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市场调查报告书
商品编码
1880433

人工智慧驱动的成像工作流程平台市场预测至2032年：按组件、模式、应用、最终用户和地区分類的全球分析

AI-Powered Imaging Workflow Platforms Market Forecasts to 2032 - Global Analysis By Component, Modality, 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 的一项研究，全球 AI 驱动的影像工作流程平台市场预计到 2025 年将达到 11 亿美元，到 2032 年将达到 78 亿美元，在预测期内的复合年增长率为 33%。

人工智慧驱动的影像工作流程平台是软硬体一体化的解决方案，利用人工智慧技术来管理、分析和解读医学影像资料。这些平台能够自动执行诸如排班、影像路由、异常检测和初步报告生成等任务，从而提高筛检效率和诊断准确性。它们可以帮助放射科医生和临床医生优先处理紧急病例，减轻行政工作量，并增强放射学和病理学领域的临床决策能力。

根据国际清算银行（BIS）的说法，分析多家银行交易模式的联盟人工智慧模型在检测复杂的跨机构支付诈骗要有效得多。

对更有效率的放射科工作流程的需求日益增长

放射科面临的日益增长的扫描量压力，正推动人工智慧工作流程平台的普及。医院正在寻求自动化分流、更快的影像路由和智慧化的工作负载平衡，以消除瓶颈并提高病患吞吐量。人工智慧工具可以缩短阅片时间、标记紧急病例，并与PACS/RIS平台无缝整合。随着放射科医师职业倦怠和人员短缺问题的日益严重，工作流程自动化已成为提升整个医学影像生态系统效率、增强营运韧性和确保诊断一致性的关键。

不透明的人工智慧决策模型会降低临床医生的信任度。

人工智慧决策流程可解释性不足是其应用的关键阻碍因素。这些过程通常如同“黑箱”，降低了临床医生对自动化建议的信任。放射科医生需要透明的证据链、可解释的输出结果以及检验的推理过程，才能安全地将人工智慧融入他们的诊断流程中。监管机构日益重视可解释性，并透过增加额外的检验层级来减缓人工智慧的普及应用。如果没有一个健全的可解释性框架，人工智慧工作流程平台将面临临床相关人员的质疑，尤其是在高风险的诊断环境中，课责和准确性至关重要。

多模态诊断整合

将包括影像、病理、基因组学和临床记录在内的多模态诊断数据整合到由人工智慧驱动的单一工作流程层中，蕴藏着巨大的机会。这种融合能够实现全面的诊断推断，使平台能够提供更丰富、更具情境感知的洞察。多模态整合有助于早期疾病检测，提高分诊准确性，并支持个人化治疗方案。随着医疗保健朝着一体化诊断​​生态系统发展，能够整合多样化资料流的人工智慧解决方案至关重要，这也推动了对下一代影像工作流程平台的需求。

快速演算法过时

随着影像技术、成像通讯协定和临床标准的快速发展，许多人工智慧模型的重新训练速度远不及演算法更新换代的速度，演算法的快速过时构成了严重的威胁。过时的演算法会导致效能下降、漏诊异常以及偏差漂移，从而损害临床信任。供应商必须持续投资于资料集更新、监管检验和自适应学习基础设施。未能及时更新演算法将导致竞争劣势和平台可靠性下降，尤其对于那些寻求能够持续优化性能、面向未来的人工智慧系统的医院而言更是如此。

新冠疫情的影响：

新冠疫情加速了放射科服务的数位化，并显着推动了人工智慧工作流程平台的应用，以满足诊断影像需求的激增和现场人员减少的情况。人工智慧辅助的胸部CT和X光片分诊对于快速评估新冠病情严重程度至关重要，并简化了临床决策流程。远端阅片和云端基础的影像共用迅速发展，进一步巩固了人们对自动化工作流程的长期兴趣。疫情最终凸显了人工智慧驱动的效率提升价值，并巩固了这些平台作为后疫情时代放射科诊疗实践中不可或缺的工具的地位。

预计在预测期内，软体平台细分市场将占据最大的市场份额。

由于人工智慧引擎的广泛应用，软体平台预计将占据最大的市场份额。这些引擎能够自动完成分诊、影像优先排序、报告结构化和工作流程协调等工作。医院正越来越多地采用与现有PACS/RIS系统对接的集中式平台，以最大限度地减少营运中断。这些解决方案提供持续升级、可扩展的处理能力和跨模态相容性，构成了数位放射线生态系统的基础。它们在整个诊断流程中的多功能性进一步巩固了其在全球市场的领先地位。

预计在预测期内，MRI细分市场将实现最高的复合年增长率。

在预测期内，受缩短扫描时间和优化阅片流程需求的不断增长的推动，磁振造影（MRI）领域预计将实现最高成长率。人工智慧平台透过自动化通讯协定选择、降噪、分割和定量分析来提高MRI的吞吐量。 MRI在神经病学、肿瘤学和肌肉骨骼疾病领域的日益普及，推动了对人工智慧辅助工具的需求。人工智慧驱动的MRI加速和重建演算法进一步促进了其应用，使MRI成为工作流程平台使用者群体成长最快的领域。

占比最大的地区：

预计亚太地区将在预测期内占据最大的市场份额。这主要得益于诊断成像基础设施的快速扩张、患者群体的不断增长以及政府对人工智慧驱动的医疗现代化的大力支持。中国、日本、韩国和印度等国家正大力投资建造智慧医院并数位化转型。人工智慧创新中心的蓬勃发展以及云端基础成像平台的日益普及进一步巩固了该地区的领先地位。这些因素共同推动了亚太地区医疗系统工作流程自动化技术的快速普及。

预计年复合成长率最高的地区：

在预测期内，北美预计将呈现最高的复合年增长率，这主要得益于该地区早期采用先进的放射学资讯技术系统、健全的人工智慧检验法规结构以及成熟的医院数位化。主要人工智慧开发商的存在、对临床自动化的巨额投资以及与PACS/RIS生态系统的广泛集成，都在推动着这一成长。此外，对工作流程效率的日益重视、放射科医生的短缺以及人工智慧辅助成像报销途径的不断扩大，也进一步推动了该地区市场的快速扩张。

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目录

第一章执行摘要

第二章 前言

  • 概述
  • 相关利益者
  • 调查范围
  • 调查方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 研究途径
  • 研究材料
    • 原始研究资料
    • 次级研究资讯来源
    • 先决条件

第三章 市场趋势分析

  • 介绍
  • 司机
  • 抑制因素
  • 机会
  • 威胁
  • 应用分析
  • 终端用户分析
  • 新兴市场
  • 新冠疫情的影响

第四章 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
  • 竞争对手之间的竞争

5. 人工智慧影像工作流程平台市场（按组件划分）

  • 介绍
  • 软体平台
  • 人工智慧分析模组
  • 工作流程优化引擎
  • 报告和视觉化工具
  • 整合和中介软体解决方案

6. 依成像方式分類的AI驱动成像工作流程平台市场

  • 介绍
  • MRI
  • CT
  • 超音波
  • X射线和透视
  • PET和核子医学影像

7. 按应用分類的AI赋能影像工作流程平台市场

  • 介绍
  • 诊断工作流程自动化
  • 临床决策支持
  • 影像分诊和优先排序
  • 品管和减少错误
  • 放射学报告优化

8. 全球人工智慧驱动的影像工作流程平台市场（按最终用户划分）

  • 介绍
  • 医院
  • 诊断中心
  • 研究所
  • 人工智慧健康科技公司
  • 放射学团体和网络

9. 全球人工智慧驱动的影像工作流程平台市场（按地区划分）

  • 介绍
  • 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
    • 亚太其他地区
  • 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

第十章：重大进展

  • 协议、伙伴关係、合作和合资企业
  • 收购与併购
  • 新产品上市
  • 业务拓展
  • 其他关键策略

第十一章 企业概况

  • Siemens Healthineers
  • GE HealthCare
  • Philips
  • IBM
  • Nuance
  • Viz.ai
  • Aidoc
  • Zebra Medical Vision
  • Arterys
  • Agfa Healthcare
  • Qure.ai
  • Canon Medical
  • Fujifilm
  • Riverain Technologies
  • Imagen Technologies
  • Butterfly Network
Product Code: SMRC32472

According to Stratistics MRC, the Global AI-Powered Imaging Workflow Platforms Market is accounted for $1.1 billion in 2025 and is expected to reach $7.8 billion by 2032 growing at a CAGR of 33% during the forecast period. AI-powered imaging workflow platforms are integrated software and hardware solutions that use artificial intelligence to manage, analyze, and interpret medical imaging data. These platforms automate tasks such as scheduling, image routing, anomaly detection, and preliminary report generation, improving screening efficiency and diagnostic accuracy. They help radiologists and clinicians prioritize urgent cases, reduce administrative workload, and enhance clinical decision-making in radiology and pathology.

According to the Bank for International Settlements, consortium-based AI models that analyze transaction patterns across multiple banks are significantly more effective at detecting sophisticated, cross-institutional payment fraud.

Market Dynamics:

Driver:

Rising demand to streamline radiology workflows

Rising pressure on radiology departments to manage increasing scan volumes is driving strong adoption of AI-powered workflow platforms. Hospitals seek automated triage, faster image routing, and intelligent workload balancing to reduce bottlenecks and improve patient throughput. AI tools accelerate reading times, flag urgent cases, and integrate seamlessly with PACS/RIS platforms. As radiologists face rising burnout and staffing shortages, workflow automation becomes a mission-critical enabler of efficiency, operational resilience, and diagnostic consistency across medical imaging ecosystems.

Restraint:

Opaque AI decision models limiting clinician trust

A key restraint is the limited interpretability of AI decision pathways, which often function as "black boxes," reducing clinician confidence in automated recommendations. Radiologists require transparent evidence trails, explainable outputs, and validated reasoning to integrate AI into diagnostic routines safely. Regulatory bodies increasingly emphasize explainability, adding additional validation layers that slow adoption. Without robust interpretability frameworks, AI workflow platforms face hesitation from clinical stakeholders, especially in high-stakes diagnostic environments where accountability and accuracy are paramount.

Opportunity:

Integration of multimodal diagnostics

A major opportunity lies in unifying multimodal diagnostic data-integrating imaging, pathology, genomics, and clinical records into a single AI-powered workflow layer. This fusion enables holistic diagnostic reasoning, allowing platforms to deliver richer, more context-aware insights. Multimodal integration improves early disease detection, enhances triage precision, and supports personalized care pathways. As healthcare shifts toward unified diagnostic ecosystems, AI solutions capable of synthesizing diverse data streams become essential, driving demand for next-generation imaging workflow platforms.

Threat:

Rapid algorithm obsolescence

Rapid algorithm obsolescence poses a growing threat as imaging technologies, acquisition protocols, and clinical standards evolve faster than many AI models can be retrained. Outdated algorithms risk performance degradation, missed anomalies, or bias drift, eroding clinical trust. Vendors must invest continuously in dataset updates, regulatory revalidations, and adaptive learning infrastructures. Failure to maintain algorithm currency can result in competitive displacement and reduced platform reliability, especially in hospitals seeking future-proof AI systems with continuous performance optimization.

Covid-19 Impact:

COVID-19 accelerated the digitization of radiology services, significantly boosting adoption of AI workflow platforms to manage surging imaging demands and reduced onsite staffing. AI-enabled triage for chest CTs and X-rays became critical for rapid COVID severity assessment, streamlining clinical decision-making. Remote reading and cloud-based imaging collaboration expanded sharply, reinforcing long-term interest in automated workflows. The pandemic ultimately highlighted the value of AI-driven efficiency, cementing these platforms as essential tools in post-pandemic radiology operations.

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

The software platforms segment is expected to command the largest market share, resulting from widespread deployment of AI engines that automate triage, image prioritization, report structuring, and workflow orchestration. Hospitals increasingly adopt centralized platforms that integrate with existing PACS/RIS systems, minimizing operational disruption. These solutions provide continuous upgrades, scalable processing, and cross-modality compatibility, making them foundational to digital radiology ecosystems. Their versatility across diagnostic pathways further reinforces their leadership in the global market.

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

Over the forecast period, the MRI segment is predicted to witness the highest growth rate, propelled by the rising need to accelerate long scan times and optimize interpretation workflows. AI platforms enhance MRI throughput by automating protocol selection, noise reduction, segmentation, and quantitative analysis. As MRI usage grows in neurology, oncology, and musculoskeletal care, demand for AI support tools intensifies. AI-driven MRI acceleration and reconstruction algorithms further stimulate adoption, positioning this modality as the fastest-growing user base for workflow platforms.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share, attributed to rapid expansion of diagnostic imaging infrastructure, rising patient volumes, and strong government support for AI-driven healthcare modernization. Countries such as China, Japan, South Korea, and India are investing heavily in smart hospitals and radiology digitization. Growing AI innovation hubs and increasing adoption of cloud-based imaging platforms reinforce the region's dominance. These factors collectively accelerate deployment of workflow automation technologies across APAC healthcare systems.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR associated with early adoption of advanced radiology IT systems, strong regulatory frameworks for AI validation, and mature hospital digitization. The presence of leading AI developers, substantial investment in clinical automation, and widespread integration with PACS/RIS ecosystems accelerates growth. Rising focus on workflow efficiency, shortage of radiologists, and expanding reimbursement pathways for AI-supported imaging further support rapid market expansion in the region.

Key players in the market

Some of the key players in AI-Powered Imaging Workflow Platforms Market include Siemens Healthineers, GE HealthCare, Philips, IBM, Nuance, Viz.ai, Aidoc, Zebra Medical Vision, Arterys, Agfa Healthcare, Qure.ai, Canon Medical, Fujifilm, Riverain Technologies, Imagen Technologies, and Butterfly Network.

Key Developments:

In August 2025, GE HealthCare introduced the Edison Workflow Orchestrator, a vendor-agnostic platform that uses predictive AI to allocate reading assignments across a radiology department in real-time based on radiologist subspecialty, current workload, and exam complexity, reducing report turnaround times by over 20%.

In July 2025, Viz.ai received FDA clearance for its Viz TAVR platform, which uses AI to automatically analyze CT scans for structural heart disease, identify eligible patients for Transcatheter Aortic Valve Replacement (TAVR), and instantly notify the heart team, streamlining the pre-procedural workflow.

In June 2025, Philips announced the Enterprise Radiology Performance Suite, a cloud-native platform that leverages AI to provide health systems with a real-time dashboard of key performance indicators (KPIs), predicting bottlenecks and recommending resource shifts to optimize departmental efficiency.

Components Covered:

  • Software Platforms
  • AI Analytics Modules
  • Workflow Optimization Engines
  • Reporting & Visualization Tools
  • Integration & Middleware Solutions

Modalities Covered:

  • MRI
  • CT
  • Ultrasound
  • X-Ray & Fluoroscopy
  • PET & Nuclear Imaging

Applications Covered:

  • Diagnostic Workflow Automation
  • Clinical Decision Support
  • Image Triage & Prioritization
  • Quality Control & Error Reduction
  • Radiology Reporting Optimization

End Users Covered:

  • Hospitals
  • Diagnostic Centers
  • Research Institutions
  • AI Health-Tech Companies
  • Radiology Groups & Networks

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 Application Analysis
  • 3.7 End User Analysis
  • 3.8 Emerging Markets
  • 3.9 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-Powered Imaging Workflow Platforms Market, By Component

  • 5.1 Introduction
  • 5.2 Software Platforms
  • 5.3 AI Analytics Modules
  • 5.4 Workflow Optimization Engines
  • 5.5 Reporting & Visualization Tools
  • 5.6 Integration & Middleware Solutions

6 Global AI-Powered Imaging Workflow Platforms Market, By Modality

  • 6.1 Introduction
  • 6.2 MRI
  • 6.3 CT
  • 6.4 Ultrasound
  • 6.5 X-Ray & Fluoroscopy
  • 6.6 PET & Nuclear Imaging

7 Global AI-Powered Imaging Workflow Platforms Market, By Application

  • 7.1 Introduction
  • 7.2 Diagnostic Workflow Automation
  • 7.3 Clinical Decision Support
  • 7.4 Image Triage & Prioritization
  • 7.5 Quality Control & Error Reduction
  • 7.6 Radiology Reporting Optimization

8 Global AI-Powered Imaging Workflow Platforms Market, By End User

  • 8.1 Introduction
  • 8.2 Hospitals
  • 8.3 Diagnostic Centers
  • 8.4 Research Institutions
  • 8.5 AI Health-Tech Companies
  • 8.6 Radiology Groups & Networks

9 Global AI-Powered Imaging Workflow Platforms Market, By Geography

  • 9.1 Introduction
  • 9.2 North America
    • 9.2.1 US
    • 9.2.2 Canada
    • 9.2.3 Mexico
  • 9.3 Europe
    • 9.3.1 Germany
    • 9.3.2 UK
    • 9.3.3 Italy
    • 9.3.4 France
    • 9.3.5 Spain
    • 9.3.6 Rest of Europe
  • 9.4 Asia Pacific
    • 9.4.1 Japan
    • 9.4.2 China
    • 9.4.3 India
    • 9.4.4 Australia
    • 9.4.5 New Zealand
    • 9.4.6 South Korea
    • 9.4.7 Rest of Asia Pacific
  • 9.5 South America
    • 9.5.1 Argentina
    • 9.5.2 Brazil
    • 9.5.3 Chile
    • 9.5.4 Rest of South America
  • 9.6 Middle East & Africa
    • 9.6.1 Saudi Arabia
    • 9.6.2 UAE
    • 9.6.3 Qatar
    • 9.6.4 South Africa
    • 9.6.5 Rest of Middle East & Africa

10 Key Developments

  • 10.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 10.2 Acquisitions & Mergers
  • 10.3 New Product Launch
  • 10.4 Expansions
  • 10.5 Other Key Strategies

11 Company Profiling

  • 11.1 Siemens Healthineers
  • 11.2 GE HealthCare
  • 11.3 Philips
  • 11.4 IBM
  • 11.5 Nuance
  • 11.6 Viz.ai
  • 11.7 Aidoc
  • 11.8 Zebra Medical Vision
  • 11.9 Arterys
  • 11.10 Agfa Healthcare
  • 11.11 Qure.ai
  • 11.12 Canon Medical
  • 11.13 Fujifilm
  • 11.14 Riverain Technologies
  • 11.15 Imagen Technologies
  • 11.16 Butterfly Network

List of Tables

  • Table 1 Global AI-Powered Imaging Workflow Platforms Market Outlook, By Region (2024-2032) ($MN) HeartFlow
  • Table 2 Global AI-Powered Imaging Workflow Platforms Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global AI-Powered Imaging Workflow Platforms Market Outlook, By Software Platforms (2024-2032) ($MN) Sectra
  • Table 4 Global AI-Powered Imaging Workflow Platforms Market Outlook, By AI Analytics Modules (2024-2032) ($MN)
  • Table 5 Global AI-Powered Imaging Workflow Platforms Market Outlook, By Workflow Optimization Engines (2024-2032) ($MN)
  • Table 6 Global AI-Powered Imaging Workflow Platforms Market Outlook, By Reporting & Visualization Tools (2024-2032) ($MN)
  • Table 7 Global AI-Powered Imaging Workflow Platforms Market Outlook, By Integration & Middleware Solutions (2024-2032) ($MN)
  • Table 8 Global AI-Powered Imaging Workflow Platforms Market Outlook, By Modality (2024-2032) ($MN)
  • Table 9 Global AI-Powered Imaging Workflow Platforms Market Outlook, By MRI (2024-2032) ($MN)
  • Table 10 Global AI-Powered Imaging Workflow Platforms Market Outlook, By CT (2024-2032) ($MN)
  • Table 11 Global AI-Powered Imaging Workflow Platforms Market Outlook, By Ultrasound (2024-2032) ($MN)
  • Table 12 Global AI-Powered Imaging Workflow Platforms Market Outlook, By X-Ray & Fluoroscopy (2024-2032) ($MN)
  • Table 13 Global AI-Powered Imaging Workflow Platforms Market Outlook, By PET & Nuclear Imaging (2024-2032) ($MN)
  • Table 14 Global AI-Powered Imaging Workflow Platforms Market Outlook, By Application (2024-2032) ($MN)
  • Table 15 Global AI-Powered Imaging Workflow Platforms Market Outlook, By Diagnostic Workflow Automation (2024-2032) ($MN)
  • Table 16 Global AI-Powered Imaging Workflow Platforms Market Outlook, By Clinical Decision Support (2024-2032) ($MN)
  • Table 17 Global AI-Powered Imaging Workflow Platforms Market Outlook, By Image Triage & Prioritization (2024-2032) ($MN)
  • Table 18 Global AI-Powered Imaging Workflow Platforms Market Outlook, By Quality Control & Error Reduction (2024-2032) ($MN)
  • Table 19 Global AI-Powered Imaging Workflow Platforms Market Outlook, By Radiology Reporting Optimization (2024-2032) ($MN)
  • Table 20 Global AI-Powered Imaging Workflow Platforms Market Outlook, By End User (2024-2032) ($MN)
  • Table 21 Global AI-Powered Imaging Workflow Platforms Market Outlook, By Hospitals (2024-2032) ($MN)
  • Table 22 Global AI-Powered Imaging Workflow Platforms Market Outlook, By Diagnostic Centers (2024-2032) ($MN)
  • Table 23 Global AI-Powered Imaging Workflow Platforms Market Outlook, By Research Institutions (2024-2032) ($MN)
  • Table 24 Global AI-Powered Imaging Workflow Platforms Market Outlook, By AI Health-Tech Companies (2024-2032) ($MN)
  • Table 25 Global AI-Powered Imaging Workflow Platforms Market Outlook, By Radiology Groups & Networks (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.