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市場調查報告書
商品編碼
2065224

人工智慧放射學市場預測至2034年：按組件、技術、部署模式、成像方法、應用、最終用戶和地區分類的全球分析

AI-Powered Radiology Market Forecasts to 2034 - Global Analysis By Component (Software, Hardware, and Services), Technology, Deployment Mode, Imaging Modality, Application, 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，全球人工智慧放射學市場預計將在 2026 年達到 24 億美元，到 2034 年達到 137 億美元，在預測期內以 24.3% 的複合年成長率成長。

人工智慧放射學是指應用人工智慧技術，例如機器學習、深度學習、電腦視覺和自然語言處理，來改進各種醫學影像模式（包括X光、CT、MRI、超音波、乳房X光攝影和核子醫學影像）的採集、分析、乳房X光攝影和報告。人工智慧系統可以輔助放射科醫師偵測病灶、量化疾病負荷、確定工作優先順序、減少影像擷取過程中的偽影，並產生結構化的放射學報告。

全球放射科醫師短缺問題日益嚴重，而醫學影像檢查的數量卻不斷增加。

全球醫療系統正面臨醫學影像檢查數量與訓練有素、能夠解讀這些檢查結果的放射科醫生數量之間日益成長的不平衡。人口老化、癌症篩檢計畫的擴展以及臨床診斷和治療計劃中對斷層成像的日益依賴，都推動了年度影像檢查檢查數量的持續高速成長。人工智慧驅動的放射診斷工具正在透過自動化常規檢測任務、識別緊急觀察以及簡化報告流程來彌補這一能力缺口。希望在不相應增加放射科醫師人數的情況下維持診斷能力的醫療機構，正將採用人工智慧驅動的工作流程最佳化工具作為其放射科營運策略的核心要素。

輻射人工智慧引入過程中面臨的監管複雜性和臨床實施障礙。

儘管FDA批准和CE認證的AI放射診斷工具日益增多，但由於監管的複雜性、整合挑戰以及對放射科醫生工作流程的擔憂，其臨床應用率仍低於市場潛力。當醫療機構從多家供應商採購AI工具時，在評估臨床性能聲明、管理軟體與各種影像存檔和通訊系統（PACS）的整合以及在實際臨床環境中部署後監測AI工具的性能方面面臨著許多挑戰。放射科醫師對AI輔助診斷的責任問題以及缺乏評估AI工具的正式培訓，都造成了應用方面的文化障礙。此外，缺乏標準化的AI性能基準框架，使得採購負責人難以從臨床角度比較競爭產品。

將生成式人工智慧整合到放射診斷報告和臨床摘要的自動生成中。

生成式人工智慧正在成為放射學工作流程中的一項變革性功能，它能夠根據影像分析結果自動產生初步放射學報告、臨床摘要和結構化的後續建議。基於放射學報告語料庫訓練的大規模語言模型已展現出生成報告草稿的能力，從而顯著減少放射科醫生撰寫文件的時間。將生成式人工智慧與定量影像分析工具結合，可實現端到端的工作流程解決方案，簡化從影像擷取到最終報告提交的整個流程。放射學設備供應商正在積極投資於生成式人工智慧技術，而領先大學醫院進行的早期臨床初步試驗已顯示出令人鼓舞的效率提升效果。這為該技術在全球影像中心和醫院放射科的廣泛商業應用奠定了基礎。

對演算法偏差和不同患者群體間表現差異的擔憂

越來越多的證據表明，將人工智慧放射學演算法應用於具有不同人口統計特徵、影像設備規格或疾病盛行率的患者群體時，尤其是在使用為模型開發而建立的訓練資料集時，可能會導致效能差異。演算法偏差的風險對於被低估的患者病患小組（包括種族和少數族群族裔）而言尤其令人擔憂，而主要基於多數患者資料檢驗的人工智慧工具可能會降低檢測準確率。放射學人工智慧工具的上市後性能監測框架仍不完善，限制了醫療服務提供者識別和糾正性能隨時間變化的能力。這些問題已引起監管機構和學術界的關注，隨著放射學人工智慧的普及，可能會導致更嚴格的合規要求，並增加供應商的法律責任風險。

新冠疫情的影響：

新冠疫情大大加速了人工智慧放射診斷工具的應用。最直接的因素是迫切需要基於人工智慧的胸部CT和胸部X光影像分析來進行新冠病毒檢測和嚴重程度分級。緊急限制措施加速了人工智慧成像工具在多個地區用於新冠病毒檢測的市場推廣，產生了大量的臨床應用數據，並使放射科醫生熟悉了人工智慧輔助的診斷工作流程。此外，疫情為醫療系統帶來的資源限制進一步凸顯了人工智慧驅動的放射自動化在維持診斷處理能力方面的戰略價值，即使在放射科醫師人手有限的情況下也能發揮作用。在後疫情時代的醫療系統中，許多疫情期間推出的人工智慧成像工具仍在繼續使用，其應用範圍已擴展到新冠病毒以外的其他影像適應症，並保持著較高的市場普及率。

在預測期內，軟體領域預計將佔據最大的市場佔有率。

預計在預測期內，軟體領域將佔據最大的市場佔有率。這反映了放射學人工智慧的關鍵價值創造機制，包括演算法影像分析、電腦輔助檢測、工作流程管理和自動化報告。軟體供應商正在建立綜合性的放射學人工智慧平台，將多種模態特定的檢測和量化演算法整合到統一的PACS整合介面下，從而建立強大的競爭優勢，並透過訂閱授權模式持續獲得收入。演算法的不斷更新和向新的臨床應用領域的拓展，進一步鞏固了軟體領域的收入成長勢頭。

預計在預測期內，服務業板塊將呈現最高的複合年成長率。

在預測期內，服務領域預計將呈現最高的成長率，這主要得益於醫療服務提供者對人工智慧部署支援、模型檢驗服務、臨床工作流程整合諮詢以及持續效能監控的需求不斷成長。隨著人工智慧放射學部署日益複雜，醫療機構不斷擴展其人工智慧工具組合，對專業部署支援和託管服務的需求也隨之成長。放射學人工智慧供應商正在擴展其專業服務服務和託管服務範圍，以在整個部署生命週期內為客戶提供支持，涵蓋從初始工作流程評估到上市後性能管治的各個環節。

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。這主要歸功於早期採用者的集中、完善的先進影像服務報銷體系，以及許多領先的大學醫院作為臨床檢驗和應用的參考中心。在美國，大規模醫療系統、綜合醫療網路和遠距放射學服務供應商對放射學人工智慧的廣泛應用，佔據了該地區的大部分收入。完善的醫療設備軟體法規環境，以及創投對放射學人工智慧新創企業的創業投資投入，維持了充滿活力的創新管道，不斷拓展市場上經臨床檢驗的人工智慧影像解決方案的範圍。

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，這主要得益於醫療影像基礎設施投資的快速成長、印度和東南亞等市場放射科醫生嚴重短缺，以及各國政府對人工智慧醫療技術作為改善診斷可及性的工具的濃厚興趣。中國不僅正在崛起成為人工智慧放射學的主要應用市場，而且正在成為關鍵的創新中心，國內企業正在開發滿足國內和區域市場需求的先進影像人工智慧解決方案。

免費客製化服務：

所有購買此報告的客戶均可享受以下免費自訂選項之一：

  • 企業概況
    • 對其他市場參與者（最多 3 家公司）進行全面分析
    • 對主要公司進行SWOT分析（最多3家公司）
  • 區域細分
    • 根據客戶的興趣，我們將提供主要國家的市場估算和預測，以及複合年成長率（註：需經可行性確認）。
  • 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領先公司進行基準分析。

目錄

第1章執行摘要

  • 市場概覽及主要亮點
  • 促進因素、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：全球人工智慧放射學市場：按組件分類

  • 軟體
  • 硬體
  • 服務

第6章：全球人工智慧放射學市場：按技術分類

  • 機器學習
  • 深度學習
  • 自然語言處理（NLP）
  • 電腦視覺
  • 預測分析
  • 放射學中的生成式人工智慧

第7章：全球人工智慧放射學市場：依部署模式分類

  • 現場
  • 基於雲端的
  • 混合實現

第8章：全球人工智慧放射學市場：按成像方法分類

  • X光
  • 電腦斷層掃描（CT）
  • 磁振造影（MRI）
  • 超音波
  • 乳房X光檢查
  • 核子醫學掃描術診斷
  • PET-CT

第9章：全球人工智慧放射學市場：按應用領域分類

  • 疾病檢測與診斷
  • 工作流程最佳化
  • 定量影像診斷與測量
  • 臨床決策支持
  • 影像重建/增強
  • 預測和預防分析

第10章：全球人工智慧放射學市場：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

第11章：全球人工智慧放射學市場：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
  • Zebra Medical Vision
  • Siemens Healthineers
  • Viz.ai
  • Philips
  • Aidoc
  • Canon Medical Systems Corporation
  • Qure.ai
  • Fujifilm Holdings Corporation
  • Lunit
  • Infervision
  • DeepHealth
  • Rad AI
  • Enlitic
  • Arterys
Product Code: SMRC36776

According to Stratistics MRC, the Global AI-Powered Radiology Market is accounted for $2.4 billion in 2026 and is expected to reach $13.7 billion by 2034, growing at a CAGR of 24.3% during the forecast period. AI-Powered Radiology refers to the application of artificial intelligence technologies, including machine learning, deep learning, computer vision, and natural language processing, to enhance the acquisition, analysis, interpretation, and reporting of medical imaging data across modalities including X-ray, CT, MRI, ultrasound, mammography, and nuclear imaging. AI-powered systems assist radiologists in detecting lesions, quantifying disease burden, prioritizing worklists, reducing image acquisition artifacts, and generating structured radiology reports.

Market Dynamics:

Driver:

Escalating global radiologist shortage and rising medical imaging examination volumes

Healthcare systems worldwide are confronting a widening imbalance between the volume of medical imaging examinations performed and the availability of trained radiologists to interpret them. Annual imaging procedure volumes continue to grow at high single-digit rates driven by aging populations, expanding cancer screening programs, and broader clinical reliance on cross-sectional imaging for diagnosis and treatment planning. AI-powered radiology tools address this capacity gap by automating routine detection tasks, triaging urgent findings, and streamlining report generation workflows. Health systems seeking to maintain diagnostic throughput without proportionally expanding radiologist headcount are prioritizing AI-powered workflow optimization tools as a core component of radiology department operational strategy.

Restraint:

Regulatory complexity and clinical adoption barriers in radiology AI deployment

Despite the growing availability of FDA-cleared and CE-marked AI radiology tools, clinical adoption rates remain below market potential due to regulatory complexity, integration challenges, and radiologist workflow concerns. Healthcare providers navigating multi-vendor AI tool procurement face substantial challenges in evaluating clinical performance claims, managing software integration with diverse picture archiving and communication systems (PACS), and monitoring AI tool performance post-deployment in real-world clinical conditions. Radiologist concerns about liability for AI-assisted diagnoses, combined with limited formal training in AI tool evaluation, create cultural adoption barriers. The absence of standardized AI performance benchmarking frameworks also makes it difficult for procurement decision-makers to compare competing products on clinically meaningful dimensions.

Opportunity:

Integration of generative AI for automated radiology report synthesis and clinical summarization

Generative artificial intelligence is emerging as a transformative capability within the radiology workflow, enabling automated synthesis of preliminary radiology reports, clinical summaries, and structured follow-up recommendations from imaging analysis outputs. Large language models trained on radiology report corpora are demonstrating the ability to generate draft reports that substantially reduce radiologist documentation time. Integration of generative AI with quantitative imaging analysis tools creates end-to-end workflow solutions that streamline the journey from image acquisition to final report delivery. Radiology informatics vendors are actively investing in generative AI capabilities, and early clinical pilots at major academic medical centers are generating promising efficiency evidence, setting the stage for broad commercial adoption across imaging centers and hospital radiology departments globally.

Threat:

Algorithmic bias concerns and performance variability across patient populations

A growing body of evidence highlights performance disparities in AI radiology algorithms when applied to patient populations that differ in demographic characteristics, imaging equipment specifications, or disease prevalence from the training datasets used in model development. Algorithmic bias risks are particularly concerning for underrepresented patient groups including racial and ethnic minorities, where AI tools validated primarily on majority-population datasets may exhibit inferior detection accuracy. Post-market performance monitoring frameworks for radiology AI tools remain underdeveloped, limiting the ability of healthcare providers to identify and remediate performance drift over time. These concerns are attracting increasing regulatory and academic scrutiny, potentially increasing compliance requirements and vendor liability exposure as radiology AI deployment scales.

Covid-19 Impact:

COVID-19 dramatically accelerated the deployment of AI-powered radiology tools, most immediately through the urgent need for AI-based chest CT and chest X-ray analysis for COVID-19 detection and severity stratification. Emergency regulatory pathways facilitated rapid market introduction of COVID AI imaging tools across multiple jurisdictions, generating substantial clinical utilization data and building radiologist familiarity with AI-assisted diagnosis workflows. The pandemic also created healthcare system resource constraints that reinforced the strategic value of AI-driven radiology automation for maintaining diagnostic throughput with constrained radiologist availability. Post-pandemic health systems have retained many AI imaging tools deployed during the crisis and expanded their application to non-COVID imaging indications, sustaining elevated market adoption trajectories.

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

The software segment is expected to account for the largest market share during the forecast period, reflecting the primary value creation mechanism of radiology AI in algorithmic image analysis, computer-aided detection, workflow management, and automated reporting. Software vendors are building comprehensive radiology AI platforms that integrate multiple modality-specific detection and quantification algorithms under unified PACS-integrated interfaces, creating strong competitive moats and recurring revenue through subscription licensing. Continuous algorithm updates and new clinical application expansion further reinforce the revenue growth trajectory of the software segment.

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

Over the forecast period, the services segment is predicted to witness the highest growth rate, driven by escalating healthcare provider demand for AI implementation support, model validation services, clinical workflow integration consulting, and ongoing performance monitoring. As AI radiology deployment complexity increases and healthcare organizations expand their AI tool portfolios, demand for specialized implementation and managed service capabilities is growing commensurately. Radiology AI vendors are expanding their professional and managed service offerings to support customers across the full deployment lifecycle, from initial workflow assessment through post-market performance governance.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, supported by a high concentration of early technology adopters, mature reimbursement frameworks for advanced imaging services, and the presence of leading academic medical centers that serve as clinical validation and adoption reference sites. The United States drives the majority of regional revenues through extensive radiology AI procurement by large health systems, integrated delivery networks, and teleradiology service providers. A well-established regulatory environment for software as a medical device, combined with strong venture capital investment in radiology AI startups, sustains a dynamic innovation pipeline that continuously expands the range of clinically validated AI imaging solutions available to the market.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, driven by rapidly growing medical imaging infrastructure investment, significant radiologist workforce shortages in markets including India and Southeast Asia, and strong government interest in AI healthcare technologies as tools for improving diagnostic access. China is emerging as both a major adoption market and a significant AI radiology innovation hub, with domestic companies developing advanced imaging AI solutions targeting local and regional market needs.

Key players in the market

Some of the key players in AI-Powered Radiology Market include GE HealthCare, Zebra Medical Vision, Siemens Healthineers, Viz.ai, Philips, Aidoc, Canon Medical Systems Corporation, Qure.ai, Fujifilm Holdings Corporation, Lunit, Infervision, DeepHealth, Rad AI, Enlitic, and Arterys.

Key Developments:

In March 2026, GE HealthCare announced the commercial launch of its next-generation AI-Rad Companion platform incorporating enhanced deep learning algorithms for pulmonary nodule characterization and automated structured reporting capabilities for chest CT examinations. The platform integrates natively with GE's Revolution CT imaging systems and third-party PACS solutions, targeting improved radiologist workflow efficiency at high-volume imaging centers globally.

In February 2026, Siemens Healthineers announced CE Mark approval and commercial availability of an expanded AI-Rad Companion Chest X-ray module incorporating detection algorithms for pneumonia, pleural effusion, and pneumothorax. The module integrates with Siemens PACS infrastructure and supports deployment across hospital radiology departments and emergency imaging environments in European markets.

Components Covered:

  • Software
  • Hardware
  • Services

Technologies Covered:

  • Machine Learning
  • Deep Learning
  • Natural Language Processing (NLP)
  • Computer Vision
  • Predictive Analytics
  • Generative AI in Radiology

Deployment Modes Covered:

  • On-Premise
  • Cloud-Based
  • Hybrid Deployment

Imaging Modalities Covered:

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

Applications Covered:

  • Disease Detection & Diagnosis
  • Workflow Optimization
  • Quantitative Imaging & Measurement
  • Clinical Decision Support
  • Image Reconstruction & Enhancement
  • Predictive & Preventive Analytics

End Users Covered:

  • Hospitals
  • Diagnostic Imaging Centers
  • Specialty Clinics
  • Academic & Research Institutes
  • Ambulatory Surgical Centers

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-Powered Radiology Market, By Component

  • 5.1 Software
  • 5.2 Hardware
  • 5.3 Services

6 Global AI-Powered Radiology Market, By Technology

  • 6.1 Machine Learning
  • 6.2 Deep Learning
  • 6.3 Natural Language Processing (NLP)
  • 6.4 Computer Vision
  • 6.5 Predictive Analytics
  • 6.6 Generative AI in Radiology

7 Global AI-Powered Radiology Market, By Deployment Mode

  • 7.1 On-Premise
  • 7.2 Cloud-Based
  • 7.3 Hybrid Deployment

8 Global AI-Powered Radiology Market, By Imaging Modality

  • 8.1 X-ray
  • 8.2 Computed Tomography (CT)
  • 8.3 Magnetic Resonance Imaging (MRI)
  • 8.4 Ultrasound
  • 8.5 Mammography
  • 8.6 Nuclear Imaging
  • 8.7 PET-CT

9 Global AI-Powered Radiology Market, By Application

  • 9.1 Disease Detection & Diagnosis
  • 9.2 Workflow Optimization
  • 9.3 Quantitative Imaging & Measurement
  • 9.4 Clinical Decision Support
  • 9.5 Image Reconstruction & Enhancement
  • 9.6 Predictive & Preventive Analytics

10 Global AI-Powered Radiology 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

11 Global AI-Powered Radiology 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 Zebra Medical Vision
  • 14.3 Siemens Healthineers
  • 14.4 Viz.ai
  • 14.5 Philips
  • 14.6 Aidoc
  • 14.7 Canon Medical Systems Corporation
  • 14.8 Qure.ai
  • 14.9 Fujifilm Holdings Corporation
  • 14.10 Lunit
  • 14.11 Infervision
  • 14.12 DeepHealth
  • 14.13 Rad AI
  • 14.14 Enlitic
  • 14.15 Arterys

List of Tables

  • Table 1 Global AI-Powered Radiology Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI-Powered Radiology Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global AI-Powered Radiology Market Outlook, By Software (2023-2034) ($MN)
  • Table 4 Global AI-Powered Radiology Market Outlook, By Hardware (2023-2034) ($MN)
  • Table 5 Global AI-Powered Radiology Market Outlook, By Services (2023-2034) ($MN)
  • Table 6 Global AI-Powered Radiology Market Outlook, By Technology (2023-2034) ($MN)
  • Table 7 Global AI-Powered Radiology Market Outlook, By Machine Learning (2023-2034) ($MN)
  • Table 8 Global AI-Powered Radiology Market Outlook, By Deep Learning (2023-2034) ($MN)
  • Table 9 Global AI-Powered Radiology Market Outlook, By Natural Language Processing (NLP) (2023-2034) ($MN)
  • Table 10 Global AI-Powered Radiology Market Outlook, By Computer Vision (2023-2034) ($MN)
  • Table 11 Global AI-Powered Radiology Market Outlook, By Predictive Analytics (2023-2034) ($MN)
  • Table 12 Global AI-Powered Radiology Market Outlook, By Generative AI in Radiology (2023-2034) ($MN)
  • Table 13 Global AI-Powered Radiology Market Outlook, By Deployment Mode (2023-2034) ($MN)
  • Table 14 Global AI-Powered Radiology Market Outlook, By On-Premise (2023-2034) ($MN)
  • Table 15 Global AI-Powered Radiology Market Outlook, By Cloud-Based (2023-2034) ($MN)
  • Table 16 Global AI-Powered Radiology Market Outlook, By Hybrid Deployment (2023-2034) ($MN)
  • Table 17 Global AI-Powered Radiology Market Outlook, By Imaging Modality (2023-2034) ($MN)
  • Table 18 Global AI-Powered Radiology Market Outlook, By X-ray (2023-2034) ($MN)
  • Table 19 Global AI-Powered Radiology Market Outlook, By Computed Tomography (CT) (2023-2034) ($MN)
  • Table 20 Global AI-Powered Radiology Market Outlook, By Magnetic Resonance Imaging (MRI) (2023-2034) ($MN)
  • Table 21 Global AI-Powered Radiology Market Outlook, By Ultrasound (2023-2034) ($MN)
  • Table 22 Global AI-Powered Radiology Market Outlook, By Mammography (2023-2034) ($MN)
  • Table 23 Global AI-Powered Radiology Market Outlook, By Nuclear Imaging (2023-2034) ($MN)
  • Table 24 Global AI-Powered Radiology Market Outlook, By PET-CT (2023-2034) ($MN)
  • Table 25 Global AI-Powered Radiology Market Outlook, By Application (2023-2034) ($MN)
  • Table 26 Global AI-Powered Radiology Market Outlook, By Disease Detection & Diagnosis (2023-2034) ($MN)
  • Table 27 Global AI-Powered Radiology Market Outlook, By Workflow Optimization (2023-2034) ($MN)
  • Table 28 Global AI-Powered Radiology Market Outlook, By Quantitative Imaging & Measurement (2023-2034) ($MN)
  • Table 29 Global AI-Powered Radiology Market Outlook, By Clinical Decision Support (2023-2034) ($MN)
  • Table 30 Global AI-Powered Radiology Market Outlook, By Image Reconstruction & Enhancement (2023-2034) ($MN)
  • Table 31 Global AI-Powered Radiology Market Outlook, By Predictive & Preventive Analytics (2023-2034) ($MN)
  • Table 32 Global AI-Powered Radiology Market Outlook, By End User (2023-2034) ($MN)
  • Table 33 Global AI-Powered Radiology Market Outlook, By Hospitals (2023-2034) ($MN)
  • Table 34 Global AI-Powered Radiology Market Outlook, By Diagnostic Imaging Centers (2023-2034) ($MN)
  • Table 35 Global AI-Powered Radiology Market Outlook, By Specialty Clinics (2023-2034) ($MN)
  • Table 36 Global AI-Powered Radiology Market Outlook, By Academic & Research Institutes (2023-2034) ($MN)
  • Table 37 Global AI-Powered Radiology Market Outlook, By Ambulatory Surgical Centers (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.