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2032 年醫療診斷影像市場人工智慧 (AI) 預測：按顯像模式、AI 類型、臨床領域、部署模型、組件、應用、最終用戶和地區進行全球分析

Artificial Intelligence in Medical Imaging Market Forecasts to 2032 - Global Analysis By Imaging Modality, AI Type, Clinical Area, Deployment Model, Component, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，全球醫學影像人工智慧 (AI) 市場規模預計在 2025 年達到 19.7 億美元，到 2032 年將達到 88.1 億美元，預測期內的複合年成長率為 23.8%。

醫學影像中的人工智慧 (AI) 涉及使用先進的計算模型來評估和解讀視覺醫療數據。透過應用機器學習和深度學習技術，AI 可以提高基於影像的診斷的準確性，同時最大限度地減少人為疏忽。它能夠自動分析核磁共振成像 (MRI)、 電腦斷層掃描和 X 光等影像，從而促進更早的疾病檢測和診斷的一致性。這項技術有助於提高影像解析度，支援預測性洞察，並簡化放射學工作流程，幫助臨床醫生做出更明智的決策。

根據《柳葉刀數位健康》報導，人工智慧系統在31,000多項醫療圖像研究中實現了與專業放射科醫生相當的診斷準確率，其匯總靈敏度為87%，特異性為92%。同一項Meta分析也發現，人工智慧顯著縮短了影像解讀時間。

慢性病負擔加重，早期診斷需求增加

心血管疾病、癌症和神經系統疾病等慢性疾病的日益流行，推動了對快速精準診斷工具的需求。人工智慧驅動的醫學影像診斷能夠增強對異常的早期發現，從而實現及時干預並改善治療效果。醫療保健提供者擴大整合人工智慧，以增強放射學評估並簡化診斷工作流程。此外，人工智慧快速且準確地分析複雜影像數據的能力對於應對長期照護挑戰至關重要。

資料隱私、安全問題和碎片化資料管治

人工智慧系統嚴重依賴大量醫療資料集，這使得病患隱私問題成為亟待解決的問題。使用雲端基礎的分析和第三方平台存在未授權存取和資料外洩的風險。此外，不同機構之間管治框架的不一致也使資料共用和標準化工作變得複雜。確保遵守國際資料保護法也增加了複雜性，尤其是在跨司法管轄區部署人工智慧解決方案時。這些問題限制了人工智慧在診斷成像領域的應用速度。

擴展到新的治療領域和預測分析

人工智慧正從輔助診斷發展到透過預測模型實現主動疾病管理。其功能正在擴展到腫瘤學、心臟病學和神經影像學等領域，有助於更深入地洞察疾病進展。透過識別細微的影像生物標記物，人工智慧可以幫助臨床醫生預測潛在的健康風險並最佳化治療方案。這種不斷擴展的應用範圍為開發人員和醫療保健組織提供了超越傳統影像使用案例的創新機會。

過度依賴人工智慧與放射科醫師技能的下降

自動化系統可能導致技能退化，尤其是在常規診斷業務中。此外，由於訓練資料有偏差或品質低下，導致人工智慧輸出錯誤，從而導致臨床決策失誤。缺乏人工監督可能會增加需要細緻判斷的複雜病例的風險。向自動化轉型需要提升醫療專業人員的技能，使其能夠有效地與人工智慧工具合作。在技術支援和人工專業知識之間取得平衡至關重要，以避免損害診斷的準確性和專家能力。

COVID-19的影響：

新冠疫情加速了人工智慧在醫學影像領域的整合，尤其是在評估肺部併發症和監測病情進展方面。醫院關閉和人滿為患凸顯了對遠距離診斷解決方案和自動化分析的需求。儘管初期資源有限，但疫情刺激了人工智慧主導的影像處理平台的創新。它也加速了臨床醫生對數位診斷工具進行呼吸評估的接受度。隨著醫療保健產業轉向數位韌性，影像領域的人工智慧有望成為後疫情時代診斷的基石。

預計預測期內，電腦斷層掃描 (CT) 領域將佔據最大佔有率

預計電腦斷層掃描 (CT) 領域將在預測期內佔據最大市場佔有率，因為它能夠靈活地捕捉多個專業的高解析度解剖細節。隨著人工智慧的融入， 電腦斷層掃描的解讀變得更快、更準確，從而提高了診斷的可信度。此技術廣泛用於檢測腫瘤、血管疾病和創傷相關損傷。 CT影像中的人工智慧演算法支援自動分割、異常檢測和報告。

定量成像和生物標記部分預計將在預測期內實現最高複合年成長率

預計定量成像和生物標記領域將在預測期內實現最高成長率。這是因為人工智慧工具現在可以從影像數據中提取與疾病嚴重程度和治療反應相關的可測量指標。這些生物標記支持個人化病患監測和藥物療效評估。醫療保健機構正在投資整合成像生物標記與基因組和臨床數據進行全面分析的平台。

比最大的地區

由於醫療基礎設施的不斷擴張和技術的快速應用，預計亞太地區將在預測期內佔據最大的市場佔有率。中國、日本和印度等國的政府正在透過政策支援和官民合作關係推動人工智慧的整合。患者人數的增加和診斷服務可近性的改善正在促進該地區的成長。主導的醫學影像診斷正受到廣泛歡迎，以解決放射科醫生運轉率和診斷準確性方面的差距。

複合年成長率最高的地區：

在預測期內，北美預計將實現最高的複合年成長率，這得益於其強大的研發實力、完善的醫療網路和有利的法規。該地區擁有大量專注於開發先進影像處理演算法的人工智慧新興企業和學術機構。人工智慧在簡化臨床工作流程和解決放射科醫生短缺問題方面的效用已在美國和加拿大廣泛認可。人工智慧輔助診斷的監管發展正在推動其商業化，使北美成為全球市場成長的關鍵驅動力。

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目錄

第1章執行摘要

第2章 前言

  • 概述
  • 相關利益者
  • 調查範圍
  • 調查方法
    • 資料探勘
    • 數據分析
    • 數據檢驗
    • 研究途徑
  • 研究材料
    • 主要研究資料
    • 次級研究資訊來源
    • 先決條件

第3章市場走勢分析

  • 驅動程式
  • 抑制因素
  • 機會
  • 威脅
  • 應用分析
  • 最終用戶分析
  • 新興市場
  • COVID-19的影響

第4章 波特五力分析

  • 供應商的議價能力
  • 買家的議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭對手之間的競爭

5. 全球醫學影像人工智慧（AI）在顯像模式上的應用

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

6. 全球醫學影像診斷人工智慧（AI）依類型

  • 機器學習
  • 深度學習
  • 自然語言處理（NLP）
  • 電腦視覺

第7章。按臨床領域分類的醫學影像診斷中的全球人工智慧（AI）

  • 放射學
  • 心臟病學
  • 神經病學
  • 乳癌篩檢/乳房攝影
  • 腫瘤學
  • 呼吸系統和肺
  • 整形外科
  • 其他臨床領域

第 8 章：全球醫學影像診斷人工智慧 (AI) 部署模式

  • 本地
  • 雲端基礎
  • 混合

9. 全球醫學影像人工智慧（AI）組件

  • 軟體
    • 人工智慧演算法和模型
    • 人工智慧平台和框架
    • 配備人工智慧的應用程式
  • 硬體
    • 專用處理器
    • 高效能運算 (HPC) 系統
    • 儲存解決方案
    • AI融合影像設備
  • 服務
    • 諮詢服務
    • 實施和整合服務
    • 資料註釋和管理服務

第 10 章。全球人工智慧（AI）在醫學影像中的應用

  • 影像分析與解釋
  • 電腦輔助診斷（CAD）
  • 定量成像和生物標記
  • 檢測和分類
  • 工作流程最佳化和分類
  • 其他用途

第 11 章。全球醫學影像診斷中的人工智慧 (AI)（按最終用戶）

  • 醫院
  • 診斷影像中心
  • 研究和學術機構
  • 製藥和生物技術公司
  • 其他最終用戶

第 12 章：全球醫學影像人工智慧 (AI) 區域分佈

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

第13章 重大進展

  • 協議、夥伴關係、合作和合資企業
  • 收購與合併
  • 新產品發布
  • 業務擴展
  • 其他關鍵策略

第12章 公司概況

  • Aidoc
  • Arterys
  • Avicenna.AI
  • Canon Medical Systems Corporation
  • CureMetrix
  • Enlitic
  • GE HealthCare
  • HeartFlow Inc.
  • IBM Watson Health
  • Infervision
  • Lunit Inc.
  • Philips Healthcare
  • Qure.ai
  • RadNet
  • Riverain Technologies
  • ScreenPoint Medical
  • Siemens Healthineers
  • Therapixel
  • Zebra Medical Vision
Product Code: SMRC30097

According to Stratistics MRC, the Global Artificial Intelligence (AI) in Medical Imaging Market is accounted for $1.97 billion in 2025 and is expected to reach $8.81 billion by 2032 growing at a CAGR of 23.8% during the forecast period. Artificial Intelligence (AI) in medical imaging involves leveraging advanced computational models to evaluate and interpret visual healthcare data. By applying machine learning and deep learning techniques, AI enhances the precision of image-based diagnostics while minimizing human oversight. It enables automated analysis of modalities like MRI, CT scans, and X-rays, facilitating early disease detection and diagnostic consistency. The technology contributes to improved image resolution, supports predictive insights, and streamlines radiology workflows to assist clinicians in making more informed decisions.

According to The Lancet Digital Health, AI systems achieved diagnostic accuracy comparable to expert radiologists, with pooled sensitivity of 87% and specificity of 92% across over 31,000 medical imaging cases. According to the same meta-analysis, AI also significantly reduced image interpretation time.

Market Dynamics:

Driver:

Rising burden of chronic diseases and demand for early diagnosis

The increasing prevalence of chronic ailments such as cardiovascular conditions, cancer, and neurological disorders has heightened the need for prompt and accurate diagnostic tools. AI-powered medical imaging enhances the detection of anomalies at early stages, allowing for timely intervention and improved treatment outcomes. Healthcare providers are increasingly integrating AI to augment radiological assessments and streamline diagnostic workflows. Moreover AI's ability to analyze complex imaging data swiftly and precisely makes it vital in addressing long-term care challenges.

Restraint:

Data privacy, security concerns, and fragmented data governance

As AI systems rely heavily on vast medical datasets, safeguarding patient privacy has become a pressing issue. The use of cloud-based analytics and third-party platforms introduces risks related to unauthorized access and data breaches. Moreover, inconsistent governance frameworks across institutions complicate data sharing and standardization efforts. Ensuring compliance with international data protection laws adds complexity, especially when deploying AI solutions across different jurisdictions. These concerns collectively restrict the pace of AI adoption in imaging diagnostics.

Opportunity:

Expansion into new therapeutic areas and predictive analytics

AI is evolving from supporting diagnostics to enabling proactive disease management through predictive modeling. Its capabilities are extending to areas such as oncology, cardiology, and neuroimaging, facilitating deeper insights into disease progression. By recognizing subtle imaging biomarkers, AI assists clinicians in forecasting potential health risks and refining treatment plans. This broadening scope presents opportunities for developers and healthcare institutions to innovate beyond traditional imaging use cases.

Threat:

Over-reliance on AI and deskilling of radiologists

Automated systems may cause skill erosion, especially in routine diagnostic tasks. Furthermore, incorrect AI outputs due to biased or poor-quality training data can mislead clinical decisions. A lack of human oversight might increase risks in complex cases requiring nuanced judgment. The shift toward automation necessitates upskilling medical professionals to effectively collaborate with AI tools. Maintaining a balance between technology support and human expertise is essential to avoid undermining diagnostic accuracy and professional competency.

Covid-19 Impact:

The COVID-19 crisis accelerated the integration of AI in medical imaging, especially for assessing lung complications and monitoring disease progression. Lockdowns and hospital overcrowding emphasized the need for remote diagnostic solutions and automated analysis. Despite initial resource constraints, the pandemic catalyzed innovation in AI-driven imaging platforms. It also fostered acceptance among clinicians of digital diagnostic tools for respiratory assessments. As the healthcare sector pivots toward digital resilience, AI in imaging is expected to become a cornerstone of post-pandemic diagnostics.

The computed tomography (CT) segment is expected to be the largest during the forecast period

The computed tomography (CT) segment is expected to account for the largest market share during the forecast period due to its versatility in capturing high-resolution anatomical details across multiple specialties. With the integration of AI, CT scan interpretation has become faster and more accurate, enhancing diagnostic confidence. The modality is widely used for detecting tumors, vascular diseases, and trauma-related injuries. AI algorithms in CT imaging support automated segmentation, anomaly detection, and report generation.

The quantitative imaging & biomarkers segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the quantitative imaging & biomarkers segment is predicted to witness the highest growth rate because AI tools are now capable of extracting measurable indicators from imaging data that correlate with disease severity or response to treatment. These biomarkers support individualized patient monitoring and drug efficacy evaluation. Healthcare institutions are investing in platforms that integrate imaging biomarkers with genomic and clinical data for comprehensive analysis.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share owing to its expanding healthcare infrastructure and rapid technology adoption. Governments across countries like China, Japan, and India are promoting AI integration through policy support and public-private partnerships. Rising patient volumes and improving access to diagnostic services are contributing to regional growth. AI-driven medical imaging is being embraced to address disparities in radiologist availability and diagnostic accuracy.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR fueled by robust R&D, established healthcare networks, and favorable regulations. The region hosts numerous AI startups and academic institutions focused on developing advanced imaging algorithms. AI's utility in streamlining clinical workflows and addressing radiologist shortages is well recognized in the U.S. and Canada. Regulatory progress in AI-enabled diagnostics supports commercialization, positioning North America as a key accelerator of global market growth.

Key players in the market

Some of the key players in Artificial Intelligence (AI) in Medical Imaging Market include Aidoc, Arterys, Avicenna.AI, Canon Medical Systems Corporation, CureMetrix, Enlitic, GE HealthCare, HeartFlow Inc., IBM Watson Health, Infervision, Lunit Inc., Philips Healthcare, Qure.ai, RadNet, Riverain Technologies, ScreenPoint Medical, Siemens Healthineers, Therapixel and Zebra Medical Vision.

Key Developments:

In June 2025, Qure.ai launches AIRA AI-powered co-pilot at the World Health Assembly. The tool aims to reduce manual workload-freeing time for direct patient care responding to the WHO's call for improved health equity.

In May 2025, GE HealthCare unveils enterprise imaging workflow efficiency solutions, introducing a suite of digital tools to optimize imaging operations and support enterprise-level deployments.

In January 2025, Aidoc announces strategic collaboration with AWS to enhance its CARE(TM) Foundation Model using Amazon Web Services' cloud and engineering scale, aiming to deliver real-time clinical AI across multiple imaging modalities.

Imaging Modalities Covered:

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

AI Types Covered:

  • Machine Learning
  • Deep Learning
  • Natural Language Processing (NLP)
  • Computer Vision

Clinical Areas Covered:

  • Radiology
  • Cardiology
  • Neurology
  • Breast Screening/Breast Imaging
  • Oncology
  • Respiratory & Pulmonary
  • Orthopedics
  • Other Clinical Areas

Deployment Models Covered:

  • On-Premise
  • Cloud-based
  • Hybrid

Components Covered:

  • Software
  • Hardware
  • Services

Applications Covered:

  • Image analysis & Interpretation
  • Computer-Aided Diagnosis (CAD)
  • Quantitative Imaging & Biomarkers
  • Detection & Classification
  • Workflow Optimization & Triage
  • Other Applications

End Users Covered:

  • Hospitals
  • Diagnostic Imaging Centers
  • Research Laboratories & Academic Institutions
  • Pharmaceutical & Biotechnology Companies
  • Other End Users

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

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

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

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Application Analysis
  • 3.7 End User Analysis
  • 3.8 Emerging Markets
  • 3.9 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global Artificial Intelligence (AI) in Medical Imaging Market, By Imaging Modality

  • 5.1 Introduction
  • 5.2 X-Ray
  • 5.3 Computed Tomography (CT)
  • 5.4 Magnetic Resonance Imaging (MRI)
  • 5.5 Ultrasound
  • 5.6 Positron Emission Tomography (PET)
  • 5.7 Mammography
  • 5.8 Other Imaging Modalities

6 Global Artificial Intelligence (AI) in Medical Imaging Market, By AI Type

  • 6.1 Introduction
  • 6.2 Machine Learning
  • 6.3 Deep Learning
  • 6.4 Natural Language Processing (NLP)
  • 6.5 Computer Vision

7 Global Artificial Intelligence (AI) in Medical Imaging Market, By Clinical Area

  • 7.1 Introduction
  • 7.2 Radiology
  • 7.3 Cardiology
  • 7.4 Neurology
  • 7.5 Breast Screening/Breast Imaging
  • 7.6 Oncology
  • 7.7 Respiratory & Pulmonary
  • 7.8 Orthopedics
  • 7.9 Other Clinical Areas

8 Global Artificial Intelligence (AI) in Medical Imaging Market, By Deployment Model

  • 8.1 Introduction
  • 8.2 On-Premise
  • 8.3 Cloud-based
  • 8.4 Hybrid

9 Global Artificial Intelligence (AI) in Medical Imaging Market, By Component

  • 9.1 Introduction
  • 9.2 Software
    • 9.2.1 AI Algorithms & Models
    • 9.2.2 AI Platforms & Frameworks
    • 9.2.3 AI-Powered Applications
  • 9.3 Hardware
    • 9.3.1 Specialized Processors
    • 9.3.2 High-Performance Computing (HPC) Systems
    • 9.3.3 Storage Solutions
    • 9.3.4 AI-Integrated Imaging Devices
  • 9.4 Services
    • 9.4.1 Consulting Services
    • 9.4.2 Implementation & Integration Services
    • 9.4.3 Data Annotation & Curation Services

10 Global Artificial Intelligence (AI) in Medical Imaging Market, By Application

  • 10.1 Introduction
  • 10.2 Image analysis & Interpretation
  • 10.3 Computer-Aided Diagnosis (CAD)
  • 10.4 Quantitative Imaging & Biomarkers
  • 10.5 Detection & Classification
  • 10.6 Workflow Optimization & Triage
  • 10.7 Other Applications

11 Global Artificial Intelligence (AI) in Medical Imaging Market, By End User

  • 11.1 Introduction
  • 11.2 Hospitals
  • 11.3 Diagnostic Imaging Centers
  • 11.4 Research Laboratories & Academic Institutions
  • 11.5 Pharmaceutical & Biotechnology Companies
  • 11.6 Other End Users

12 Global Artificial Intelligence (AI) in Medical Imaging Market, By Geography

  • 12.1 Introduction
  • 12.2 North America
    • 12.2.1 US
    • 12.2.2 Canada
    • 12.2.3 Mexico
  • 12.3 Europe
    • 12.3.1 Germany
    • 12.3.2 UK
    • 12.3.3 Italy
    • 12.3.4 France
    • 12.3.5 Spain
    • 12.3.6 Rest of Europe
  • 12.4 Asia Pacific
    • 12.4.1 Japan
    • 12.4.2 China
    • 12.4.3 India
    • 12.4.4 Australia
    • 12.4.5 New Zealand
    • 12.4.6 South Korea
    • 12.4.7 Rest of Asia Pacific
  • 12.5 South America
    • 12.5.1 Argentina
    • 12.5.2 Brazil
    • 12.5.3 Chile
    • 12.5.4 Rest of South America
  • 12.6 Middle East & Africa
    • 12.6.1 Saudi Arabia
    • 12.6.2 UAE
    • 12.6.3 Qatar
    • 12.6.4 South Africa
    • 12.6.5 Rest of Middle East & Africa

13 Key Developments

  • 13.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 13.2 Acquisitions & Mergers
  • 13.3 New Product Launch
  • 13.4 Expansions
  • 13.5 Other Key Strategies

12 Company Profiling

  • 12.1 Aidoc
  • 12.2 Arterys
  • 12.3 Avicenna.AI
  • 12.4 Canon Medical Systems Corporation
  • 12.5 CureMetrix
  • 12.6 Enlitic
  • 12.7 GE HealthCare
  • 12.8 HeartFlow Inc.
  • 12.9 IBM Watson Health
  • 12.10 Infervision
  • 12.11 Lunit Inc.
  • 12.12 Philips Healthcare
  • 12.13 Qure.ai
  • 12.14 RadNet
  • 12.15 Riverain Technologies
  • 12.16 ScreenPoint Medical
  • 12.17 Siemens Healthineers
  • 12.18 Therapixel
  • 12.19 Zebra Medical Vision

List of Tables

  • Table 1 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Imaging Modality (2024-2032) ($MN)
  • Table 3 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By X-Ray (2024-2032) ($MN)
  • Table 4 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Computed Tomography (CT) (2024-2032) ($MN)
  • Table 5 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Magnetic Resonance Imaging (MRI) (2024-2032) ($MN)
  • Table 6 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Ultrasound (2024-2032) ($MN)
  • Table 7 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Positron Emission Tomography (PET) (2024-2032) ($MN)
  • Table 8 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Mammography (2024-2032) ($MN)
  • Table 9 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Other Imaging Modalities (2024-2032) ($MN)
  • Table 10 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By AI Type (2024-2032) ($MN)
  • Table 11 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Machine Learning (2024-2032) ($MN)
  • Table 12 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Deep Learning (2024-2032) ($MN)
  • Table 13 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Natural Language Processing (NLP) (2024-2032) ($MN)
  • Table 14 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Computer Vision (2024-2032) ($MN)
  • Table 15 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Clinical Area (2024-2032) ($MN)
  • Table 16 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Radiology (2024-2032) ($MN)
  • Table 17 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Cardiology (2024-2032) ($MN)
  • Table 18 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Neurology (2024-2032) ($MN)
  • Table 19 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Breast Screening/Breast Imaging (2024-2032) ($MN)
  • Table 20 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Oncology (2024-2032) ($MN)
  • Table 21 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Respiratory & Pulmonary (2024-2032) ($MN)
  • Table 22 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Orthopedics (2024-2032) ($MN)
  • Table 23 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Other Clinical Areas (2024-2032) ($MN)
  • Table 24 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Deployment Model (2024-2032) ($MN)
  • Table 25 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By On-Premise (2024-2032) ($MN)
  • Table 26 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Cloud-based (2024-2032) ($MN)
  • Table 27 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Hybrid (2024-2032) ($MN)
  • Table 28 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Component (2024-2032) ($MN)
  • Table 29 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Software (2024-2032) ($MN)
  • Table 30 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By AI Algorithms & Models (2024-2032) ($MN)
  • Table 31 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By AI Platforms & Frameworks (2024-2032) ($MN)
  • Table 32 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By AI-Powered Applications (2024-2032) ($MN)
  • Table 33 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Hardware (2024-2032) ($MN)
  • Table 34 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Specialized Processors (2024-2032) ($MN)
  • Table 35 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By High-Performance Computing (HPC) Systems (2024-2032) ($MN)
  • Table 36 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Storage Solutions (2024-2032) ($MN)
  • Table 37 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By AI-Integrated Imaging Devices (2024-2032) ($MN)
  • Table 38 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Services (2024-2032) ($MN)
  • Table 39 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Consulting Services (2024-2032) ($MN)
  • Table 40 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Implementation & Integration Services (2024-2032) ($MN)
  • Table 41 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Data Annotation & Curation Services (2024-2032) ($MN)
  • Table 42 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Application (2024-2032) ($MN)
  • Table 43 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Image analysis & Interpretation (2024-2032) ($MN)
  • Table 44 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Computer-Aided Diagnosis (CAD) (2024-2032) ($MN)
  • Table 45 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Quantitative Imaging & Biomarkers (2024-2032) ($MN)
  • Table 46 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Detection & Classification (2024-2032) ($MN)
  • Table 47 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Workflow Optimization & Triage (2024-2032) ($MN)
  • Table 48 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Other Applications (2024-2032) ($MN)
  • Table 49 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By End User (2024-2032) ($MN)
  • Table 50 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Hospitals (2024-2032) ($MN)
  • Table 51 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Diagnostic Imaging Centers (2024-2032) ($MN)
  • Table 52 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Research Laboratories & Academic Institutions (2024-2032) ($MN)
  • Table 53 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Pharmaceutical & Biotechnology Companies (2024-2032) ($MN)
  • Table 54 Global Artificial Intelligence (AI) in Medical Imaging Market Outlook, By Other End Users (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.