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1856984

全球醫療圖像人工智慧市場：預測至2032年－按組件、顯像模式、部署方法、技術、應用、最終用戶和地區進行分析

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個工作天內

價格

根據 Stratistics MRC 的數據，全球用於醫療圖像的AI 市場預計到 2025 年將達到 18.5 億美元，到 2032 年將達到 164.8 億美元，預測期內複合年成長率為 36.6%。

醫療圖像人工智慧（AI）是指應用先進的計算演算法和機器學習技術來分析、解讀和增強醫學影像，包括X光、 電腦斷層掃描、MRI和超音波。 AI系統能夠自動偵測模式並量化異常，有助於放射科醫師更準確、更有效率地診斷疾病。透過利用深度學習模型，AI可以提高影像品質、減少人為誤差，並實現對患者預後的預測分析。它還有助於最佳化工作流程、制定個人化治療方案和更早發現疾病，使醫學影像轉變為更精準、數據主導、以患者為中心的醫療服務。

人工智慧演算法和運算能力的進步

深度學習模型支援對CT、MRI、X光和超音波等多種影像模式中的異常進行自動檢測、分割和分類。 GPU加速和雲端基礎處理技術實現了即時分析，並可在醫院和影像中心進行可擴展部署。與PACS和RIS系統的整合提高了工作流程效率和診斷吞吐量。在高容量、資源受限的環境中，對AI輔助閱片的需求日益成長。這些功能正在推動全球醫療保健系統的平台創新和臨床應用。

與現有系統的整合問題

人工智慧影像處理工具必須與傳統的PACS、EMR和醫院IT系統對接，而這些系統架構和資料標準各不相同。客製化整合計劃會增加成本、延緩實施並降低工作流程的連續性。缺乏標準化的API和資料格式阻礙了跨平台相容性和供應商協作。 IT團隊在混合部署中面臨維護資料完整性、審核和合規性的挑戰。這些限制阻礙了在擁有跨多個地點、基礎設施各異的醫療網路中推廣應用。

對早期準確診斷的需求日益成長

人工智慧模型能夠提高複雜影像資料集中腫瘤、病變和異常檢測的敏感度和特異性。該平台支援分診、優先排序和二次閱片工作流程，從而增強臨床決策能力並減少診斷延誤。與電子健康記錄和臨床決策支援工具的整合，可實現縱向分析和個人化診療。篩檢項目和基於價值的醫療模式對可擴展、可重複的診斷工具的需求日益成長。這一趨勢正在推動人工智慧驅動的診斷成像和精準診斷技術的發展。

缺乏標準化和法律規範

監管機構對人工智慧模型核准、上市後監管和臨床試驗要求的執行方式各不相同。缺乏統一的績效基準和審核通訊協定，使得供應商比較和採購決策變得複雜。醫院和影像中心在評估模型在不同患者群體中的可靠性、偏差和普適性方面面臨諸多挑戰。公共和私人支付方對人工智慧輔助診斷的報銷政策仍不完善。這些風險持續限制平台成熟度和在受監管醫療環境中的臨床整合。

新冠疫情的影響：

疫情加速了人工智慧在醫療圖像的應用，因為醫療系統面臨診斷積壓、人員短缺和感染控制等諸多挑戰。人工智慧工具支援對胸部CT和X光片中的新冠肺炎進行分診和嚴重程度評分。遠距閱片和雲端基礎部署確保了在資源匱乏的偏遠地區也能提供持續的醫療服務。急診和門診對可擴展的自動化影像工作流程的需求激增。疫情後的策略已將人工智慧影像作為診斷韌性和數位醫療基礎設施的核心組成部分。這一轉變強化了對智慧影像處理平台和臨床人工智慧管治的長期投資。

預計深度學習細分市場在預測期內將成為最大的細分市場。

由於深度學習在影像分類、分割和異常檢測方面表現出色，預計在預測期內將佔據最大的市場佔有率。卷積類神經網路和基於Transformer的架構能夠對放射學和病理學影像進行高精度解讀。該平台利用預訓練模型和遷移學習技術，加速在各種臨床環境中的部署。與標註工具和資料湖的整合，實現了模型的持續改進和檢驗。醫院、研究機構和影像設備供應商對可擴展且可解釋的深度學習解決方案的需求日益成長。

預計腫瘤領域在預測期內將達到最高的複合年成長率。

預計在預測期內，腫瘤學領域將實現最高成長率，因為人工智慧平台正不斷擴展應用於癌症篩檢、分期和治療計劃制定。治療模型能夠檢測腫瘤、測量疾病進展並評估乳癌、肺癌、攝護腺癌和大腸癌等癌症的治療反應。與放射組學和基因組學平台的整合支援多模態分析和個人化腫瘤工作流程。公共衛生計畫和腫瘤中心對早期檢測和精準診斷的需求日益成長。在臨床試驗、學術研究和商業應用方面，對人工智慧癌症成像的投資也不斷增加。

佔比最大的地區：

由於北美擁有先進的醫療基礎設施、完善的監管體係以及醫院和影像網路的企業級應用，預計在預測期內，北美將佔據最大的市場佔有率。美國和加拿大的醫療機構正在放射科、病理科和腫瘤科部署人工智慧影像處理平台，以提高診斷準確性和工作流程效率。對雲端基礎設施、資料管治和臨床檢驗的投資，為平台的擴充性和合規性提供了保障。主要供應商、學術中心和監管機構的存在，推動了創新和標準化進程。

複合年成長率最高的地區：

預計亞太地區在預測期內將呈現最高的複合年成長率，這得益於醫療現代化、癌症篩檢計畫和人工智慧政策改革在區域經濟中的整合。中國、印度、日本和韓國等國家正在公立醫院、診斷實驗室和遠端醫療網路中推廣人工智慧影像處理平台。政府支持的舉措正在推動基礎設施投資、新興企業孵化以及在都市區地區檢驗臨床人工智慧。本地供應商提供多語言、具成本效益的解決方案，以滿足區域疾病特徵和合規性需求。服務不足的人口和高流量影像中心對可擴展且易於使用的診斷工具的需求日益成長。這些趨勢正在推動區域人工智慧醫療圖像生態系統的發展。

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目錄

第1章執行摘要

第2章 引言

  • 概述
  • 相關利益者
  • 分析範圍
  • 分析方法
    • 資料探勘
    • 數據分析
    • 數據檢驗
    • 分析方法
  • 分析材料
    • 原始研究資料
    • 二手研究資訊來源
    • 先決條件

第3章 市場趨勢分析

  • 促進要素
  • 抑制因素
  • 市場機遇
  • 威脅
  • 技術分析
  • 應用分析
  • 終端用戶分析
  • 新興市場
  • 新冠疫情的感染疾病

第4章 波特五力分析

  • 供應商的議價能力
  • 買方議價能力
  • 替代產品的威脅
  • 新參與企業的威脅
  • 公司間的競爭

5. 全球醫療圖像人工智慧市場（按組件分類）

  • 軟體
    • 獨立人工智慧工具
    • 整合人工智慧平台
  • 服務
    • 部署和整合
    • 培訓支援
    • 諮詢

6. 全球醫療圖像人工智慧市場（依顯像模式）

  • X光
  • 電腦斷層掃描（CT）
  • 磁振造影（MRI）
  • 超音波
  • 正子斷層掃描（PET）
  • 單光子發射電腦斷層掃描（SPECT）
  • 其他成像方式

7. 全球醫療圖像人工智慧市場依部署模式分類

  • 雲端基礎的
  • 本地部署

8. 全球醫療圖像人工智慧市場（按技術分類）

  • 深度學習（DL）
  • 機器學習（ML）
  • 自然語言處理（NLP）
  • 電腦視覺
  • 圖像可解釋人工智慧 (XAI)

9. 全球人工智慧醫療圖像市場（按應用領域分類）

  • 放射科
  • 瘤
  • 心臟病
  • 神經病學
  • 整形外科
  • 呼吸系統醫療設備
  • 其他用途

第10章 全球醫療圖像人工智慧市場（按最終用戶分類）

  • 醫院
  • 診斷影像中心
  • 專科診所
  • 研究和學術機構
  • 其他最終用戶

第11章 全球醫療圖像人工智慧市場（按地區分類）

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 其他亞太地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地區
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲地區

第12章：主要趨勢

  • 合約、商業夥伴關係和合資企業
  • 企業合併（M&A）
  • 新產品發布
  • 業務拓展
  • 其他關鍵策略

第13章：公司簡介

  • 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.