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市場調查報告書
商品編碼
2007891

人工智慧醫學影像市場預測至2034年—按成像方式、部署模式、技術、應用、最終用戶和地區分類的全球分析

AI Medical Imaging Market Forecasts to 2034 - Global Analysis By Modality, Deployment Mode, Technology, Application, End User and Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，預計到 2026 年，全球 AI 醫學影像市場規模將達到 56 億美元，並在預測期內以 22.7% 的複合年成長率成長，到 2034 年將達到 289 億美元。

人工智慧驅動的醫學影像是指將機器學習演算法、深度神經網路和電腦視覺系統應用於醫學診斷影像（例如X光片、電腦斷層掃描（CT）、磁振造影（MRI）、超音波、核子醫學和乳房X光片）的自動化分析、解讀和影像增強。這些系統能夠偵測解剖結構異常、分割病灶區域、最佳化放射科醫師的工作優先順序、縮短掃描採集時間並產生結構化的診斷報告。目前，這些系統已在腫瘤科、循環系統、神經科、呼吸內科和整形外科等醫院和門診影像環境中得到應用。

放射科醫師短缺及其工作壓力巨大。

放射科醫生短缺和影像檢查數量激增給工作流程帶來了巨大壓力，但人力智慧醫學影像解決方案透過自動化常規影像分流、異常標記和報告，正在應對這一挑戰。在大多數大型醫療系統中，影像檢查數量的成長速度超過了放射科醫生數量的成長速度，導致檢查瓶頸，而人工智慧優先排序工具可以很大程度上解決這個問題。醫療系統管理者正在積極採用人工智慧影像解決方案來提高人員效率，這為醫學影像人工智慧平台供應商帶來了持續的軟體訂閱收入。

對演算法偏差和普適性的擔憂

演算法偏差和泛化能力的限制是臨床應用的一大障礙。基於人口統計偏差資料集訓練的人工智慧醫學影像模型，在應用於訓練群組中被低估的患者群體時表現不佳。放射科管理者在決定實施前，越來越傾向於尋求在不同病患群體中進行外部檢驗的證據。監管機構對人工智慧模型在不同種族、年齡和性別等亞群體中的表現監管力度不斷加大，這要求影像人工智慧開發人員投入更多資源進行超越標準臨床表現基準的廣泛檢驗研究。

新興市場的放射學基礎設施

新興市場放射科基礎設施的差異為人工智慧驅動的醫學影像平台帶來了變革性的成長機遇，這些平台能夠將診斷範圍擴展到專科醫生集中的都市區之外。人工智慧影像解讀工具使農村醫療機構的非專科臨床醫生能夠獲得與放射科醫生相當的常見疾病診斷結果。印度、東南亞和撒哈拉以南非洲的政府遠端醫療和數位健康基礎設施項目正在將人工智慧成像功能融入基層醫療服務拓展舉措，從而創造一個巨大的潛在市場。

責任和臨床責任方面缺乏明確性

人工智慧驅動的醫學影像診斷結果的責任歸屬和臨床責任分割不清，對人工智慧的普及應用構成系統性威脅。這是因為相關法規和法律體制並未明確界定人工智慧診斷錯誤導致患者預後不良時，應由誰來負責。放射科醫師和醫院風險負責人對在缺乏獨立臨床檢驗的情況下完全依賴人工智慧輸出結果持抵制態度，導致人工智慧的自主部署僅限於輔助功能。此外，醫療事故保險對人工智慧輔助診斷的覆蓋不足，進一步加劇了機構在加速採用人工智慧過程中面臨的風險評估難度。

新冠疫情的影響：

新冠疫情加速了人工智慧在醫學影像領域的應用，展現了其在以應對疫情為導向的放射科室中的快速價值，例如，用於檢測新冠肺炎的胸部CT和X光人工智慧工具已獲得緊急監管核准。疫情期間工作流程自動化的先例鞏固了人工智慧影像輔助工具在醫院通訊協定的應用。疫情後，隨著醫療系統將人工智慧分診工具永久應用於呼吸系統疾病、腫瘤篩檢和心血管影像等領域，人工智慧影像平台的應用正在加速推進。

在預測期內，核醫學影像領域預計將佔據最大的市場佔有率。

在預測期內，核醫學影像領域預計將佔據最大的市場佔有率。這主要歸功於PET-CT和SPECT影像技術在腫瘤分期、心臟灌注評估和神經退化性疾病診斷等領域的臨床應用日益廣泛。人工智慧（AI）技術與核醫學影像的融合，實現了病灶的自動定量、最佳化的衰減校正以及示蹤劑量的降低。越來越多的臨床證據表明，人工智慧增強的核子醫學掃描術診斷在癌症早期檢測方面具有較高的準確性，這促使轉診醫生更廣泛地應用該技術，並加速了影像中心設備的升級換代。

在預測期內，基於雲端的細分市場預計將呈現最高的複合年成長率。

在預測期內，雲端解決方案預計將呈現最高的成長率，這主要得益於醫療系統對可擴展人工智慧推理能力的需求，他們希望在無需對本地GPU基礎設施進行大量資本投資的情況下獲得此類解決方案。雲端託管的人工智慧醫學影像平台支援多站點部署、持續模型更新以及跨機構資料聚合，從而實現模型的持續改進。領先的雲端服務供應商正在建立專用的醫學影像人工智慧基礎設施和市場生態系統，以降低醫院IT部門部署人工智慧診斷工具的整合門檻。

市佔率最大的地區：

在預測期內，北美預計將佔據最大的市場佔有率，這得益於其先進的人工智慧醫學影像研究基礎設施、高診斷影像利用率以及強大的FDA已通過核准人工智慧影像產品產品系列。美國擁有全球最大的人工智慧已通過核准醫學影像設備部署基地。強大的先進診斷程序報銷機制以及由GE醫療和西門子醫療等公司支持的積極醫院人工智慧應用計劃，鞏固了該地區的領先地位。

複合年成長率最高的地區：

在預測期內，亞太地區預計將呈現最高的複合年成長率，這主要得益於診斷成像基礎設施投資的快速成長、政府主導的人工智慧醫療發展項目，以及大量尚未開發的患者群體（他們將受益於人工智慧遠距放射學）。中國國家藥品監督管理局（NMPA）已建立人工智慧醫療設備的快速核准流程，加速了人工智慧影像產品在國內外市場的核准。日本和韓國的先進影像設備製造生態系統正在將人工智慧功能整合到其所有產品線中。

免費客製化服務：

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  • 企業概況
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  • 區域細分
    • 應客戶要求，我們提供主要國家和地區的市場估算和預測，以及複合年成長率（註：需進行可行性檢查）。
  • 競爭性標竿分析
    • 根據產品系列、地理覆蓋範圍和策略聯盟對主要企業進行基準分析。

目錄

第1章執行摘要

  • 市場概覽及主要亮點
  • 成長動力、挑戰與機遇
  • 競爭格局概述
  • 戰略洞察與建議

第2章：研究框架

  • 研究目標和範圍
  • 相關人員分析
  • 研究假設和限制
  • 調查方法

第3章 市場動態與趨勢分析

  • 市場定義與結構
  • 主要市場促進因素
  • 市場限制與挑戰
  • 投資成長機會和重點領域
  • 產業威脅與風險評估
  • 技術與創新展望
  • 新興市場/高成長市場
  • 監管和政策環境
  • 新冠疫情的影響及復甦前景

第4章：競爭環境與策略評估

  • 波特五力分析
    • 供應商的議價能力
    • 買方的議價能力
    • 替代品的威脅
    • 新進入者的威脅
    • 競爭公司之間的競爭
  • 主要企業市佔率分析
  • 產品基準評效和效能比較

第5章：全球人工智慧醫學影像市場：以影像方式分類

  • X光影像診斷
  • 電腦斷層掃描（CT）
  • 磁振造影（MRI）
  • 超音波影像
  • 核子醫學掃描術診斷
  • 乳房X光檢查
  • 其他方式

第6章：全球人工智慧醫療影像市場：依部署模式分類

  • 基於雲端的
  • 現場
  • 混合
  • 網路為基礎的平台
  • SaaS模式
  • 整合系統

第7章 全球人工智慧醫療影像市場：按技術分類

  • 深度學習
  • 機器學習
  • 自然語言處理
  • 電腦視覺
  • 基於雲端的人工智慧
  • 邊緣人工智慧

第8章：全球人工智慧醫療影像市場：按應用領域分類

  • 腫瘤影像診斷
  • 心臟影像
  • 神經影像學
  • 呼吸影像
  • 整形外科影像診斷
  • 消化系統成像
  • 其他用途

第9章：全球人工智慧醫療影像市場：按最終用戶分類

  • 醫院
  • 診斷影像中心
  • 研究機構
  • 門診手術中心
  • 遠端放射診斷服務供應商
  • 大學醫院
  • 其他最終用戶

第10章：全球人工智慧醫療影像市場：按地區分類

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 義大利
    • 西班牙
    • 荷蘭
    • 比利時
    • 瑞典
    • 瑞士
    • 波蘭
    • 其他歐洲國家
  • 亞太地區
    • 中國
    • 日本
    • 印度
    • 韓國
    • 澳洲
    • 印尼
    • 泰國
    • 馬來西亞
    • 新加坡
    • 越南
    • 其他亞太國家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥倫比亞
    • 智利
    • 秘魯
    • 其他南美國家
  • 世界其他地區（RoW）
    • 中東
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 卡達
      • 以色列
      • 其他中東國家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲國家

第11章 策略市場資訊

  • 工業價值網路和供應鏈評估
  • 空白區域和機會地圖
  • 產品演進與市場生命週期分析
  • 通路、經銷商和打入市場策略的評估

第12章 產業趨勢與策略舉措

  • 併購
  • 夥伴關係、聯盟、合資企業
  • 新產品發布和認證
  • 擴大生產能力和投資
  • 其他策略舉措

第13章：公司簡介

  • GE Healthcare
  • Siemens Healthineers
  • Philips Healthcare
  • Canon Medical Systems Corporation
  • IBM Watson Health
  • Aidoc Medical Ltd.
  • Zebra Medical Vision
  • Arterys Inc.
  • Viz.ai, Inc.
  • Enlitic, Inc.
  • Qure.ai
  • Lunit Inc.
  • Butterfly Network, Inc.
  • Tempus Labs
  • NVIDIA Corporation
  • Fujifilm Holdings Corporation
  • Samsung Medison
  • Agfa-Gevaert Group
Product Code: SMRC34766

According to Stratistics MRC, the Global AI Medical Imaging Market is accounted for $5.6 billion in 2026 and is expected to reach $28.9 billion by 2034 growing at a CAGR of 22.7% during the forecast period. AI medical imaging refers to the application of machine learning algorithms, deep neural networks, and computer vision systems to the automated analysis, interpretation, and enhancement of medical diagnostic images including X-rays, computed tomography scans, magnetic resonance imaging, ultrasound, nuclear medicine, and mammography outputs. These systems detect anatomical anomalies, segment pathological regions, prioritize radiologist worklists, reduce scan acquisition times, and generate structured diagnostic reports. They are deployed in oncology, cardiology, neurology, pulmonology, and orthopedic imaging workflows across hospital and outpatient imaging settings.

Market Dynamics:

Driver:

Radiologist Shortage and Workload Pressure

Radiologist shortage and escalating imaging study volumes are creating acute workflow pressure that AI medical imaging solutions address by automating routine image triage, anomaly flagging, and report generation. Diagnostic imaging volumes are growing faster than radiologist workforce expansion in most major healthcare systems, generating backlogs that AI prioritization tools can materially compress. Health system administrators are actively procuring AI imaging solutions as workforce productivity tools, establishing recurring software subscription revenue streams for medical imaging AI platform vendors.

Restraint:

Algorithm Bias and Generalizability Concerns

Algorithm bias and generalizability limitations present clinical adoption barriers as AI medical imaging models trained on demographically narrow datasets demonstrate performance degradation when applied to patient populations underrepresented in training cohorts. Radiology department administrators are increasingly demanding external validation evidence across diverse patient demographics before procurement commitment. Regulatory scrutiny of AI model performance across racial, age, and gender subgroups is intensifying, requiring extensive validation study investment from imaging AI developers beyond standard clinical performance benchmarks.

Opportunity:

Emerging Market Radiology Infrastructure

Emerging market radiology infrastructure gaps present a transformative growth opportunity for AI medical imaging platforms that can extend diagnostic coverage beyond specialist-concentrated urban centers. AI-powered reading tools enable non-specialist clinicians in rural health facilities to access radiologist-equivalent diagnostic interpretation for common conditions. Government telemedicine and digital health infrastructure programs in India, Southeast Asia, and Sub-Saharan Africa are integrating AI imaging capabilities into primary care expansion initiatives, creating substantial new addressable market volumes.

Threat:

Liability and Clinical Responsibility Ambiguity

Liability and clinical responsibility ambiguity for AI-generated medical imaging interpretations represents a systemic threat to adoption, as regulatory and legal frameworks have not definitively established accountability when AI diagnostic errors contribute to adverse patient outcomes. Radiologists and hospital risk managers express institutional reluctance to fully rely on AI outputs without independent clinical verification, limiting autonomous AI deployment beyond assistive functions. Medical malpractice insurance policy gaps for AI-assisted diagnostics further compound institutional risk calculus against accelerated adoption.

Covid-19 Impact:

COVID-19 catalyzed AI medical imaging adoption as chest CT and X-ray AI tools for COVID-19 pneumonia detection received emergency regulatory approvals, demonstrating rapid value in overwhelmed radiology departments. Pandemic-era workflow automation precedents normalized AI imaging assistant integration in hospital protocols. Post-pandemic, AI imaging platform procurement has accelerated as health systems permanently incorporate AI triage tools for respiratory pathology, oncology screening, and cardiovascular imaging.

The nuclear imaging segment is expected to be the largest during the forecast period

The nuclear imaging segment is expected to account for the largest market share during the forecast period, due to increasing clinical adoption of PET-CT and SPECT imaging for oncology staging, cardiac perfusion assessment, and neurodegenerative disease diagnosis. AI integration with nuclear imaging enables automated lesion quantification, attenuation correction optimization, and reduced tracer dosing protocols. Growing clinical evidence supporting AI-enhanced nuclear imaging accuracy in early cancer detection is expanding referring physician utilization and driving imaging center equipment upgrade cycles.

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

Over the forecast period, the cloud-based segment is predicted to witness the highest growth rate, driven by health system demand for scalable AI inference capacity without capital-intensive on-premise GPU infrastructure investment. Cloud-hosted AI medical imaging platforms enable multi-site deployment, continuous model update delivery, and cross-institutional data aggregation for ongoing model improvement. Major cloud providers are building dedicated medical imaging AI infrastructure and marketplace ecosystems that reduce integration barriers for hospital IT departments adopting AI diagnostic tools.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, due to leading AI medical imaging research infrastructure, high diagnostic imaging utilization rates, and substantial FDA-cleared AI imaging product portfolios. The U.S. hosts the largest installed base of medical imaging AI-cleared devices globally. Strong reimbursement frameworks for advanced diagnostic procedures and active hospital AI adoption programs supported by companies including GE Healthcare and Siemens Healthineers sustain dominant regional positioning.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, due to rapidly expanding diagnostic imaging infrastructure investment, government AI healthcare development programs, and large underserved patient populations benefiting from AI-driven teleradiology. China's NMPA has established expedited review tracks for AI medical device approvals, accelerating domestic and international imaging AI product launches. Japan and South Korea's advanced imaging equipment manufacturing ecosystems are integrating AI capabilities across product lines.

Key players in the market

Some of the key players in AI Medical Imaging Market include GE Healthcare, Siemens Healthineers, Philips Healthcare, Canon Medical Systems Corporation, IBM Watson Health, Aidoc Medical Ltd., Zebra Medical Vision, Arterys Inc., Viz.ai, Inc., Enlitic, Inc., Qure.ai, Lunit Inc., Butterfly Network, Inc., Tempus Labs, NVIDIA Corporation, Fujifilm Holdings Corporation, Samsung Medison, and Agfa-Gevaert Group.

Key Developments:

In March 2026, NVIDIA Corporation introduced a purpose-built medical imaging AI inference hardware platform optimized for hospital on-premise deployment with HIPAA-compliant data processing.

In February 2026, GE Healthcare launched its Edison AI imaging platform expansion with new oncology CT lesion detection algorithms cleared by FDA for lung nodule screening workflows.

In January 2026, Aidoc Medical Ltd. secured a major multi-site hospital system contract deploying its AI radiology triage platform across 40 imaging centers for emergency pathology detection.

In October 2025, Qure.ai announced expansion into Latin American markets through a regional telemedicine partnership integrating AI chest X-ray reading into primary care networks.

Modalities Covered:

  • X-ray Imaging
  • Computed Tomography (CT)
  • Magnetic Resonance Imaging (MRI)
  • Ultrasound Imaging
  • Nuclear Imaging
  • Mammography
  • Other Modalities

Deployment Modes Covered:

  • Cloud-based
  • On-premise
  • Hybrid
  • Web-based Platforms
  • SaaS Models
  • Integrated Systems

Technologies Covered:

  • Deep Learning
  • Machine Learning
  • Natural Language Processing
  • Computer Vision
  • Cloud-based AI
  • Edge AI

Applications Covered:

  • Oncology Imaging
  • Cardiology Imaging
  • Neurology Imaging
  • Pulmonology Imaging
  • Orthopedic Imaging
  • Gastroenterology Imaging
  • Other Applications

End Users Covered:

  • Hospitals
  • Diagnostic Imaging Centers
  • Research Institutes
  • Ambulatory Surgical Centers
  • Tele-radiology Providers
  • Academic Medical Centers
  • Other End Users

Regions Covered:

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

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2023, 2024, 2025, 2026, 2027, 2028, 2030, 3032 and 2034
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

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

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

Table of Contents

1 Executive Summary

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

2 Research Framework

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

3 Market Dynamics and Trend Analysis

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

4 Competitive and Strategic Assessment

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

5 Global AI Medical Imaging Market, By Modality

  • 5.1 X-ray Imaging
  • 5.2 Computed Tomography (CT)
  • 5.3 Magnetic Resonance Imaging (MRI)
  • 5.4 Ultrasound Imaging
  • 5.5 Nuclear Imaging
  • 5.6 Mammography
  • 5.7 Other Modalities

6 Global AI Medical Imaging Market, By Deployment Mode

  • 6.1 Cloud-based
  • 6.2 On-premise
  • 6.3 Hybrid
  • 6.4 Web-based Platforms
  • 6.5 SaaS Models
  • 6.6 Integrated Systems

7 Global AI Medical Imaging Market, By Technology

  • 7.1 Deep Learning
  • 7.2 Machine Learning
  • 7.3 Natural Language Processing
  • 7.4 Computer Vision
  • 7.5 Cloud-based AI
  • 7.6 Edge AI

8 Global AI Medical Imaging Market, By Application

  • 8.1 Oncology Imaging
  • 8.2 Cardiology Imaging
  • 8.3 Neurology Imaging
  • 8.4 Pulmonology Imaging
  • 8.5 Orthopedic Imaging
  • 8.6 Gastroenterology Imaging
  • 8.7 Other Applications

9 Global AI Medical Imaging Market, By End User

  • 9.1 Hospitals
  • 9.2 Diagnostic Imaging Centers
  • 9.3 Research Institutes
  • 9.4 Ambulatory Surgical Centers
  • 9.5 Tele-radiology Providers
  • 9.6 Academic Medical Centers
  • 9.7 Other End Users

10 Global AI Medical Imaging Market, By Geography

  • 10.1 North America
    • 10.1.1 United States
    • 10.1.2 Canada
    • 10.1.3 Mexico
  • 10.2 Europe
    • 10.2.1 United Kingdom
    • 10.2.2 Germany
    • 10.2.3 France
    • 10.2.4 Italy
    • 10.2.5 Spain
    • 10.2.6 Netherlands
    • 10.2.7 Belgium
    • 10.2.8 Sweden
    • 10.2.9 Switzerland
    • 10.2.10 Poland
    • 10.2.11 Rest of Europe
  • 10.3 Asia Pacific
    • 10.3.1 China
    • 10.3.2 Japan
    • 10.3.3 India
    • 10.3.4 South Korea
    • 10.3.5 Australia
    • 10.3.6 Indonesia
    • 10.3.7 Thailand
    • 10.3.8 Malaysia
    • 10.3.9 Singapore
    • 10.3.10 Vietnam
    • 10.3.11 Rest of Asia Pacific
  • 10.4 South America
    • 10.4.1 Brazil
    • 10.4.2 Argentina
    • 10.4.3 Colombia
    • 10.4.4 Chile
    • 10.4.5 Peru
    • 10.4.6 Rest of South America
  • 10.5 Rest of the World (RoW)
    • 10.5.1 Middle East
      • 10.5.1.1 Saudi Arabia
      • 10.5.1.2 United Arab Emirates
      • 10.5.1.3 Qatar
      • 10.5.1.4 Israel
      • 10.5.1.5 Rest of Middle East
    • 10.5.2 Africa
      • 10.5.2.1 South Africa
      • 10.5.2.2 Egypt
      • 10.5.2.3 Morocco
      • 10.5.2.4 Rest of Africa

11 Strategic Market Intelligence

  • 11.1 Industry Value Network and Supply Chain Assessment
  • 11.2 White-Space and Opportunity Mapping
  • 11.3 Product Evolution and Market Life Cycle Analysis
  • 11.4 Channel, Distributor, and Go-to-Market Assessment

12 Industry Developments and Strategic Initiatives

  • 12.1 Mergers and Acquisitions
  • 12.2 Partnerships, Alliances, and Joint Ventures
  • 12.3 New Product Launches and Certifications
  • 12.4 Capacity Expansion and Investments
  • 12.5 Other Strategic Initiatives

13 Company Profiles

  • 13.1 GE Healthcare
  • 13.2 Siemens Healthineers
  • 13.3 Philips Healthcare
  • 13.4 Canon Medical Systems Corporation
  • 13.5 IBM Watson Health
  • 13.6 Aidoc Medical Ltd.
  • 13.7 Zebra Medical Vision
  • 13.8 Arterys Inc.
  • 13.9 Viz.ai, Inc.
  • 13.10 Enlitic, Inc.
  • 13.11 Qure.ai
  • 13.12 Lunit Inc.
  • 13.13 Butterfly Network, Inc.
  • 13.14 Tempus Labs
  • 13.15 NVIDIA Corporation
  • 13.16 Fujifilm Holdings Corporation
  • 13.17 Samsung Medison
  • 13.18 Agfa-Gevaert Group

List of Tables

  • Table 1 Global AI Medical Imaging Market Outlook, By Region (2023-2034)($MN)
  • Table 2 Global AI Medical Imaging Market Outlook, By Modality (2023-2034)($MN)
  • Table 3 Global AI Medical Imaging Market Outlook, By X-ray Imaging (2023-2034) ($MN)
  • Table 4 Global AI Medical Imaging Market Outlook, By Computed Tomography (CT) (2023-2034)($MN)
  • Table 5 Global AI Medical Imaging Market Outlook, By Magnetic Resonance Imaging (MRI) (2023-2034)($MN)
  • Table 6 Global AI Medical Imaging Market Outlook, By Ultrasound Imaging (2023-2034)($MN)
  • Table 7 Global AI Medical Imaging Market Outlook, By Nuclear Imaging (2023-2034)($MN)
  • Table 8 Global AI Medical Imaging Market Outlook, By Mammography (2023-2034)($MN)
  • Table 9 Global AI Medical Imaging Market Outlook, By Other Modalities (2023-2034)($MN)
  • Table 10 Global AI Medical Imaging Market Outlook, By Deployment Mode (2023-2034) ($MN)
  • Table 11 Global AI Medical Imaging Market Outlook, By Cloud-based (2023-2034)($MN)
  • Table 12 Global AI Medical Imaging Market Outlook, By On-premise (2023-2034) ($MN)
  • Table 13 Global AI Medical Imaging Market Outlook, By Hybrid (2023-2034)($MN)
  • Table 14 Global AI Medical Imaging Market Outlook, By Web-based Platforms (2023-2034)($MN)
  • Table 15 Global AI Medical Imaging Market Outlook, By SaaS Models (2023-2034)($MN)
  • Table 16 Global AI Medical Imaging Market Outlook, By Integrated Systems (2023-2034) ($MN)
  • Table 17 Global AI Medical Imaging Market Outlook, By Technology (2023-2034)($MN)
  • Table 18 Global AI Medical Imaging Market Outlook, By Deep Learning (2023-2034) ($MN)
  • Table 19 Global AI Medical Imaging Market Outlook, By Machine Learning (2023-2034)($MN)
  • Table 20 Global AI Medical Imaging Market Outlook, By Natural Language Processing (2023-2034)($MN)
  • Table 21 Global AI Medical Imaging Market Outlook, By Computer Vision (2023-2034) ($MN)
  • Table 22 Global AI Medical Imaging Market Outlook, By Cloud-based AI (2023-2034)($MN)
  • Table 23 Global AI Medical Imaging Market Outlook, By Edge AI (2023-2034)($MN)
  • Table 24 Global AI Medical Imaging Market Outlook, By Application (2023-2034)($MN)
  • Table 25 Global AI Medical Imaging Market Outlook, By Oncology Imaging (2023-2034)($MN)
  • Table 26 Global AI Medical Imaging Market Outlook, By Cardiology Imaging (2023-2034)($MN)
  • Table 27 Global AI Medical Imaging Market Outlook, By Neurology Imaging (2023-2034)($MN)
  • Table 28 Global AI Medical Imaging Market Outlook, By Pulmonology Imaging (2023-2034)($MN)
  • Table 29 Global AI Medical Imaging Market Outlook, By Orthopedic Imaging (2023-2034)($MN)
  • Table 30 Global AI Medical Imaging Market Outlook, By Gastroenterology Imaging (2023-2034) ($MN)
  • Table 31 Global AI Medical Imaging Market Outlook, By Other Applications (2023-2034)($MN)
  • Table 32 Global AI Medical Imaging Market Outlook, By End User (2023-2034)($MN)
  • Table 33 Global AI Medical Imaging Market Outlook, By Hospitals (2023-2034)($MN)
  • Table 34 Global AI Medical Imaging Market Outlook, By Diagnostic Imaging Centers (2023-2034)($MN)
  • Table 35 Global AI Medical Imaging Market Outlook, By Research Institutes (2023-2034)($MN)
  • Table 36 Global AI Medical Imaging Market Outlook, By Ambulatory Surgical Centers (2023-2034)($MN)
  • Table 37 Global AI Medical Imaging Market Outlook, By Tele-radiology Providers (2023-2034)($MN)
  • Table 38 Global AI Medical Imaging Market Outlook, By Academic Medical Centers (2023-2034)($MN)
  • Table 39 Global AI Medical Imaging Market Outlook, By Other End Users (2023-2034)($MN)

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