查看購物車
  • English
  • Japanese
  • Korean
封面
市場調查報告書
商品編碼
2021742

2034年放射學人工智慧市場預測：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 in 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，預計到 2026 年，全球放射學人工智慧市場規模將達到 6 億美元，到 2034 年將達到 32 億美元，預測期內複合年成長率為 23.4%。

放射學中的人工智慧是指應用包括機器學習和深度學習在內的先進人工智慧技術，來輔助醫學影像資料的分析、解讀和管理。這能夠實現自動異常檢測、提升影像品質、最佳化工作流程，並為臨床決策提供支援。透過處理大量從CT、MRI和X光等影像設備取得的影像數據，人工智慧能夠幫助放射科醫師提高診斷準確率、縮短解讀時間，並透過更快速、更準確的醫學影像分析來改善患者預後。

醫學影像檢查的增加和放射科醫生的短缺

醫學影像數量的指數級成長，加上全球放射科醫生短缺，使得人工智慧驅動的工作流程解決方案的需求日益迫切。人工智慧演算法在嚴重病例分診方面表現出色，能夠幫助放射科醫師優先處理顱內出血和肺動脈栓塞等危及生命的疾病。此外，精準醫療的日益普及也推動了對人工智慧所能提供的先進影像生物標記和定量分析的需求。人工智慧在縮短檢測結果報告時間和提高診斷一致性方面已得到證實，這促使醫療機構將這些工具整合到標準診療流程中，從而進一步擴大了市場。

高昂的實施成本和互通性挑戰

將人工智慧整合到臨床放射學工作流程中面臨許多挑戰，包括高昂的實施成本以及確保與現有PACS和EHR系統互通性的必要性。資料隱私、網路安全以及與演算法偏差相關的倫理問題也構成重大挑戰。此外，缺乏針對人工智慧醫療軟體的標準化法規結構和報銷模式，為開發商和實施機構帶來了財務不確定性。臨床檢驗和能夠證明改善患者預後的積極證據仍然是廣泛應用的主要障礙。

價值醫療和個人化醫療的進展

向價值醫療模式的轉變，為放射學領域的人工智慧提供了展現其在降低成本和改善患者療效方面影響的絕佳機會。人工智慧驅動的解決方案能夠自動完成測量和記錄等常規任務，使放射科醫生能夠專注於複雜病例和直接的患者互動。整合影像資料、基因組資訊和電子健康記錄的多模態人工智慧模型的開發，有望在個人化醫療領域帶來突破性進展。新興市場也蓄勢待發，因為可擴展的雲端人工智慧解決方案有望克服傳統基礎設施的限制。

技術過時及網路安全風險

人工智慧領域的快速技術進步對現有軟體解決方案構成了過時的威脅，因此需要持續的研發投入才能保持競爭力。過度依賴人工智慧而缺乏充分的人工監督可能導致誤診和法律責任問題，從而削弱人們對該技術的信心。此外，市場整合的加速趨勢可能會限制競爭和創新。針對互聯醫療設備和人工智慧系統的網路安全威脅也會對病患資料的完整性和醫院運作構成風險，因此強力的保護措施至關重要。

新冠疫情的影響：

新冠疫情加速了人工智慧在放射學領域的應用，因為醫療系統面臨前所未有的胸部CT和X光影像量。人工智慧工具被迅速部署，用於檢測和量化病毒相關的肺部異常，從而減輕了本已不堪重負的放射科醫生的負擔。這場危機加速了監管核准流程，促使基於人工智慧的診斷工具獲得了緊急使用授權。它也凸顯了遠端和雲端解決方案的需求，從根本上推動了市場向數位轉型和分散式診斷工作流程發展。

在預測期內，軟體領域預計將佔據最大佔有率。

軟體領域預計將佔據最大的市場佔有率，這主要得益於演算法在影像分析、診斷支援和工作流程自動化中發揮的基礎性作用。這些軟體解決方案對於將原始影像資料轉化為可操作的臨床見解至關重要。用於病灶檢測和器官分割等任務的先進深度學習模型的持續發展，進一步鞏固了這一市場主導地位。由於醫院希望在不進行重大硬體升級的情況下提高放射科醫生的工作效率和診斷準確性，因此對複雜且整合化的軟體平台的需求仍然非常旺盛。

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，由於其擴充性、成本效益和促進遠端協作的能力，基於雲端的採用領域預計將呈現最高的成長率。雲端平台無需大規模的本地IT基礎設施即可實現無縫更新、集中式資料管理和運算能力部署。這種模式對新興地區尋求快速數位轉型的中小型影像中心和醫院尤其具有吸引力。遠距放射診斷的興起以及對可在多個站點使用的AI工具日益成長的需求，進一步加速了基於雲端的解決方案的採用。

市佔率最大的地區：

在整個預測期內，北美預計將保持最大的市場佔有率，這得益於其先進的醫療資訊IT基礎設施、眾多領先的人工智慧開發公司的強大影響力以及有利的報銷環境。尤其值得一提的是，美國在大型醫院網路和診斷影像中心採用人工智慧工具方面發揮著主導作用。大量的研發投入、擁有FDA核准的競爭環境以及對重視效率和準確性的價值醫療模式的高度重視，都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，這主要得益於醫療基礎設施的快速擴張和醫學影像診斷的日益普及。中國、印度和日本等國家正大力投資數位舉措和人工智慧研究。該地區龐大的人口基數、慢性病盛行率的上升以及放射科醫生短缺問題的日益嚴峻，都推動了市場需求。政府對人工智慧應用的支援以及醫療設備產業的快速發展，為市場的快速擴張創造了有利條件。

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
    • 對主要企業進行SWOT分析（最多3家公司）
  • 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
  • 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
  • 主要企業市佔率分析
  • 產品基準評效和效能比較

第5章：全球放射線人工智慧市場：按組件分類

  • 軟體
    • 影像分析軟體
    • 診斷支援軟體
    • 工作流程自動化軟體
  • 硬體
    • 人工智慧驅動的醫學影像系統
    • 邊緣運算設備
  • 服務
    • 整合與部署
    • 培訓和諮詢
    • 維護和支援

第6章：全球放射線人工智慧市場：按技術分類

  • 機器學習
  • 深度學習
  • 自然語言處理（NLP）
  • 電腦視覺
  • 情境感知計算

第7章：全球放射線人工智慧市場：依部署模式分類

  • 現場
  • 基於雲端的
  • 混合實現

第8章：全球放射線人工智慧市場：依影像方法分類

  • X光
  • 電腦斷層掃描（CT）
  • 磁振造影（MRI）
  • 超音波
  • 乳房X光檢查
  • 正子斷層掃描（PET）

第9章：全球放射學人工智慧市場：按應用領域分類

  • 檢測和診斷
  • 影像分割與量化
  • 工作流程最佳化和分診
  • 預測和預後分析
  • 治療方案
  • 監測和後續觀察
  • 其他用途

第10章：全球放射學人工智慧市場：按最終用戶分類

  • 醫院和診所
  • 診斷影像中心
  • 門診手術中心
  • 學術研究機構
  • 其他最終用戶

第11章：全球放射線人工智慧市場：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

  • Siemens Healthineers
  • GE HealthCare
  • Philips Healthcare
  • Canon Medical Systems
  • IBM
  • NVIDIA
  • Aidoc
  • Arterys
  • Viz.ai
  • Qure.ai
  • Enlitic
  • Lunit
  • Zebra Medical Vision
  • iCAD
  • Infervision
Product Code: SMRC35004

According to Stratistics MRC, the Global AI in Radiology Market is accounted for $0.6 billion in 2026 and is expected to reach $3.2 billion by 2034, growing at a CAGR of 23.4% during the forecast period. AI in Radiology is the application of advanced artificial intelligence technologies, including machine learning and deep learning, to support the analysis, interpretation, and management of medical imaging data. It enables automated identification of abnormalities, image enhancement, workflow optimization, and clinical decision support. By processing large volumes of imaging data from modalities such as CT, MRI, and X-rays, AI helps radiologists improve diagnostic accuracy, shorten interpretation time, and enhance patient outcomes through faster and more precise medical imaging insights.

Market Dynamics:

Driver:

Rising medical imaging volumes and radiologist shortages

The exponential growth in medical imaging volumes, coupled with a global shortage of radiologists, is creating an urgent need for AI-powered workflow solutions. AI algorithms excel at triaging critical cases, allowing radiologists to prioritize life-threatening conditions like intracranial hemorrhages or pulmonary embolisms. Furthermore, the push for precision medicine is driving demand for advanced imaging biomarkers and quantitative analysis that AI can provide. The proven ability of AI to reduce turnaround times and improve diagnostic consistency is compelling healthcare providers to integrate these tools into their standard practice, fueling market expansion.

Restraint:

High implementation costs and interoperability challenges

The integration of AI into clinical radiology workflows faces significant hurdles due to high implementation costs and the need for seamless interoperability with existing PACS and EHR systems. Concerns regarding data privacy, cybersecurity, and the ethical implications of algorithmic bias also pose substantial challenges. Furthermore, the lack of standardized regulatory frameworks and reimbursement models for AI-based medical software creates financial uncertainty for developers and adopters. Clinical validation and the need for prospective evidence demonstrating improved patient outcomes remain critical barriers to widespread adoption.

Opportunity:

Value-based care and personalized medicine advancements

The shift toward value-based care presents a significant opportunity for AI in radiology to demonstrate its impact on cost reduction and patient outcomes. AI-driven solutions that automate routine tasks, such as measurement and documentation, free up radiologists to focus on complex cases and direct patient interaction. The development of multimodal AI models that integrate imaging data with genomics and electronic health records offers the potential for groundbreaking advancements in personalized medicine. Emerging markets are also primed for adoption, as they seek to leapfrog traditional infrastructure limitations with scalable, cloud-based AI solutions.

Threat:

Technological obsolescence and cybersecurity risks

The rapid pace of technological advancement in AI poses a threat of obsolescence for established software solutions, requiring continuous R&D investment to remain competitive. An over-reliance on AI without adequate human oversight could lead to diagnostic errors or liability issues, eroding trust in the technology. Additionally, the market is witnessing increasing consolidation, which could limit competition and innovation. Cybersecurity threats targeting interconnected medical devices and AI systems also pose a risk to patient data integrity and hospital operations, necessitating robust protective measures.

Covid-19 Impact:

The COVID-19 pandemic acted as a catalyst for AI adoption in radiology, as healthcare systems faced unprecedented imaging volumes for chest CTs and X-rays. AI tools were rapidly deployed to assist in the detection and quantification of lung abnormalities associated with the virus, alleviating the burden on overstretched radiologists. The crisis accelerated regulatory approvals, with agencies issuing emergency use authorizations for AI-based diagnostic tools. It also highlighted the necessity of remote, cloud-based solutions, fundamentally shifting the market toward digital transformation and decentralized diagnostic workflows.

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

The software segment is anticipated to account for the largest market share, driven by the foundational role of algorithms in image analysis, diagnostic support, and workflow automation. These software solutions are essential for converting raw imaging data into actionable clinical insights. The continuous development of sophisticated deep learning models for tasks like lesion detection and organ segmentation is fueling this dominance. As hospitals seek to enhance radiologist efficiency and diagnostic accuracy without significant hardware overhauls, the demand for advanced, integrable software platforms remains exceptionally high.

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

Over the forecast period, the cloud-based deployment segment is predicted to witness the highest growth rate, attributed to its scalability, cost-effectiveness, and ability to facilitate remote collaboration. Cloud platforms enable seamless updates, centralized data management, and the deployment of computational power without substantial on-site IT infrastructure. This model is particularly attractive for smaller imaging centers and hospitals in emerging regions seeking rapid digital transformation. The shift toward teleradiology and the need for accessible AI tools across multiple facilities are further accelerating the adoption of cloud-based solutions.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, driven by its advanced healthcare IT infrastructure, strong presence of key AI developers, and favorable reimbursement landscape. The United States, in particular, leads in the adoption of AI tools across major hospital networks and imaging centers. High R&D investment, a competitive regulatory environment with FDA clearances, and a strong focus on value-based care models that reward efficiency and accuracy collectively solidify the region's dominant position.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, supported by rapidly expanding healthcare infrastructure and increasing medical imaging volumes. Countries like China, India, and Japan are investing heavily in digital health initiatives and AI research. The region's large population base, rising prevalence of chronic diseases, and a growing need to address radiologist shortages are driving demand. Government support for AI integration and a burgeoning medical device sector are creating a fertile ground for rapid market expansion.

Key players in the market

Some of the key players in AI in Radiology Market include Siemens Healthineers, GE HealthCare, Philips Healthcare, Canon Medical Systems, IBM, NVIDIA, Aidoc, Arterys, Viz.ai, Qure.ai, Enlitic, Lunit, Zebra Medical Vision, iCAD, and Infervision.

Key Developments:

In March 2026, IBM and ETH Zurich announced a 10-year collaboration to advance the next generation of algorithms at the intersection of AI and quantum computing. This initiative represents the latest milestone in the long-standing collaboration between the two institutions, further strengthening a scientific exchange that has helped create the future of information technology.

In March 2026, NVIDIA and Marvell Technology, Inc. announced a strategic partnership to connect Marvell to the NVIDIA AI factory and AI-RAN ecosystem through NVIDIA NVLink Fusion(TM), offering customers building on NVIDIA architectures greater choice and flexibility in developing next-generation infrastructure. The companies will also collaborate on silicon photonics technology.

Components Covered:

  • Software
  • Hardware
  • Services

Technologies Covered:

  • Machine Learning
  • Deep Learning
  • Natural Language Processing (NLP)
  • Computer Vision
  • Context-Aware Computing

Deployment Modes Covered:

  • On-Premises
  • Cloud-Based
  • Hybrid Deployment

Imaging Modalities Covered:

  • X-Ray
  • Computed Tomography (CT)
  • Magnetic Resonance Imaging (MRI)
  • Ultrasound
  • Mammography
  • Positron Emission Tomography (PET)

Applications Covered:

  • Detection & Diagnosis
  • Image Segmentation & Quantification
  • Workflow Optimization & Triage
  • Predictive & Prognostic Analytics
  • Treatment Planning
  • Monitoring & Follow-Up
  • Other Applications

End Users Covered:

  • Hospitals & Clinics
  • Diagnostic Imaging Centers
  • Ambulatory Surgical Centers
  • Academic & Research Institutes
  • 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 in Radiology Market, By Component

  • 5.1 Software
    • 5.1.1 Image Analysis Software
    • 5.1.2 Diagnostic Support Software
    • 5.1.3 Workflow Automation Software
  • 5.2 Hardware
    • 5.2.1 AI-Enabled Imaging Systems
    • 5.2.2 Edge Computing Devices
  • 5.3 Services
    • 5.3.1 Integration & Deployment
    • 5.3.2 Training & Consulting
    • 5.3.3 Maintenance & Support

6 Global AI in Radiology Market, By Technology

  • 6.1 Machine Learning
  • 6.2 Deep Learning
  • 6.3 Natural Language Processing (NLP)
  • 6.4 Computer Vision
  • 6.5 Context-Aware Computing

7 Global AI in Radiology Market, By Deployment Mode

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

8 Global AI in 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 Positron Emission Tomography (PET)

9 Global AI in Radiology Market, By Application

  • 9.1 Detection & Diagnosis
  • 9.2 Image Segmentation & Quantification
  • 9.3 Workflow Optimization & Triage
  • 9.4 Predictive & Prognostic Analytics
  • 9.5 Treatment Planning
  • 9.6 Monitoring & Follow-Up
  • 9.7 Other Applications

10 Global AI in Radiology Market, By End User

  • 10.1 Hospitals & Clinics
  • 10.2 Diagnostic Imaging Centers
  • 10.3 Ambulatory Surgical Centers
  • 10.4 Academic & Research Institutes
  • 10.5 Other End Users

11 Global AI in 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 Siemens Healthineers
  • 14.2 GE HealthCare
  • 14.3 Philips Healthcare
  • 14.4 Canon Medical Systems
  • 14.5 IBM
  • 14.6 NVIDIA
  • 14.7 Aidoc
  • 14.8 Arterys
  • 14.9 Viz.ai
  • 14.10 Qure.ai
  • 14.11 Enlitic
  • 14.12 Lunit
  • 14.13 Zebra Medical Vision
  • 14.14 iCAD
  • 14.15 Infervision

List of Tables

  • Table 1 Global AI in Radiology Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI in Radiology Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global AI in Radiology Market Outlook, By Software (2023-2034) ($MN)
  • Table 4 Global AI in Radiology Market Outlook, By Image Analysis Software (2023-2034) ($MN)
  • Table 5 Global AI in Radiology Market Outlook, By Diagnostic Support Software (2023-2034) ($MN)
  • Table 6 Global AI in Radiology Market Outlook, By Workflow Automation Software (2023-2034) ($MN)
  • Table 7 Global AI in Radiology Market Outlook, By Hardware (2023-2034) ($MN)
  • Table 8 Global AI in Radiology Market Outlook, By AI-Enabled Imaging Systems (2023-2034) ($MN)
  • Table 9 Global AI in Radiology Market Outlook, By Edge Computing Devices (2023-2034) ($MN)
  • Table 10 Global AI in Radiology Market Outlook, By Services (2023-2034) ($MN)
  • Table 11 Global AI in Radiology Market Outlook, By Integration & Deployment (2023-2034) ($MN)
  • Table 12 Global AI in Radiology Market Outlook, By Training & Consulting (2023-2034) ($MN)
  • Table 13 Global AI in Radiology Market Outlook, By Maintenance & Support (2023-2034) ($MN)
  • Table 14 Global AI in Radiology Market Outlook, By Technology (2023-2034) ($MN)
  • Table 15 Global AI in Radiology Market Outlook, By Machine Learning (2023-2034) ($MN)
  • Table 16 Global AI in Radiology Market Outlook, By Deep Learning (2023-2034) ($MN)
  • Table 17 Global AI in Radiology Market Outlook, By Natural Language Processing (NLP) (2023-2034) ($MN)
  • Table 18 Global AI in Radiology Market Outlook, By Computer Vision (2023-2034) ($MN)
  • Table 19 Global AI in Radiology Market Outlook, By Context-Aware Computing (2023-2034) ($MN)
  • Table 20 Global AI in Radiology Market Outlook, By Deployment Mode (2023-2034) ($MN)
  • Table 21 Global AI in Radiology Market Outlook, By On-Premises (2023-2034) ($MN)
  • Table 22 Global AI in Radiology Market Outlook, By Cloud-Based (2023-2034) ($MN)
  • Table 23 Global AI in Radiology Market Outlook, By Hybrid Deployment (2023-2034) ($MN)
  • Table 24 Global AI in Radiology Market Outlook, By Imaging Modality (2023-2034) ($MN)
  • Table 25 Global AI in Radiology Market Outlook, By X-Ray (2023-2034) ($MN)
  • Table 26 Global AI in Radiology Market Outlook, By Computed Tomography (CT) (2023-2034) ($MN)
  • Table 27 Global AI in Radiology Market Outlook, By Magnetic Resonance Imaging (MRI) (2023-2034) ($MN)
  • Table 28 Global AI in Radiology Market Outlook, By Ultrasound (2023-2034) ($MN)
  • Table 29 Global AI in Radiology Market Outlook, By Mammography (2023-2034) ($MN)
  • Table 30 Global AI in Radiology Market Outlook, By Positron Emission Tomography (PET) (2023-2034) ($MN)
  • Table 31 Global AI in Radiology Market Outlook, By Application (2023-2034) ($MN)
  • Table 32 Global AI in Radiology Market Outlook, By Detection & Diagnosis (2023-2034) ($MN)
  • Table 33 Global AI in Radiology Market Outlook, By Image Segmentation & Quantification (2023-2034) ($MN)
  • Table 34 Global AI in Radiology Market Outlook, By Workflow Optimization & Triage (2023-2034) ($MN)
  • Table 35 Global AI in Radiology Market Outlook, By Predictive & Prognostic Analytics (2023-2034) ($MN)
  • Table 36 Global AI in Radiology Market Outlook, By Treatment Planning (2023-2034) ($MN)
  • Table 37 Global AI in Radiology Market Outlook, By Monitoring & Follow-Up (2023-2034) ($MN)
  • Table 38 Global AI in Radiology Market Outlook, By Other Applications (2023-2034) ($MN)
  • Table 39 Global AI in Radiology Market Outlook, By End User (2023-2034) ($MN)
  • Table 40 Global AI in Radiology Market Outlook, By Hospitals & Clinics (2023-2034) ($MN)
  • Table 41 Global AI in Radiology Market Outlook, By Diagnostic Imaging Centers (2023-2034) ($MN)
  • Table 42 Global AI in Radiology Market Outlook, By Ambulatory Surgical Centers (2023-2034) ($MN)
  • Table 43 Global AI in Radiology Market Outlook, By Academic & Research Institutes (2023-2034) ($MN)
  • Table 44 Global AI in Radiology Market Outlook, By Other End Users (2023-2034) ($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.