查看购物车
  • Traditional Chinese
  • English
  • Japanese
封面
市场调查报告书
商品编码
1856984

全球医疗图像人工智慧市场：预测至2032年－按组件、显像模式、部署方法、技术、应用、最终用户和地区进行分析

AI in Medical Imaging Market Forecasts to 2032 - Global Analysis By Component (Software and Services), Imaging Modality, Deployment Mode, Technology, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的数据，全球用于医疗图像的AI 市场预计到 2025 年将达到 18.5 亿美元，到 2032 年将达到 164.8 亿美元，预测期内复合年增长率为 36.6%。

医疗图像人工智慧（AI）是指应用先进的计算演算法和机器学习技术来分析、解读和增强医学影像，包括X光、 电脑断层扫描、MRI和超音波。 AI系统能够自动侦测模式并量化异常，有助于放射科医师更准确、更有效率地诊断疾病。透过利用深度学习模型，AI可以提高影像品质、减少人为误差，并实现对患者预后的预测分析。它还有助于优化工作流程、制定个人化治疗方案和更早发现疾病，使医学影像转变为更精准、数据主导、以患者为中心的医疗服务。

人工智慧演算法和运算能力的进步

深度学习模型支援对CT、MRI、X光和超音波等多种影像模式中的异常进行自动检测、分割和分类。 GPU加速和云端基础处理技术实现了即时分析，并可在医院和影像中心进行可扩展部署。与PACS和RIS系统的整合提高了工作流程效率和诊断吞吐量。在高容量、资源受限的环境中，对AI辅助阅片的需求日益增长。这些功能正在推动全球医疗保健系统的平台创新和临床应用。

与现有系统的整合问题

人工智慧影像处理工具必须与传统的PACS、EMR和医院IT系统对接，而这些系统架构和资料标准各不相同。客製化整合计划会增加成本、延缓实施并降低工作流程的连续性。缺乏标准化的API和资料格式阻碍了跨平台相容性和供应商协作。 IT团队在混合部署中面临维护资料完整性、审核和合规性的挑战。这些限制阻碍了在拥有跨多个地点、基础设施各异的医疗网络中推广应用。

对早期准确诊断的需求日益增长

人工智慧模型能够提高复杂影像资料集中肿瘤、病变和异常检测的敏感度和特异性。该平台支援分诊、优先排序和二次阅片工作流程，从而增强临床决策能力并减少诊断延误。与电子健康记录和临床决策支援工具的集成，可实现纵向分析和个人化诊疗。筛检项目和基于价值的医疗模式对可扩展、可重复的诊断工具的需求日益增长。这一趋势正在推动人工智慧驱动的诊断成像和精准诊断技术的发展。

缺乏标准化和法律规范

监管机构对人工智慧模型核准、上市后监管和临床试验要求的执行方式各不相同。缺乏统一的绩效基准和审核通讯协定，使得供应商比较和采购决策变得复杂。医院和影像中心在评估模型在不同患者群体中的可靠性、偏差和普适性方面面临诸多挑战。公共和私人支付方对人工智慧辅助诊断的报销政策仍不完善。这些风险持续限制平台成熟度和在受监管医疗环境中的临床整合。

新冠疫情的影响：

疫情加速了人工智慧在医疗图像的应用，因为医疗系统面临诊断积压、人员短缺和感染控制等诸多挑战。人工智慧工具支援对胸部CT和X光片中的新冠肺炎进行分诊和严重程度评分。远距阅片和云端基础部署确保了在资源匮乏的偏远地区也能提供持续的医疗服务。急诊和门诊对可扩展的自动化影像工作流程的需求激增。疫情后的策略已将人工智慧影像作为诊断韧性和数位医疗基础设施的核心组成部分。这一转变强化了对智慧影像处理平台和临床人工智慧管治的长期投资。

预计深度学习细分市场在预测期内将成为最大的细分市场。

由于深度学习在影像分类、分割和异常检测方面表现出色，预计在预测期内将占据最大的市场份额。卷积类神经网路和基于Transformer的架构能够对放射学和病理学影像进行高精度解读。该平台利用预训练模型和迁移学习技术，加速在各种临床环境中的部署。与标註工具和资料湖的集成，实现了模型的持续改进和检验。医院、研究机构和影像设备供应商对可扩展且可解释的深度学习解决方案的需求日益增长。

预计肿瘤领域在预测期内将达到最高的复合年增长率。

预计在预测期内，肿瘤学领域将实现最高成长率，因为人工智慧平台正不断扩展应用于癌症筛检、分期和治疗计划制定。治疗模型能够检测肿瘤、测量疾病进展并评估乳癌、肺癌、摄护腺癌和大肠癌等癌症的治疗反应。与放射组学和基因组学平台的整合支援多模态分析和个人化肿瘤工作流程。公共卫生计画和肿瘤中心对早期检测和精准诊断的需求日益增长。在临床试验、学术研究和商业应用方面，对人工智慧癌症成像的投资也不断增加。

占比最大的地区：

由于北美拥有先进的医疗基础设施、完善的监管体係以及医院和影像网路的企业级应用，预计在预测期内，北美将占据最大的市场份额。美国和加拿大的医疗机构正在放射科、病理科和肿瘤科部署人工智慧影像处理平台，以提高诊断准确性和工作流程效率。对云端基础设施、资料管治和临床检验的投资，为平台的扩充性和合规性提供了保障。主要供应商、学术中心和监管机构的存在，推动了创新和标准化进程。

复合年增长率最高的地区：

预计亚太地区在预测期内将呈现最高的复合年增长率，这得益于医疗现代化、癌症筛检计画和人工智慧政策改革在区域经济中的整合。中国、印度、日本和韩国等国家正在公立医院、诊断实验室和远端医疗网路中推广人工智慧影像处理平台。政府支持的倡议正在推动基础设施投资、新兴企业孵化以及在都市区地区检验临床人工智慧。本地供应商提供多语言、高性价比的解决方案，以满足区域疾病特征和合规性需求。服务不足的人口和高流量影像中心对可扩展且易于使用的诊断工具的需求日益增长。这些趋势正在推动区域人工智慧医疗图像生态系统的发展。

免费客製化服务

订阅本报告的用户可从以下免费自订选项中选择一项：

  • 公司简介
    • 对最多三家其他公司进行全面分析
    • 对主要企业进行SWOT分析（最多3家公司）
  • 区域分类
    • 根据客户兴趣对主要国家进行市场估算、预测和复合年增长率分析（註：基于可行性检查）
  • 竞争基准化分析
    • 基于产品系列、地域覆盖和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第二章 引言

  • 概述
  • 相关利益者
  • 分析范围
  • 分析方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 分析方法
  • 分析材料
    • 原始研究资料
    • 二手研究资讯来源
    • 先决条件

第三章 市场趋势分析

  • 司机
  • 抑制因素
  • 市场机会
  • 威胁
  • 技术分析
  • 应用分析
  • 终端用户分析
  • 新兴市场
  • 新冠疫情的感染疾病

第四章 波特五力分析

  • 供应商的议价能力
  • 买方议价能力
  • 替代产品的威胁
  • 新参与企业的威胁
  • 公司间的竞争

5. 全球医疗图像人工智慧市场（按组件划分）

  • 软体
    • 独立人工智慧工具
    • 整合人工智慧平台
  • 服务
    • 部署和集成
    • 培训支援
    • 咨询

6. 全球医疗图像人工智慧市场（依显像模式）

  • X射线
  • 电脑断层扫描（CT）
  • 磁振造影（MRI）
  • 超音波
  • 正子断层扫描（PET）
  • 单光子发射电脑断层扫描（SPECT）
  • 其他成像方式

7. 全球医疗图像人工智慧市场依部署模式划分

  • 云端基础的
  • 本地部署

8. 全球医疗图像人工智慧市场（按技术划分）

  • 深度学习（DL）
  • 机器学习（ML）
  • 自然语言处理（NLP）
  • 电脑视觉
  • 图像可解释人工智慧 (XAI)

9. 全球人工智慧医疗图像市场（按应用领域划分）

  • 放射科
  • 瘤
  • 心臟病
  • 神经病学
  • 整形外科
  • 呼吸系统医疗设备
  • 其他用途

第十章 全球医疗图像人工智慧市场（按最终用户划分）

  • 医院
  • 诊断影像中心
  • 专科诊所
  • 研究和学术机构
  • 其他最终用户

第十一章 全球医疗图像人工智慧市场（按地区划分）

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十二章：主要趋势

  • 合约、商业伙伴关係和合资企业
  • 企业合併（M&A）
  • 新产品发布
  • 业务拓展
  • 其他关键策略

第十三章：公司简介

  • Aidoc
  • Zebra Medical Vision
  • Arterys
  • Viz.ai
  • Qure.ai
  • Siemens Healthineers
  • GE HealthCare
  • Philips Healthcare
  • IBM Watson Health
  • NVIDIA
  • Microsoft
  • RadNet
  • Lunit
  • HeartFlow
  • Enlitic
Product Code: SMRC31849

According to Stratistics MRC, the Global AI in Medical Imaging Market is accounted for $1.85 billion in 2025 and is expected to reach $16.48 billion by 2032 growing at a CAGR of 36.6% during the forecast period. Artificial Intelligence (AI) in medical imaging refers to the application of advanced computational algorithms and machine learning techniques to analyze, interpret, and enhance medical images such as X-rays, CT scans, MRIs, and ultrasounds. AI systems can automatically detect patterns, quantify abnormalities, and assist radiologists in diagnosing diseases with higher accuracy and efficiency. By leveraging deep learning models, AI can improve image quality, reduce human error, and enable predictive analytics for patient outcomes. It also facilitates workflow optimization, personalized treatment planning, and early detection of conditions, transforming medical imaging into a more precise, data-driven, and patient-centric practice.

Market Dynamics:

Driver:

Advancements in AI algorithms and computing power

Deep learning models support automated detection, segmentation, and classification of anomalies across CT, MRI, X-ray, and ultrasound modalities. GPU acceleration and cloud-based processing enable real-time analysis and scalable deployment across hospitals and imaging centers. Integration with PACS and RIS systems improves workflow efficiency and diagnostic throughput. Demand for AI-assisted interpretation is rising across high-volume and resource-constrained environments. These capabilities are propelling platform innovation and clinical adoption across global healthcare systems.

Restraint:

Integration challenges with existing systems

AI imaging tools must interface with legacy PACS, EMR, and hospital IT systems that vary in architecture and data standards. Custom integration projects increase cost, delay implementation, and degrade workflow continuity. Lack of standardized APIs and data formats hampers cross-platform compatibility and vendor collaboration. IT teams face challenges in maintaining data integrity, auditability, and compliance across hybrid deployments. These constraints continue to hinder adoption across multi-site and infrastructure-heavy healthcare networks.

Opportunity:

Rising demand for early and accurate diagnosis

AI models improve sensitivity and specificity in detecting tumors, lesions, and abnormalities across complex imaging datasets. Platforms support triage, prioritization, and second-read workflows that enhance clinical decision-making and reduce diagnostic delays. Integration with electronic health records and clinical decision support tools enables longitudinal analysis and personalized care. Demand for scalable and reproducible diagnostic tools is rising across screening programs and value-based care models. These dynamics are fostering growth across AI-enabled imaging and precision diagnostics.

Threat:

Lack of standardization and regulatory frameworks

Regulatory bodies vary in their approach to AI model approval, post-market surveillance, and clinical trial requirements. Absence of harmonized performance benchmarks and audit protocols complicates vendor comparison and procurement decisions. Hospitals and imaging centers face challenges in assessing model reliability, bias, and generalizability across diverse patient populations. Reimbursement policies for AI-assisted diagnostics remain underdeveloped across public and private payers. These risks continue to constrain platform maturity and clinical integration across regulated healthcare environments.

Covid-19 Impact:

The pandemic accelerated AI adoption in medical imaging as healthcare systems faced diagnostic backlogs, staff shortages, and infection control mandates. AI tools supported triage and severity scoring for COVID-19 pneumonia across chest CT and X-ray scans. Remote interpretation and cloud-based deployment enabled continuity of care across quarantined and resource-limited settings. Demand for scalable and automated imaging workflows surged across emergency and outpatient departments. Post-pandemic strategies now include AI imaging as a core pillar of diagnostic resilience and digital health infrastructure. These shifts are reinforcing long-term investment in intelligent imaging platforms and clinical AI governance.

The deep learning segment is expected to be the largest during the forecast period

The deep learning segment is expected to account for the largest market share during the forecast period due to its superior performance in image classification, segmentation, and anomaly detection across medical modalities. Convolutional neural networks and transformer-based architectures support high-accuracy interpretation of radiological and pathological images. Platforms use pretrained models and transfer learning to accelerate deployment across diverse clinical settings. Integration with annotation tools and data lakes enables continuous model refinement and validation. Demand for scalable and explainable deep learning solutions is rising across hospitals, research institutions, and imaging vendors.

The oncology segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the oncology segment is predicted to witness the highest growth rate as AI platforms scale across cancer screening, staging, and treatment planning. Models detect tumours, measure progression, and assess treatment response across breast, lung, prostate, and colorectal cancers. Integration with radiomics and genomics platforms supports multi-modal analysis and personalized oncology workflows. Demand for early detection and precision diagnostics is rising across public health programs and oncology centres. Investment in AI-enabled cancer imaging is increasing across clinical trials, academic research, and commercial deployments.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share due to its advanced healthcare infrastructure, regulatory engagement, and enterprise adoption across hospitals and imaging networks. U.S. and Canadian institutions deploy AI imaging platforms across radiology, pathology, and oncology departments to improve diagnostic accuracy and workflow efficiency. Investment in cloud infrastructure, data governance, and clinical validation supports platform scalability and compliance. Presence of leading vendors, academic centres, and regulatory bodies drives innovation and standardization.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR as healthcare modernization, cancer screening programs, and AI policy reform converge across regional economies. Countries like China, India, Japan, and South Korea scale AI imaging platforms across public hospitals, diagnostic labs, and telemedicine networks. Government-backed initiatives support infrastructure investment, startup incubation, and clinical AI validation across urban and rural regions. Local vendors offer multilingual and cost-effective solutions tailored to regional disease profiles and compliance needs. Demand for scalable and accessible diagnostic tools is rising across underserved populations and high-volume imaging centres. These trends are accelerating regional growth across AI medical imaging ecosystems.

Key players in the market

Some of the key players in AI in Medical Imaging Market include Aidoc, Zebra Medical Vision, Arterys, Viz.ai, Qure.ai, Siemens Healthineers, GE HealthCare, Philips Healthcare, IBM Watson Health, NVIDIA, Microsoft, RadNet, Lunit, HeartFlow and Enlitic.

Key Developments:

In July 2025, Aidoc unveiled its CARE1(TM) model, a foundational AI engine integrated into its aiOS(TM) platform. CARE1(TM) supports multi-specialty diagnostic workflows, enabling real-time triage, prioritization, and clinical decision support across radiology, cardiology, and neurology. The launch builds on Aidoc's portfolio of 20+ FDA-cleared algorithms, positioning it as a leader in enterprise-grade clinical AI.

In June 2025, Zebra Medical Vision enhanced its AI1(TM) bundle, integrating multiple FDA-cleared algorithms into a unified diagnostic platform. The solution automates detection of conditions like coronary artery disease, osteoporosis, and breast cancer, embedding seamlessly into radiologists' native workflows. The update improves diagnostic throughput and supports population health initiatives across large hospital networks.

Components Covered:

  • Software
  • Services

Imaging Modalities Covered:

  • X-Ray
  • Computed Tomography (CT)
  • Magnetic Resonance Imaging (MRI)
  • Ultrasound
  • Positron Emission Tomography (PET)
  • Single-Photon Emission Computed Tomography (SPECT)
  • Other Imaging Modalities

Deployment Modes Covered:

  • Cloud-Based
  • On-Premise

Technologies Covered:

  • Deep Learning
  • Machine Learning
  • Natural Language Processing (NLP)
  • Computer Vision
  • Explainable AI (XAI) in Imaging

Applications Covered:

  • Radiology
  • Oncology
  • Cardiology
  • Neurology
  • Orthopedics
  • Pulmonology
  • Other Applications

End Users Covered:

  • Hospitals
  • Diagnostic Imaging Centers
  • Specialty Clinics
  • Research & Academic Institutions
  • Other End Users

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & 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 2024, 2025, 2026, 2028, and 2032
  • 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

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Technology Analysis
  • 3.7 Application Analysis
  • 3.8 End User Analysis
  • 3.9 Emerging Markets
  • 3.10 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global AI in Medical Imaging Market, By Component

  • 5.1 Introduction
  • 5.2 Software
    • 5.2.1 Standalone AI Tools
    • 5.2.2 Integrated AI Platforms
  • 5.3 Services
    • 5.3.1 Deployment & Integration
    • 5.3.2 Training & Support
    • 5.3.3 Consulting

6 Global AI in Medical Imaging Market, By Imaging Modality

  • 6.1 Introduction
  • 6.2 X-Ray
  • 6.3 Computed Tomography (CT)
  • 6.4 Magnetic Resonance Imaging (MRI)
  • 6.5 Ultrasound
  • 6.6 Positron Emission Tomography (PET)
  • 6.7 Single-Photon Emission Computed Tomography (SPECT)
  • 6.8 Other Imaging Modalities

7 Global AI in Medical Imaging Market, By Deployment Mode

  • 7.1 Introduction
  • 7.2 Cloud-Based
  • 7.3 On-Premise

8 Global AI in Medical Imaging Market, By Technology

  • 8.1 Introduction
  • 8.2 Deep Learning
  • 8.3 Machine Learning
  • 8.4 Natural Language Processing (NLP)
  • 8.5 Computer Vision
  • 8.6 Explainable AI (XAI) in Imaging

9 Global AI in Medical Imaging Market, By Application

  • 9.1 Introduction
  • 9.2 Radiology
  • 9.3 Oncology
  • 9.4 Cardiology
  • 9.5 Neurology
  • 9.6 Orthopedics
  • 9.7 Pulmonology
  • 9.8 Other Applications

10 Global AI in Medical Imaging Market, By End User

  • 10.1 Introduction
  • 10.2 Hospitals
  • 10.3 Diagnostic Imaging Centers
  • 10.4 Specialty Clinics
  • 10.5 Research & Academic Institutions
  • 10.6 Other End Users

11 Global AI in Medical Imaging Market, By Geography

  • 11.1 Introduction
  • 11.2 North America
    • 11.2.1 US
    • 11.2.2 Canada
    • 11.2.3 Mexico
  • 11.3 Europe
    • 11.3.1 Germany
    • 11.3.2 UK
    • 11.3.3 Italy
    • 11.3.4 France
    • 11.3.5 Spain
    • 11.3.6 Rest of Europe
  • 11.4 Asia Pacific
    • 11.4.1 Japan
    • 11.4.2 China
    • 11.4.3 India
    • 11.4.4 Australia
    • 11.4.5 New Zealand
    • 11.4.6 South Korea
    • 11.4.7 Rest of Asia Pacific
  • 11.5 South America
    • 11.5.1 Argentina
    • 11.5.2 Brazil
    • 11.5.3 Chile
    • 11.5.4 Rest of South America
  • 11.6 Middle East & Africa
    • 11.6.1 Saudi Arabia
    • 11.6.2 UAE
    • 11.6.3 Qatar
    • 11.6.4 South Africa
    • 11.6.5 Rest of Middle East & Africa

12 Key Developments

  • 12.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 12.2 Acquisitions & Mergers
  • 12.3 New Product Launch
  • 12.4 Expansions
  • 12.5 Other Key Strategies

13 Company Profiling

  • 13.1 Aidoc
  • 13.2 Zebra Medical Vision
  • 13.3 Arterys
  • 13.4 Viz.ai
  • 13.5 Qure.ai
  • 13.6 Siemens Healthineers
  • 13.7 GE HealthCare
  • 13.8 Philips Healthcare
  • 13.9 IBM Watson Health
  • 13.10 NVIDIA
  • 13.11 Microsoft
  • 13.12 RadNet
  • 13.13 Lunit
  • 13.14 HeartFlow
  • 13.15 Enlitic

List of Tables

  • Table 1 Global AI in Medical Imaging Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI in Medical Imaging Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global AI in Medical Imaging Market Outlook, By Software (2024-2032) ($MN)
  • Table 4 Global AI in Medical Imaging Market Outlook, By Standalone AI Tools (2024-2032) ($MN)
  • Table 5 Global AI in Medical Imaging Market Outlook, By Integrated AI Platforms (2024-2032) ($MN)
  • Table 6 Global AI in Medical Imaging Market Outlook, By Services (2024-2032) ($MN)
  • Table 7 Global AI in Medical Imaging Market Outlook, By Deployment & Integration (2024-2032) ($MN)
  • Table 8 Global AI in Medical Imaging Market Outlook, By Training & Support (2024-2032) ($MN)
  • Table 9 Global AI in Medical Imaging Market Outlook, By Consulting (2024-2032) ($MN)
  • Table 10 Global AI in Medical Imaging Market Outlook, By Imaging Modality (2024-2032) ($MN)
  • Table 11 Global AI in Medical Imaging Market Outlook, By X-Ray (2024-2032) ($MN)
  • Table 12 Global AI in Medical Imaging Market Outlook, By Computed Tomography (CT) (2024-2032) ($MN)
  • Table 13 Global AI in Medical Imaging Market Outlook, By Magnetic Resonance Imaging (MRI) (2024-2032) ($MN)
  • Table 14 Global AI in Medical Imaging Market Outlook, By Ultrasound (2024-2032) ($MN)
  • Table 15 Global AI in Medical Imaging Market Outlook, By Positron Emission Tomography (PET) (2024-2032) ($MN)
  • Table 16 Global AI in Medical Imaging Market Outlook, By Single-Photon Emission Computed Tomography (SPECT) (2024-2032) ($MN)
  • Table 17 Global AI in Medical Imaging Market Outlook, By Other Imaging Modalities (2024-2032) ($MN)
  • Table 18 Global AI in Medical Imaging Market Outlook, By Deployment Mode (2024-2032) ($MN)
  • Table 19 Global AI in Medical Imaging Market Outlook, By Cloud-Based (2024-2032) ($MN)
  • Table 20 Global AI in Medical Imaging Market Outlook, By On-Premise (2024-2032) ($MN)
  • Table 21 Global AI in Medical Imaging Market Outlook, By Technology (2024-2032) ($MN)
  • Table 22 Global AI in Medical Imaging Market Outlook, By Deep Learning (2024-2032) ($MN)
  • Table 23 Global AI in Medical Imaging Market Outlook, By Machine Learning (2024-2032) ($MN)
  • Table 24 Global AI in Medical Imaging Market Outlook, By Natural Language Processing (NLP) (2024-2032) ($MN)
  • Table 25 Global AI in Medical Imaging Market Outlook, By Computer Vision (2024-2032) ($MN)
  • Table 26 Global AI in Medical Imaging Market Outlook, By Explainable AI (XAI) in Imaging (2024-2032) ($MN)
  • Table 27 Global AI in Medical Imaging Market Outlook, By Application (2024-2032) ($MN)
  • Table 28 Global AI in Medical Imaging Market Outlook, By Radiology (2024-2032) ($MN)
  • Table 29 Global AI in Medical Imaging Market Outlook, By Oncology (2024-2032) ($MN)
  • Table 30 Global AI in Medical Imaging Market Outlook, By Cardiology (2024-2032) ($MN)
  • Table 31 Global AI in Medical Imaging Market Outlook, By Neurology (2024-2032) ($MN)
  • Table 32 Global AI in Medical Imaging Market Outlook, By Orthopedics (2024-2032) ($MN)
  • Table 33 Global AI in Medical Imaging Market Outlook, By Pulmonology (2024-2032) ($MN)
  • Table 34 Global AI in Medical Imaging Market Outlook, By Other Applications (2024-2032) ($MN)
  • Table 35 Global AI in Medical Imaging Market Outlook, By End User (2024-2032) ($MN)
  • Table 36 Global AI in Medical Imaging Market Outlook, By Hospitals (2024-2032) ($MN)
  • Table 37 Global AI in Medical Imaging Market Outlook, By Diagnostic Imaging Centers (2024-2032) ($MN)
  • Table 38 Global AI in Medical Imaging Market Outlook, By Specialty Clinics (2024-2032) ($MN)
  • Table 39 Global AI in Medical Imaging Market Outlook, By Research & Academic Institutions (2024-2032) ($MN)
  • Table 40 Global AI in Medical Imaging Market Outlook, By Other End Users (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.