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市場調查報告書
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2065210

人工智慧驅動的醫學影像分析市場預測至2034年：全球分析（按組成部分、診斷影像方法、技術、工作流程階段、臨床功能、最終用戶和地區分類）

AI-Based Medical Imaging Analytics Market Forecasts to 2034 - Global Analysis By Component (Software, Hardware, and Services), Imaging Modality, Technology, Workflow Stage, Clinical Function, End User and By 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，全球人工智慧驅動的醫學影像分析市場預計將在 2026 年達到 38 億美元，到 2034 年達到 225 億美元，在預測期內以 24.9% 的複合年成長率成長。

人工智慧驅動的醫學影像分析是指應用人工智慧、機器學習、深度學習和電腦視覺演算法來分析CT、MRI、X光、超音波和乳房X光等診斷設備產生的醫學影像。這些平台可以幫助放射科醫生和臨床醫生檢測異常情況、量化疾病進展、輔助診斷決策並簡化放射科工作流程。

放射科醫師短缺問題日益嚴重，而醫學影像檢查檢查數量卻激增

全球醫療系統正面臨訓練有素的放射科醫生嚴重短缺的問題，人口老化和慢性病負擔日益加重導致影像檢查檢查數量呈指數級成長，加劇了這一局面。人工智慧影像分析平台透過自動化常規影像篩檢、標記需要緊急處理的關鍵觀察，並為非放射科醫生提供人工智慧輔助的初步診斷觀察，從而有效應對了這一能力危機。投資人工智慧工作流程的醫院和診斷中心報告稱，處理時間顯著縮短，肺動脈栓塞、中風和肺結節等疾病的檢出率也得到提高，這有力地證明了該技術的投資回報。

監管不確定性和臨床相關性要求

儘管越來越多的臨床證據支持人工智慧驅動的影像工具，但其應用仍受到嚴格的法規核准流程的限制，尤其是對於被歸類為醫療設備的診斷人工智慧軟體而言。獲得FDA批准或CE認證需要進行大規模的臨床檢驗研究，以證明其性能與放射科醫生相當或更優，而這一過程耗時耗力。放射科醫師對演算法決策支援的抵觸情緒、對人工智慧產生觀察的法律責任擔憂，以及人工智慧輔助解讀的保險報銷管道有限，都進一步延緩了其商業化進程，尤其是在技術預算有限的小規模醫療機構中。

聯邦學習的擴展，使得跨多個機構進行人工智慧模型訓練成為可能。

聯邦學習正成為一種創新方法，它無需將高度敏感的患者資料集中儲存在各個機構中，即可開發出強大的AI成像模型。聯邦架構透過使用分散式資料集在本地訓練演算法，並聚合模型參數而非原始影像，既解決了資料隱私問題，又使AI系統能夠從更大規模、更多樣化的患者群體中學習。學術醫療中心、醫療保健系統和AI公司正在建立協力網路，以開發針對特異性疾病的模型，並提高其在不同人群和成像設備類型中的泛化能力，從而為服務不足的成像亞專科領域開闢商業性機會。

演算法偏差和不同患者群體間的表現差異

主要基於特定人群或特定品牌成像設備資料集訓練的人工智慧成像模型，在臨床環境多樣化的情況下部署時，性能可能下降。已有研究報告指出，演算法在檢測不同種族、性別和體型族群的病灶時存在偏差，這引發了人們對病患安全和公平性的擔憂。在資源受限的環境下，使用低配置設備擷取的影像效能下降，進一步限制了人工智慧成像模型的全球部署潛力。為因應這些風險，需要監管機構強制要求對模型進行持續監測，進行前瞻性檢驗研究，並在人工智慧系統的開發和上市後監管過程中，收集和維護多樣化的資料集。

新冠疫情的影響：

新冠疫情迫切需要展現人工智慧醫學影像應用的價值，尤其是在胸部CT和X光影像中檢測新冠肺炎影像模式。緊急法規核准加速了人工智慧影像工具的臨床部署，證明了其在快速分流大量患者方面的價值。疫情也凸顯了擴充性的雲端影像分析平台的重要性，這些平台使放射科醫生能夠遠端存取和使用人工智慧驅動的影像診斷。在後疫情時代，醫療機構正在維持並擴大對人工智慧影像的投資，將其作為旨在提高診斷能力的更廣泛數位轉型策略的一部分。

在預測期內，軟體領域預計將佔據最大的市場佔有率。

在以人工智慧為基礎的醫學影像市場中，軟體板塊佔據最大的收入佔有率，這反映了醫院網路和診斷中心普遍採用訂閱式和永久許可式軟體部署的商業模式。診斷影像軟體和工作流程最佳化工具被廣泛用於提高放射科醫生的工作效率並減少觀察。企業軟體合約帶來的高生命週期價值、持續的升級收入以及與現有PACS和RIS基礎設施的整合，為供應商提供了強勁的收入前景。不斷增強的整合生成式人工智慧功能的平台也推動了軟體收入的成長。

預計在預測期內，深度學習技術領域將呈現最高的複合年成長率。

深度學習預計將在人工智慧成像技術領域實現最高的成長速度，這得益於其識別影像資料中複雜多維模式的卓越能力，超越了傳統機器學習。卷積類神經網路和基於變壓器的架構在放射學的各個領域，包括神經放射學、心臟病學和腫瘤成像，都展現出了突破性的性能。透過與學術機構和聯盟的合作，大規模標註圖像資料集的獲取正在加速深度學習模型的開發。運算效率的提高和雲端GPU存取的便利性降低了在醫療保健領域大規模部署深度學習解決方案的門檻。

市佔率最大的地區：

預計北美將在整個預測期內佔據最大的市場佔有率。由於許多領先的人工智慧醫療公司、世界一流的學術醫療中心以及有利於商業化人工智慧成像產品上市的早期監管流程，北美在全球人工智慧醫療成像市場中佔據最大佔有率。美國FDA的數位健康卓越中心簡化了人工智慧/機器學習醫療設備的核准流程，加速了產品進入市場。醫療IT領域的高額投入、PACS基礎設施的廣泛應用以及醫生對人工智慧成像能力的深刻理解，將進一步鞏固北美在整個預測期內的市場領導地位。

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。在中國、日本、韓國和印度等國政府主導的數位醫療舉措的推動下，亞太地區預計將在人工智慧醫學影像分析市場中實現最高的複合年成長率。中國的國家人工智慧發展策略明確優先發展醫療人工智慧應用，因此國家層級大力投資人工智慧影像分析新創企業和醫院試驗計畫。在印度，儘管診斷影像中心網路正在不斷擴張，但專業放射科醫生嚴重短缺，這推動了對人工智慧輔助診斷工具的強勁商業性需求。區域性科技公司正在開發針對亞太地區醫療保健領域常見疾病模式和影像設備的在地化影像人工智慧解決方案。

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目錄

第1章執行摘要

  • 市場概覽及主要亮點
  • 促進因素、挑戰和機遇
  • 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
  • 主要公司市佔率分析
  • 產品基準評效和效能比較

第5章：全球人工智慧驅動的醫學影像分析市場：按組件分類

  • 軟體
    • 影像診斷分析軟體
    • 工作流程最佳化軟體
    • 報告創建和視覺化軟體
    • 預測分析軟體
  • 硬體
    • 人工智慧加速器
    • 影像資料儲存系統
    • 邊緣運算設備
  • 服務
    • 諮詢服務
    • 整合和配置服務
    • 培訓和支援服務

第6章：全球人工智慧驅動的醫學影像分析市場：按診斷影像方法分類

  • 電腦斷層掃描（CT）
  • 磁振造影（MRI）
  • X光影像診斷
  • 超音波影像
  • 核子醫學掃描術診斷
  • 乳房X光檢查
  • 光學成像
  • 多模態影像診斷

第7章：全球人工智慧驅動的醫學影像分析市場：按技術分類

  • 機器學習
  • 深度學習
  • 電腦視覺
  • 自然語言處理（NLP）
  • 人工智慧世代
  • 聯邦學習
  • 基於雲端的人工智慧分析

第8章：全球人工智慧驅動的醫學影像分析市場：按工作流程階段分類

  • 影像擷取
  • 影像處理
  • 影像解讀
  • 診斷支持
  • 報告和文檔
  • 治療方案
  • 後續跟進和監測

第9章：全球人工智慧驅動的醫學影像分析市場：按臨床功能分類

  • 電腦輔助檢測（CADe）
  • 電腦輔助診斷（CADx）
  • 定量影像分析
  • 風險分層
  • 疾病進展監測
  • 預測和預後分析

第10章：全球人工智慧驅動的醫學影像分析市場：按最終用戶分類

  • 醫院
  • 診斷影像中心
  • 專科診所
  • 學術研究機構
  • 門診手術中心
  • 受託研究機構（CRO）
  • 其他最終用戶

第11章 全球人工智慧驅動的醫學影像分析市場：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12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13章 產業趨勢與策略舉措

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

第14章：公司簡介

  • GE HealthCare
  • Siemens Healthineers
  • Philips
  • Canon Medical Systems
  • Fujifilm Holdings Corporation
  • Aidoc
  • Viz.ai
  • Lunit
  • Qure.ai
  • Infervision
  • Arterys
  • Butterfly Network
  • Enlitic
  • iCAD
  • Tempus AI
Product Code: SMRC36762

According to Stratistics MRC, the Global AI-Based Medical Imaging Analytics Market is accounted for $3.8 billion in 2026 and is expected to reach $22.5 billion by 2034, growing at a CAGR of 24.9% during the forecast period. AI-Based Medical Imaging Analytics refers to the application of artificial intelligence, machine learning, deep learning, and computer vision algorithms to analyze medical images generated through modalities such as CT, MRI, X-ray, ultrasound, and mammography. These platforms assist radiologists and clinicians in detecting abnormalities, quantifying disease progression, supporting diagnostic decisions, and streamlining radiology workflows.

Market Dynamics:

Driver:

Escalating radiologist shortage and surging medical imaging volumes

Healthcare systems globally face an acute shortage of trained radiologists, exacerbated by exponential growth in imaging study volumes driven by an aging population and expanding chronic disease burden. AI-based imaging analytics platforms address this capacity crisis by automating routine image triage, flagging critical findings for urgent review, and enabling non-radiologist clinicians to access AI-assisted preliminary interpretations. Hospitals and diagnostic centers investing in AI workflows report significant reductions in turnaround time and improved detection rates for conditions such as pulmonary embolism, stroke, and lung nodules, creating compelling return-on-investment arguments for technology adoption.

Restraint:

Regulatory uncertainty and clinical validation requirements

Despite growing clinical evidence supporting AI imaging tools, their adoption is constrained by stringent regulatory approval pathways, particularly for diagnostic AI software classified as medical devices. Obtaining FDA clearance or CE marking requires extensive multi-site clinical validation studies demonstrating performance equivalency or superiority to radiologist interpretation, a process that is time-intensive and costly. Radiologist resistance to algorithmic decision support, liability concerns around AI-generated interpretations, and limited reimbursement pathways for AI-assisted reads further dampen commercialization momentum, particularly among smaller healthcare institutions with constrained technology budgets.

Opportunity:

Expansion of federated learning enabling multi-institutional AI model training

Federated learning is emerging as a transformative approach for developing robust AI imaging models without centralizing sensitive patient data across institutions. By training algorithms locally on distributed datasets and aggregating model parameters rather than raw images, federated architectures address data privacy concerns while enabling AI systems to learn from far larger and more diverse patient populations. Academic medical centers, health systems, and AI companies are forming collaborative networks to build disease-specific models with enhanced generalizability across demographic groups and imaging equipment types, unlocking commercial opportunities in underserved imaging subspecialties.

Threat:

Algorithm bias and performance variability across patient populations

AI imaging models trained predominantly on datasets from specific demographic groups or imaging equipment brands risk underperforming when deployed in clinically diverse settings. Documented cases of algorithmic bias in detecting pathologies across racial, gender, and body habitus groups raise patient safety and equity concerns. Performance degradation on images acquired from lower-specification equipment in resource-limited settings further limits global deployability. Addressing these risks requires continuous model monitoring, prospective validation studies, and regulatory mandates for diversity-aware dataset curation during AI system development and post-market surveillance.

Covid-19 Impact:

The COVID-19 pandemic created an urgent testbed for AI medical imaging applications, particularly in detecting COVID-19 pneumonia patterns on chest CT and X-ray studies. Emergency authorizations from regulatory bodies accelerated clinical deployment of AI imaging tools, demonstrating their value in triaging large patient volumes rapidly. The pandemic also highlighted the importance of scalable, cloud-based imaging analytics platforms capable of remote radiologist access and AI-assisted interpretation. Post-pandemic, health systems are maintaining and expanding AI imaging investments as part of broader digital transformation strategies aimed at improving diagnostic resilience.

The Software segment is expected to be the largest during the forecast period

The software segment commands the largest revenue share within the AI-based medical imaging analytics market, reflecting the dominant commercial model of subscription-based and perpetual license software deployments across hospital networks and diagnostic centers. Diagnostic imaging analytics software and workflow optimization tools are widely adopted to enhance radiologist productivity and reduce missed findings. The high lifetime value of enterprise software contracts, ongoing upgrade revenues, and integration with existing PACS and RIS infrastructure create strong revenue visibility for software vendors. Continuous platform enhancements incorporating generative AI capabilities are sustaining software revenue growth.

The Deep Learning Technology segment is expected to have the highest CAGR during the forecast period

Deep learning is forecast to achieve the highest growth rate among AI imaging technologies, driven by its superior ability to identify complex, multi-dimensional patterns within imaging data that exceed conventional machine learning capabilities. Convolutional neural networks and transformer-based architectures are demonstrating breakthrough performance across radiology subspecialties including neuroradiology, cardiology, and oncology imaging. The availability of large-scale annotated imaging datasets through academic partnerships and federated consortia is accelerating deep learning model development. Increased compute efficiency and cloud GPU accessibility are reducing barriers to deploying deep learning solutions at scale within healthcare organizations.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, North America commands the largest share of the global AI-based medical imaging analytics market, anchored by the concentration of leading AI healthcare companies, world-class academic medical centers, and early regulatory pathways enabling commercial AI imaging product launches. The United States FDA's Digital Health Center of Excellence has streamlined the clearance of AI/ML-enabled medical devices, facilitating faster market entry. High healthcare IT expenditure, widespread adoption of PACS infrastructure, and strong physician awareness of AI imaging capabilities collectively reinforce North America's market leadership throughout the forecast period.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR. Asia Pacific is expected to record the highest CAGR in the AI-based medical imaging analytics market, fueled by government-led digital health initiatives in China, Japan, South Korea, and India. China's national AI development strategy explicitly prioritizes medical AI applications, with substantial state investment in AI imaging startups and hospital pilot programs. India's expanding network of diagnostic imaging centers and an acute shortage of specialist radiologists are driving strong commercial demand for AI-assisted diagnostic tools. Regional technology companies are developing locally adapted imaging AI solutions tailored to disease patterns and imaging equipment prevalent across the Asia Pacific healthcare landscape.

Key players in the market

Some of the key players in Global AI-Based Medical Imaging Analytics Market include GE HealthCare, Siemens Healthineers, Philips, Canon Medical Systems, Fujifilm Holdings Corporation, Aidoc, Viz.ai, Lunit, Qure.ai, Infervision, Arterys, Butterfly Network, Enlitic, iCAD, and Tempus AI.

Key Developments:

In March 2026, GE HealthCare announced the commercial launch of an expanded AI imaging suite incorporating deep learning-based anomaly detection across cardiac MRI and chest CT studies, with automated prioritization features designed to flag time-sensitive findings for immediate radiologist review, deployed initially across health systems in the United States and United Kingdom.

In February 2026, Aidoc secured a significant multi-year enterprise agreement with a large hospital network encompassing over 40 facilities to deploy its AI-powered triage and notification platform across emergency radiology workflows, expanding its installed base and reinforcing its market position in AI-enabled critical care imaging analytics.

Components Covered:

  • Software
  • Hardware
  • Services

Imaging Modalitys Covered:

  • CT
  • MRI
  • X-ray
  • Ultrasound
  • Nuclear Imaging
  • Mammography
  • Optical Imaging
  • Multi-modal

Technologies Covered:

  • Machine Learning
  • Deep Learning
  • Computer Vision
  • NLP
  • Generative AI
  • Federated Learning
  • Cloud-based AI

Workflow Stages Covered:

  • Image Acquisition
  • Image Processing
  • Image Interpretation
  • Diagnosis Assistance
  • Reporting & Documentation
  • Treatment Planning
  • Follow-up & Monitoring

Clinical Functions Covered:

  • CADe
  • CADx
  • Quantitative Imaging Analysis
  • Risk Stratification
  • Disease Progression Monitoring
  • Predictive & Prognostic Analytics

End Users Covered:

  • Hospitals
  • Diagnostic Imaging Centers
  • Specialty Clinics
  • Academic & Research Institutes
  • Ambulatory Surgical Centers
  • CROs
  • 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, 2032 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-Based Medical Imaging Analytics Market, By Component

  • 5.1 Software
    • 5.1.1 Diagnostic Imaging Analytics Software
    • 5.1.2 Workflow Optimization Software
    • 5.1.3 Reporting & Visualization Software
    • 5.1.4 Predictive Analytics Software
  • 5.2 Hardware
    • 5.2.1 AI Accelerators
    • 5.2.2 Imaging Data Storage Systems
    • 5.2.3 Edge Computing Devices
  • 5.3 Services
    • 5.3.1 Consulting Services
    • 5.3.2 Integration & Deployment Services
    • 5.3.3 Training & Support Services

6 Global AI-Based Medical Imaging Analytics Market, By Imaging Modality

  • 6.1 Computed Tomography (CT)
  • 6.2 Magnetic Resonance Imaging (MRI)
  • 6.3 X-ray Imaging
  • 6.4 Ultrasound Imaging
  • 6.5 Nuclear Imaging
  • 6.6 Mammography
  • 6.7 Optical Imaging
  • 6.8 Multi-modal Imaging

7 Global AI-Based Medical Imaging Analytics Market, By Technology

  • 7.1 Machine Learning
  • 7.2 Deep Learning
  • 7.3 Computer Vision
  • 7.4 Natural Language Processing (NLP)
  • 7.5 Generative AI
  • 7.6 Federated Learning
  • 7.7 Cloud-based AI Analytics

8 Global AI-Based Medical Imaging Analytics Market, By Workflow Stage

  • 8.1 Image Acquisition
  • 8.2 Image Processing
  • 8.3 Image Interpretation
  • 8.4 Diagnosis Assistance
  • 8.5 Reporting & Documentation
  • 8.6 Treatment Planning
  • 8.7 Follow-up & Monitoring

9 Global AI-Based Medical Imaging Analytics Market, By Clinical Function

  • 9.1 Computer-Aided Detection (CADe)
  • 9.2 Computer-Aided Diagnosis (CADx)
  • 9.3 Quantitative Imaging Analysis
  • 9.4 Risk Stratification
  • 9.5 Disease Progression Monitoring
  • 9.6 Predictive & Prognostic Analytics

10 Global AI-Based Medical Imaging Analytics Market, By End User

  • 10.1 Hospitals
  • 10.2 Diagnostic Imaging Centers
  • 10.3 Specialty Clinics
  • 10.4 Academic & Research Institutes
  • 10.5 Ambulatory Surgical Centers
  • 10.6 Contract Research Organizations (CROs)
  • 10.7 Other End Users

11 Global AI-Based Medical Imaging Analytics Market, By Geography

  • 11.1 North America
    • 11.1.1 United States
    • 11.1.2 Canada
    • 11.1.3 Mexico
  • 11.2 Europe
    • 11.2.1 United Kingdom
    • 11.2.2 Germany
    • 11.2.3 France
    • 11.2.4 Italy
    • 11.2.5 Spain
    • 11.2.6 Netherlands
    • 11.2.7 Belgium
    • 11.2.8 Sweden
    • 11.2.9 Switzerland
    • 11.2.10 Poland
    • 11.2.11 Rest of Europe
  • 11.3 Asia Pacific
    • 11.3.1 China
    • 11.3.2 Japan
    • 11.3.3 India
    • 11.3.4 South Korea
    • 11.3.5 Australia
    • 11.3.6 Indonesia
    • 11.3.7 Thailand
    • 11.3.8 Malaysia
    • 11.3.9 Singapore
    • 11.3.10 Vietnam
    • 11.3.11 Rest of Asia Pacific
  • 11.4 South America
    • 11.4.1 Brazil
    • 11.4.2 Argentina
    • 11.4.3 Colombia
    • 11.4.4 Chile
    • 11.4.5 Peru
    • 11.4.6 Rest of South America
  • 11.5 Rest of the World (RoW)
    • 11.5.1 Middle East
      • 11.5.1.1 Saudi Arabia
      • 11.5.1.2 United Arab Emirates
      • 11.5.1.3 Qatar
      • 11.5.1.4 Israel
      • 11.5.1.5 Rest of Middle East
    • 11.5.2 Africa
      • 11.5.2.1 South Africa
      • 11.5.2.2 Egypt
      • 11.5.2.3 Morocco
      • 11.5.2.4 Rest of Africa

12 Strategic Market Intelligence

  • 12.1 Industry Value Network and Supply Chain Assessment
  • 12.2 White-Space and Opportunity Mapping
  • 12.3 Product Evolution and Market Life Cycle Analysis
  • 12.4 Channel, Distributor, and Go-to-Market Assessment

13 Industry Developments and Strategic Initiatives

  • 13.1 Mergers and Acquisitions
  • 13.2 Partnerships, Alliances, and Joint Ventures
  • 13.3 New Product Launches and Certifications
  • 13.4 Capacity Expansion and Investments
  • 13.5 Other Strategic Initiatives

14 Company Profiles

  • 14.1 GE HealthCare
  • 14.2 Siemens Healthineers
  • 14.3 Philips
  • 14.4 Canon Medical Systems
  • 14.5 Fujifilm Holdings Corporation
  • 14.6 Aidoc
  • 14.7 Viz.ai
  • 14.8 Lunit
  • 14.9 Qure.ai
  • 14.10 Infervision
  • 14.11 Arterys
  • 14.12 Butterfly Network
  • 14.13 Enlitic
  • 14.14 iCAD
  • 14.15 Tempus AI

List of Tables

  • Table 1 Global AI-Based Medical Imaging Analytics Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI-Based Medical Imaging Analytics Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global AI-Based Medical Imaging Analytics Market Outlook, By Software (2023-2034) ($MN)
  • Table 4 Global AI-Based Medical Imaging Analytics Market Outlook, By Diagnostic Imaging Analytics Software (2023-2034) ($MN)
  • Table 5 Global AI-Based Medical Imaging Analytics Market Outlook, By Workflow Optimization Software (2023-2034) ($MN)
  • Table 6 Global AI-Based Medical Imaging Analytics Market Outlook, By Reporting & Visualization Software (2023-2034) ($MN)
  • Table 7 Global AI-Based Medical Imaging Analytics Market Outlook, By Predictive Analytics Software (2023-2034) ($MN)
  • Table 8 Global AI-Based Medical Imaging Analytics Market Outlook, By Hardware (2023-2034) ($MN)
  • Table 9 Global AI-Based Medical Imaging Analytics Market Outlook, By AI Accelerators (2023-2034) ($MN)
  • Table 10 Global AI-Based Medical Imaging Analytics Market Outlook, By Imaging Data Storage Systems (2023-2034) ($MN)
  • Table 11 Global AI-Based Medical Imaging Analytics Market Outlook, By Edge Computing Devices (2023-2034) ($MN)
  • Table 12 Global AI-Based Medical Imaging Analytics Market Outlook, By Services (2023-2034) ($MN)
  • Table 13 Global AI-Based Medical Imaging Analytics Market Outlook, By Consulting Services (2023-2034) ($MN)
  • Table 14 Global AI-Based Medical Imaging Analytics Market Outlook, By Integration & Deployment Services (2023-2034) ($MN)
  • Table 15 Global AI-Based Medical Imaging Analytics Market Outlook, By Training & Support Services (2023-2034) ($MN)
  • Table 16 Global AI-Based Medical Imaging Analytics Market Outlook, By Imaging Modality (2023-2034) ($MN)
  • Table 17 Global AI-Based Medical Imaging Analytics Market Outlook, By Computed Tomography (CT) (2023-2034) ($MN)
  • Table 18 Global AI-Based Medical Imaging Analytics Market Outlook, By Magnetic Resonance Imaging (MRI) (2023-2034) ($MN)
  • Table 19 Global AI-Based Medical Imaging Analytics Market Outlook, By X-ray Imaging (2023-2034) ($MN)
  • Table 20 Global AI-Based Medical Imaging Analytics Market Outlook, By Ultrasound Imaging (2023-2034) ($MN)
  • Table 21 Global AI-Based Medical Imaging Analytics Market Outlook, By Nuclear Imaging (2023-2034) ($MN)
  • Table 22 Global AI-Based Medical Imaging Analytics Market Outlook, By Mammography (2023-2034) ($MN)
  • Table 23 Global AI-Based Medical Imaging Analytics Market Outlook, By Optical Imaging (2023-2034) ($MN)
  • Table 24 Global AI-Based Medical Imaging Analytics Market Outlook, By Multi-modal Imaging (2023-2034) ($MN)
  • Table 25 Global AI-Based Medical Imaging Analytics Market Outlook, By Technology (2023-2034) ($MN)
  • Table 26 Global AI-Based Medical Imaging Analytics Market Outlook, By Machine Learning (2023-2034) ($MN)
  • Table 27 Global AI-Based Medical Imaging Analytics Market Outlook, By Deep Learning (2023-2034) ($MN)
  • Table 28 Global AI-Based Medical Imaging Analytics Market Outlook, By Computer Vision (2023-2034) ($MN)
  • Table 29 Global AI-Based Medical Imaging Analytics Market Outlook, By Natural Language Processing (NLP) (2023-2034) ($MN)
  • Table 30 Global AI-Based Medical Imaging Analytics Market Outlook, By Generative AI (2023-2034) ($MN)
  • Table 31 Global AI-Based Medical Imaging Analytics Market Outlook, By Federated Learning (2023-2034) ($MN)
  • Table 32 Global AI-Based Medical Imaging Analytics Market Outlook, By Cloud-based AI Analytics (2023-2034) ($MN)
  • Table 33 Global AI-Based Medical Imaging Analytics Market Outlook, By Workflow Stage (2023-2034) ($MN)
  • Table 34 Global AI-Based Medical Imaging Analytics Market Outlook, By Image Acquisition (2023-2034) ($MN)
  • Table 35 Global AI-Based Medical Imaging Analytics Market Outlook, By Image Processing (2023-2034) ($MN)
  • Table 36 Global AI-Based Medical Imaging Analytics Market Outlook, By Image Interpretation (2023-2034) ($MN)
  • Table 37 Global AI-Based Medical Imaging Analytics Market Outlook, By Diagnosis Assistance (2023-2034) ($MN)
  • Table 38 Global AI-Based Medical Imaging Analytics Market Outlook, By Reporting & Documentation (2023-2034) ($MN)
  • Table 39 Global AI-Based Medical Imaging Analytics Market Outlook, By Treatment Planning (2023-2034) ($MN)
  • Table 40 Global AI-Based Medical Imaging Analytics Market Outlook, By Follow-up & Monitoring (2023-2034) ($MN)
  • Table 41 Global AI-Based Medical Imaging Analytics Market Outlook, By Clinical Function (2023-2034) ($MN)
  • Table 42 Global AI-Based Medical Imaging Analytics Market Outlook, By Computer-Aided Detection (CADe) (2023-2034) ($MN)
  • Table 43 Global AI-Based Medical Imaging Analytics Market Outlook, By Computer-Aided Diagnosis (CADx) (2023-2034) ($MN)
  • Table 44 Global AI-Based Medical Imaging Analytics Market Outlook, By Quantitative Imaging Analysis (2023-2034) ($MN)
  • Table 45 Global AI-Based Medical Imaging Analytics Market Outlook, By Risk Stratification (2023-2034) ($MN)
  • Table 46 Global AI-Based Medical Imaging Analytics Market Outlook, By Disease Progression Monitoring (2023-2034) ($MN)
  • Table 47 Global AI-Based Medical Imaging Analytics Market Outlook, By Predictive & Prognostic Analytics (2023-2034) ($MN)
  • Table 48 Global AI-Based Medical Imaging Analytics Market Outlook, By End User (2023-2034) ($MN)
  • Table 49 Global AI-Based Medical Imaging Analytics Market Outlook, By Hospitals (2023-2034) ($MN)
  • Table 50 Global AI-Based Medical Imaging Analytics Market Outlook, By Diagnostic Imaging Centers (2023-2034) ($MN)
  • Table 51 Global AI-Based Medical Imaging Analytics Market Outlook, By Specialty Clinics (2023-2034) ($MN)
  • Table 52 Global AI-Based Medical Imaging Analytics Market Outlook, By Academic & Research Institutes (2023-2034) ($MN)
  • Table 53 Global AI-Based Medical Imaging Analytics Market Outlook, By Ambulatory Surgical Centers (2023-2034) ($MN)
  • Table 54 Global AI-Based Medical Imaging Analytics Market Outlook, By Contract Research Organizations (CROs) (2023-2034) ($MN)
  • Table 55 Global AI-Based Medical Imaging Analytics Market Outlook, By Other End Users (2023-2034) ($MN)

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