imaging market, providing details on [A] location of headquarters, [B] company size, [C] company mission, [D] company footprint, [E] management team, [F] contact details, [G] financial information, [H] operating business segments, [I] portfolio, [J] recent developments, and an informed future outlook.
  • Megatrends: An evaluation of ongoing megatrends in the AI in medical imaging industry.
  • Patent Analysis: An insightful analysis of patents filed / granted in the AI in medical imaging domain, based on relevant parameters, including [A] type of patent, [B] patent publication year, [C] patent age and [D] leading players.
  • Recent Developments: An overview of the recent developments made in the AI in medical imaging market, along with analysis based on relevant parameters, including [A] year of initiative, [B] type of initiative, [C] geographical distribution and [D] most active players.
  • Porter's Five Forces Analysis: An analysis of five competitive forces prevailing in the AI in medical imaging market, including threats of new entrants, bargaining power of buyers, bargaining power of suppliers, threats of substitute products and rivalry among existing competitors.
  • SWOT Analysis: An insightful SWOT framework, highlighting the strengths, weaknesses, opportunities and threats in the domain. Additionally, it provides Harvey ball analysis, highlighting the relative impact of each SWOT parameter.
  • Value Chain Analysis: A comprehensive analysis of the value chain, providing information on the different phases and stakeholders involved in the AI in medical imaging market.

Key Questions Answered in this Report

  • What is the current and future market size?
  • Who are the leading companies in this market?
  • What are the growth drivers that are likely to influence the evolution of this market?
  • What are the key partnership and funding trends shaping this industry?
  • Which region is likely to grow at higher CAGR till 2040?
  • How is the current and future market opportunity likely to be distributed across key market segments?

Reasons to Buy this Report

  • Detailed Market Analysis: The report provides a comprehensive market analysis, offering detailed revenue projections of the overall market and its specific sub-segments. This information is valuable to both established market leaders and emerging entrants.
  • In-depth Analysis of Trends: Stakeholders can leverage the report to gain a deeper understanding of the competitive dynamics within the market. Each report maps ecosystem activity across partnerships, funding, and patent landscapes to reveal growth hotspots and white spaces in the industry.
  • Opinion of Industry Experts: The report features extensive interviews and surveys with key opinion leaders and industry experts to validate market trends mentioned in the report.
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TABLE OF CONTENTS

SECTION I: REPORT OVERVIEW

1. PREFACE

  • 1.1. Introduction
  • 1.2. Market Share Insights
  • 1.3. Key Market Insights
  • 1.4. Report Coverage
  • 1.5. Key Questions Answered
  • 1.6. Chapter Outlines

2. RESEARCH METHODOLOGY

  • 2.1. Chapter Overview
  • 2.2. Research Assumptions
  • 2.3. Database Building
    • 2.3.1. Data Collection
    • 2.3.2. Data Validation
    • 2.3.3. Data Analysis
  • 2.4. Project Methodology
    • 2.4.1. Secondary Research
      • 2.4.1.1. Annual Reports
      • 2.4.1.2. Academic Research Papers
      • 2.4.1.3. Company Websites
      • 2.4.1.4. Investor Presentations
      • 2.4.1.5. Regulatory Filings
      • 2.4.1.6. White Papers
      • 2.4.1.7. Industry Publications
      • 2.4.1.8. Conferences and Seminars
      • 2.4.1.9. Government Portals
      • 2.4.1.10. Media and Press Releases
      • 2.4.1.11. Newsletters
      • 2.4.1.12. Industry Databases
      • 2.4.1.13. Roots Proprietary Databases
      • 2.4.1.14. Paid Databases and Sources
      • 2.4.1.15. Social Media Portals
      • 2.4.1.16. Other Secondary Sources
    • 2.4.2. Primary Research
      • 2.4.2.1. Introduction
      • 2.4.2.2. Types
        • 2.4.2.2.1. Qualitative
        • 2.4.2.2.2. Quantitative
      • 2.4.2.3. Advantages
      • 2.4.2.4. Techniques
        • 2.4.2.4.1. Interviews
        • 2.4.2.4.2. Surveys
        • 2.4.2.4.3. Focus Groups
        • 2.4.2.4.4. Observational Research
        • 2.4.2.4.5. Social Media Interactions
      • 2.4.2.5. Stakeholders
        • 2.4.2.5.1. Company Executives (CXOs)
        • 2.4.2.5.2. Board of Directors
        • 2.4.2.5.3. Company Presidents and Vice Presidents
        • 2.4.2.5.4. Key Opinion Leaders
        • 2.4.2.5.5. Research and Development Heads
        • 2.4.2.5.6. Technical Experts
        • 2.4.2.5.7. Subject Matter Experts
        • 2.4.2.5.8. Scientists
        • 2.4.2.5.9. Doctors and Other Healthcare Providers
      • 2.4.2.6. Ethics and Integrity
        • 2.4.2.6.1. Research Ethics
        • 2.4.2.6.2. Data Integrity
    • 2.4.3. Analytical Tools and Databases

3. MARKET DYNAMICS

  • 3.1. Forecast Methodology
    • 3.1.1. Top-Down Approach
    • 3.1.2. Bottom-Up Approach
    • 3.1.3. Hybrid Approach
  • 3.2. Market Assessment Framework
    • 3.2.1. Total Addressable Market (TAM)
    • 3.2.2. Serviceable Addressable Market (SAM)
    • 3.2.3. Serviceable Obtainable Market (SOM)
    • 3.2.4. Currently Acquired Market (CAM)
  • 3.3. Forecasting Tools and Techniques
    • 3.3.1. Qualitative Forecasting
    • 3.3.2. Correlation
    • 3.3.3. Regression
    • 3.3.4. Time Series Analysis
    • 3.3.5. Extrapolation
    • 3.3.6. Convergence
    • 3.3.7. Forecast Error Analysis
    • 3.3.8. Data Visualization
    • 3.3.9. Scenario Planning
    • 3.3.10. Sensitivity Analysis
  • 3.4. Key Considerations
    • 3.4.1. Demographics
    • 3.4.2. Market Access
    • 3.4.3. Reimbursement Scenarios
    • 3.4.4. Industry Consolidation
  • 3.5. Robust Quality Control
  • 3.6. Key Market Segmentations
  • 3.7. Limitations

4. MACRO-ECONOMIC INDICATORS

  • 4.1. Chapter Overview
  • 4.2. Market Dynamics
    • 4.2.1. Time Period
      • 4.2.1.1. Historical Trends
      • 4.2.1.2. Current and Forecasted Estimates
    • 4.2.2. Currency Coverage
      • 4.2.2.1. Overview of Major Currencies Affecting the Market
      • 4.2.2.2. Impact of Currency Fluctuations on the Industry
    • 4.2.3. Foreign Exchange Impact
      • 4.2.3.1. Evaluation of Foreign Exchange Rates and Their Impact on Market
      • 4.2.3.2. Strategies for Mitigating Foreign Exchange Risk
    • 4.2.4. Recession
      • 4.2.4.1. Historical Analysis of Past Recessions and Lessons Learnt
      • 4.2.4.2. Assessment of Current Economic Conditions and Potential Impact on the Market
    • 4.2.5. Inflation
      • 4.2.5.1. Measurement and Analysis of Inflationary Pressures in the Economy
      • 4.2.5.2. Potential Impact of Inflation on the Market Evolution
    • 4.2.6. Interest Rates
      • 4.2.6.1. Overview of Interest Rates and Their Impact on the Market
      • 4.2.6.2. Strategies for Managing Interest Rate Risk
    • 4.2.7. Commodity Flow Analysis
      • 4.2.7.1. Type of Commodity
      • 4.2.7.2. Origins and Destinations
      • 4.2.7.3. Values and Weights
      • 4.2.7.4. Modes of Transportation
    • 4.2.8. Global Trade Dynamics
      • 4.2.8.1. Import Scenario
      • 4.2.8.2. Export Scenario
    • 4.2.9. War Impact Analysis
      • 4.2.9.1. Russian-Ukraine War
      • 4.2.9.2. Israel-Hamas War
    • 4.2.10. COVID Impact / Related Factors
      • 4.2.10.1. Global Economic Impact
      • 4.2.10.2. Industry-specific Impact
      • 4.2.10.3. Government Response and Stimulus Measures
      • 4.2.10.4. Future Outlook and Adaptation Strategies
    • 4.2.11. Other Indicators
      • 4.2.11.1. Fiscal Policy
      • 4.2.11.2. Consumer Spending
      • 4.2.11.3. Gross Domestic Product (GDP)
      • 4.2.11.4. Employment
      • 4.2.11.5. Taxes
      • 4.2.11.6. R&D Innovation
      • 4.2.11.7. Stock Market Performance
      • 4.2.11.8. Supply Chain
      • 4.2.11.9. Cross-Border Dynamics

SECTION II: QUALITATIVE INSIGHTS

5. EXECUTIVE SUMMARY

6. INTRODUCTION

  • 6.1. Chapter Overview
  • 6.2. Overview of AI in Medical Imaging Market
    • 6.2.1. Evolution and Milestones
    • 6.2.2. Type of Technology
    • 6.2.3. Key Applications
    • 6.2.4. Regulatory and Ethical Considerations
  • 6.3. Future Perspective

7. REGULATORY SCENARIO

SECTION III: MARKET OVERVIEW

8. COMPREHENSIVE DATABASE OF LEADING PLAYERS

9. COMPETITIVE LANDSCAPE

  • 9.1. Chapter Overview
  • 9.2. AI in Medical Imaging Market: Overall Market Landscape
    • 9.2.1. Analysis by Year of Establishment
    • 9.2.2. Analysis by Company Size
    • 9.2.3. Analysis by Location of Headquarters
    • 9.2.4. Analysis by Type of AI In Medical Imaging Solution Provider

10. WHITE SPACE ANALYSIS

11. COMPANY COMPETITIVENESS ANALYSIS

12. STARTUP ECOSYSTEM IN THE AI IN MEDICAL IMAGING MARKET

  • 12.1. AI in Medical Imaging Market: Market Landscape of Startups
    • 12.1.1. Analysis by Year of Establishment
    • 12.1.2. Analysis by Company Size
    • 12.1.3. Analysis by Company Size and Year of Establishment
    • 12.1.4. Analysis by Location of Headquarters
    • 12.1.5. Analysis by Company Size and Location of Headquarters
    • 12.1.6. Analysis by Ownership Structure
  • 12.2. Key Findings

SECTION IV: COMPANY PROFILES

13. COMPANY PROFILES

  • 13.1. Chapter Overview
  • 13.2. Artelus *
    • 13.2.1. Company Overview
    • 13.2.2. Company Mission
    • 13.2.3. Company Footprint
    • 13.2.4. Management Team
    • 13.2.5. Contact Details
    • 13.2.6. Financial Performance
    • 13.2.7. Operating Business Segments
    • 13.2.8. Service / Product Portfolio (project specific)
    • 13.2.9. MOAT Analysis
    • 13.2.10. Recent Developments and Future Outlook
  • 13.3. Arterys
  • 13.4. Butterfly Network
  • 13.5. ContextVision
  • 13.6. Enlitic
  • 13.7. Echonous
  • 13.8. GE Healthcare
  • 13.9. InferVision
  • 13.10. VUNO

SECTION V: MARKET TRENDS

14. MEGA TRENDS ANALYSIS

15. PATENT ANALYSIS

16. RECENT DEVELOPMENTS

  • 16.1. Chapter Overview
  • 16.2. Recent Funding
  • 16.3. Recent Partnerships
  • 16.4. Other Recent Initiatives

SECTION VI: MARKET OPPORTUNITY ANALYSIS

17. GLOBAL AI IN MEDICAL IMAGING MARKET

  • 17.1. Chapter Overview
  • 17.2. Key Assumptions and Methodology
  • 17.3. Trends Disruption Impacting Market
  • 17.4. Demand Side Trends
  • 17.5. Supply Side Trends
  • 17.6. Global AI in Medical Imaging Market, Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 17.7. Multivariate Scenario Analysis
    • 17.7.1. Conservative Scenario
    • 17.7.2. Optimistic Scenario
  • 17.8. Investment Feasibility Index
  • 17.9. Key Market Segmentations

18. MARKET OPPORTUNITIES BASED ON APPLICATION AREA

  • 18.1. Chapter Overview
  • 18.2. Key Assumptions and Methodology
  • 18.3. Revenue Shift Analysis
  • 18.4. Market Movement Analysis
  • 18.5. Penetration-Growth (P-G) Matrix
  • 18.6. AI in Medical Imaging Market for Lung Infections / Respiratory Disorders: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 18.7. AI in Medical Imaging Market for Brain Injuries / Disorders: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 18.8. AI in Medical Imaging Market for Lung Cancer: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 18.9. AI in Medical Imaging Market for Cardiac Conditions / Cardiovascular Disorders: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 18.10. AI in Medical Imaging Market for Bone Deformities / Orthopedic Disorders: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 18.11. AI in Medical Imaging Market for Breast Cancer: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 18.12. AI in Medical Imaging Market for Other Application Areas: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 18.13. Data Triangulation and Validation
    • 18.13.1. Secondary Sources
    • 18.13.2. Primary Sources
    • 18.13.3. Statistical Modeling

19. MARKET OPPORTUNITIES BASED ON TYPE OF IMAGE PROCESSED

  • 19.1. Chapter Overview
  • 19.2. Key Assumptions and Methodology
  • 19.3. Revenue Shift Analysis
  • 19.4. Market Movement Analysis
  • 19.5. Penetration-Growth (P-G) Matrix
  • 19.6. AI in Medical Imaging Market for X-ray: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 19.7. AI in Medical Imaging Market for MRI: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 19.8. AI in Medical Imaging Market for CT: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 19.9. AI in Medical Imaging Market for Ultrasound: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 19.10. Data Triangulation and Validation
    • 19.10.1. Secondary Sources
    • 19.10.2. Primary Sources
    • 19.10.3. Statistical Modeling

20. MARKET OPPORTUNITIES FOR AI IN MEDICAL IMAGING IN NORTH AMERICA

  • 20.1. Chapter Overview
  • 20.2. Key Assumptions and Methodology
  • 20.3. Revenue Shift Analysis
  • 20.4. Market Movement Analysis
  • 20.5. Penetration-Growth (P-G) Matrix
  • 20.6. AI in Medical Imaging Market in North America: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 20.6.1. AI in Medical Imaging Market in the US: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 20.6.2. AI in Medical Imaging Market in Canada: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 20.6.3. AI in Medical Imaging Market in Mexico: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 20.6.4. AI in Medical Imaging Market in Other North American Countries: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 20.7. Data Triangulation and Validation

21. MARKET OPPORTUNITIES FOR AI IN MEDICAL IMAGING IN EUROPE

  • 21.1. Chapter Overview
  • 21.2. Key Assumptions and Methodology
  • 21.3. Revenue Shift Analysis
  • 21.4. Market Movement Analysis
  • 21.5. Penetration-Growth (P-G) Matrix
  • 21.6. AI in Medical Imaging Market in Europe: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 21.6.1. AI in Medical Imaging Market in Austria: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 21.6.2. AI in Medical Imaging Market in Belgium: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 21.6.3. AI in Medical Imaging Market in Denmark: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 21.6.4. AI in Medical Imaging Market in France: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 21.6.5. AI in Medical Imaging Market in Germany: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 21.6.6. AI in Medical Imaging Market in Ireland: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 21.6.7. AI in Medical Imaging Market in Italy: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 21.6.8. AI in Medical Imaging Market in Netherlands: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 21.6.9. AI in Medical Imaging Market in Norway: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 21.6.10. AI in Medical Imaging Market in Russia: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 21.6.11. AI in Medical Imaging Market in Spain: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 21.6.12. AI in Medical Imaging Market in Sweden: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 21.6.13. AI in Medical Imaging Market in Switzerland: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 21.6.14. AI in Medical Imaging Market in the UK: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 21.6.15. AI in Medical Imaging Market in Other European Countries: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 21.7. Data Triangulation and Validation

22. MARKET OPPORTUNITIES FOR AI IN MEDICAL IMAGING IN ASIA

  • 22.1. Chapter Overview
  • 22.2. Key Assumptions and Methodology
  • 22.3. Revenue Shift Analysis
  • 22.4. Market Movement Analysis
  • 22.5. Penetration-Growth (P-G) Matrix
  • 22.6. AI in Medical Imaging Market in Asia: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 22.6.1. AI in Medical Imaging Market in China: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 22.6.2. AI in Medical Imaging Market in India: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 22.6.3. AI in Medical Imaging Market in Japan: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 22.6.4. AI in Medical Imaging Market in Singapore: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 22.6.5. AI in Medical Imaging Market in South Korea: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 22.6.6. AI in Medical Imaging Market in Other Asian Countries: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 22.7. Data Triangulation and Validation

23. MARKET OPPORTUNITIES FOR AI IN MEDICAL IMAGING IN MIDDLE EAST AND NORTH AFRICA (MENA)

  • 23.1. Chapter Overview
  • 23.2. Key Assumptions and Methodology
  • 23.3. Revenue Shift Analysis
  • 23.4. Market Movement Analysis
  • 23.5. Penetration-Growth (P-G) Matrix
  • 23.6. AI in Medical Imaging Market in Middle East and North Africa (MENA): Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 23.6.1. AI in Medical Imaging Market in Egypt: Historical Trends (Since 2020) and Forecasted Estimates (Till 205)
    • 23.6.2. AI in Medical Imaging Market in Iran: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 23.6.3. AI in Medical Imaging Market in Iraq: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 23.6.4. AI in Medical Imaging Market in Israel: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 23.6.5. AI in Medical Imaging Market in Kuwait: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 23.6.6. AI in Medical Imaging Market in Saudi Arabia: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 23.6.7. AI in Medical Imaging Market in United Arab Emirates (UAE): Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 23.6.8. AI in Medical Imaging Market in Other MENA Countries: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 23.7. Data Triangulation and Validation

24. MARKET OPPORTUNITIES FOR AI IN MEDICAL IMAGING IN LATIN AMERICA

  • 24.1. Chapter Overview
  • 24.2. Key Assumptions and Methodology
  • 24.3. Revenue Shift Analysis
  • 24.4. Market Movement Analysis
  • 24.5. Penetration-Growth (P-G) Matrix
  • 24.6. AI in Medical Imaging Market in Latin America: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 24.6.1. AI in Medical Imaging Market in Argentina: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 24.6.2. AI in Medical Imaging Market in Brazil: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 24.6.3. AI in Medical Imaging Market in Chile: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 24.6.4. AI in Medical Imaging Market in Colombia Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 24.6.5. AI in Medical Imaging Market in Venezuela: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 24.6.6. AI in Medical Imaging Market in Other Latin American Countries: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 24.7. Data Triangulation and Validation

25. MARKET OPPORTUNITIES FOR AI IN MEDICAL IMAGING IN REST OF THE WORLD

  • 25.1. Chapter Overview
  • 25.2. Key Assumptions and Methodology
  • 25.3. Revenue Shift Analysis
  • 25.4. Market Movement Analysis
  • 25.5. Penetration-Growth (P-G) Matrix
  • 25.6. AI in Medical Imaging Market in Rest of the World: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 25.6.1. AI in Medical Imaging Market in Australia: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 25.6.2. AI in Medical Imaging Market in New Zealand: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 25.6.3. AI in Medical Imaging Market in Other Countries
  • 25.7. Data Triangulation and Validation

26. MARKET CONCENTRATION ANALYSIS: DISTRIBUTION BY LEADING PLAYERS

  • 26.1. Leading Player 1
  • 26.2. Leading Player 2
  • 26.3. Leading Player 3
  • 26.4. Leading Player 4
  • 26.5. Leading Player 5
  • 26.6. Leading Player 6
  • 26.7. Leading Player 7
  • 26.8. Leading Player 8

27. ADJACENT MARKET ANALYSIS

SECTION VII: STRATEGIC TOOLS

28. KEY WINNING STRATEGIES

29. PORTER'S FIVE FORCES ANALYSIS

30. SWOT ANALYSIS

31. ROOTS STRATEGIC RECOMMENDATIONS

  • 31.1. Chapter Overview
  • 31.2. Key Business-related Strategies
    • 31.2.1. Research & Development
    • 31.2.2. Product Manufacturing
    • 31.2.3. Commercialization / Go-to-Market
    • 31.2.4. Sales and Marketing
  • 31.3. Key Operations-related Strategies
    • 31.3.1. Risk Management
    • 31.3.2. Workforce
    • 31.3.3. Finance
    • 31.3.4. Others

SECTION VIII: OTHER EXCLUSIVE INSIGHTS

32. INSIGHTS FROM PRIMARY RESEARCH

33. REPORT CONCLUSION

SECTION IX: APPENDIX

34. TABULATED DATA

35. LIST OF COMPANIES AND ORGANIZATIONS

36. ROOTS SUBSCRIPTION SERVICES

37. AUTHOR DETAILS