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市場調查報告書
商品編碼
1919789

全球醫療人工智慧市場（至 2040 年）：按平台類型、組件類型、應用類型、技術類型、最終用戶、主要地區、主要參與者、行業趨勢和預測

Artificial Intelligence (AI) in Healthcare Market, till 2040: Distribution by Type of Platform, Type of Component, Type of Application, Type of Technology, End User, Key Geographical Regions and Leading Players: Industry Trends and Global Forecasts
出版日期: | 出版商: Roots Analysis | 英文 198 Pages | 商品交期: 7-10個工作天內

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簡介目錄

醫療人工智慧市場展望

預計到 2040 年，全球醫療人工智慧市場規模將從目前的 485 億美元增長至 19,234 億美元，預測期內複合年增長率 (CAGR) 為 30%。

人工智慧正在迅速改變醫療保健產業，改善疾病在臨床環境中的檢測、治療和管理方式。這使臨床醫生能夠更有效率地工作，同時為患者提供更安全、更個人化的照護。人工智慧系統可以分析大量的醫療數據，包括影像、實驗室數值和電子健康記錄，以識別可能預示疾病早期跡象的細微模式。在放射學、病理學、心臟病學和腫瘤學領域，演算法正在透過提高診斷準確性和降低人為錯誤風險來輔助臨床醫生。除了診斷之外，人工智慧還能幫助制定個人化治療方案、預測疾病進展並優化藥物選擇，從而支持向精準醫療的更廣泛轉變。

在營運方面，人工智慧透過自動化臨床文件、虛擬助理和整合到醫院資訊系統中的決策支援工具來簡化工作流程。這些應用減輕了行政負擔，使醫療專業人員能夠更專注於直接的病患照護和複雜的決策。同時，透過人工智慧穿戴裝置進行的遠端患者監測有助於持續追蹤生命徵象並及早發現併發症，從而將醫療保健從被動應對轉變為主動預防。考慮到以上因素，預計醫療保健人工智慧市場在預測期內將經歷顯著增長。

醫療保健市場中的人工智慧 (AI) - 圖片 1

高階主管的策略洞察

人工智慧在遠端監測和個人化醫療中的變革性作用

人工智慧透過實現持續、數據驅動的醫療服務，增強了遠距患者監測 (RPM) 和個人化醫療。穿戴式裝置和居家感光元件即時追蹤心率、血壓、血氧飽和度和血糖值等生命徵象。人工智慧演算法能夠偵測細微的異常情況，例如心律不整和血氧飽和度下降，並及時向醫療專業人員發出警報，從而促進早期幹預。這優化了門診環境下慢性疾病（例如糖尿病、心血管疾病和慢性阻塞性肺病 (COPD)）的管理。

在個人化醫療中，人工智慧整合了個人的基因組圖譜、生活方式因素和歷史健康數據，以製定個人化的優化治療策略，預測治療結果，並動態調整劑量和治療方案。這些功能的結合使用可降低醫療成本，擴大醫療服務的覆蓋範圍，並促進居家醫療模式的發展。

醫療保健人工智慧市場的主要成長驅動因素

醫療保健人工智慧的成長受多種因素驅動，包括糖尿病和心血管疾病等慢性疾病的日益增多以及全球人口老化。這為先進的診斷和管理解決方案創造了巨大的需求。來自電子健康記錄、穿戴式裝置和診斷測試的大量健康數據集的湧現，使人工智慧能夠改善預測分析、診斷和個人化治療。機器學習演算法的進步、經濟高效的運算基礎設施和可擴展的雲端技術正在推動人工智慧的廣泛應用。臨床人才短缺正在推動日常工作的自動化，從而降低成本並加速藥物研發進程。大量的風險投資和企業投資進一步刺激了該市場的創新和成長。

醫療保健領域人工智慧的演進：產業新興趨勢

醫療保健領域人工智慧的新興趨勢包括：先進的可穿戴設備，能夠實現即時健康監測，並主動提醒臨床醫生注意心律不整等異常情況。生成式人工智慧有助於實現臨床文件的自動化，產生用於安全模型訓練的合成患者數據，並加速藥物研發進程。此外，自主人工智慧代理可作為虛擬助手，簡化預約安排和治療後追蹤流程。增強型診斷利用人工智慧透過影像技術快速檢測惡性腫瘤和腦血管疾病，並輔以精準醫療，根據基因組資訊和生活方式特徵量身訂做治療方案。預測分析透過早期預警降低敗血症等疾病的風險，而增強型遠距醫療則整合了人工智慧聊天機器人和虛擬護理，以優化效率、成本效益和全球可及性。這些進步使人工智慧成為下一代醫療保健的基礎，推動了患者療效、營運效率和全球公平醫療服務取得的變革性改善。

本報告研究了全球醫療保健人工智慧市場，提供了市場規模估算、機會分析、競爭格局和公司概況。

目錄

第一部分：報告概述

第一章：引言

第二章：研究方法

第三章：市場動態

第四章：宏觀經濟指標

第二部分：質性研究結果

第五章：摘要整理

第六章：引言

第七章：監理環境

第三部分：市場概覽

第八章：關鍵指標綜合資料庫

第九章：競爭格局

第十章：市場空白分析

第十一章：競爭分析

第十二章：醫療人工智慧市場的新創企業生態系統

第四部分：公司簡介

第十三章：公司簡介

  • 章節概述
  • Google
  • 通用電氣醫療保健
  • IBM
  • 英特爾
  • 伊特雷克斯
  • IQVIA
  • 微軟
  • 美敦力
  • 醫療數據
  • 默克
  • 英偉達
  • 甲骨文

第 5 部分：市場趨勢

第 14 章：大趨勢分析

第 15 章：專利分析

第 16 章：最新進展

第 6 部分：市場機會分析

第 17 章：全球醫療保健人工智慧市場

第 18 章：平台市場機會類型

第19章：依組件類型劃分的市場機會

第20章：按應用類型劃分的市場機會

第21章：依技術類型劃分的市場機會

第22章：依最終用戶劃分的市場機會

第23章：北美醫療保健人工智慧市場機會

第24章：歐洲醫療保健人工智慧市場機會

第25章：亞洲醫療保健人工智慧市場機會

第26章：中東和北非（MENA）醫療保健人工智慧市場機會

第27章：市場機會拉丁美洲醫療保健人工智慧市場

第28章：其他地區醫療保健人工智慧市場的市場機會

第29章：主要參與者的市場集中度分析

第30章：鄰近市場分析

第7節：策略工具

第31章：關鍵成功策略

第32章：波特五力分析

第33章：SWOT分析

第34章：Roots策略建議

第8節：其他獨家發現

第35章：一手調查結果

第36章：報告結論

第9節：附錄

簡介目錄
Product Code: RAU1140

AI in Healthcare Market Outlook

As per Roots Analysis, the global AI in healthcare market size is estimated to grow from USD 48.5 billion in the current year to USD 1,923.4 billion by 2040, at a CAGR of 30% during the forecast period, till 2040. The new study provides market size, growth scenarios, industry trends and future forecasts.

Artificial intelligence is rapidly reshaping healthcare by improving how diseases are detected, treated, and managed across care settings. It enables clinicians to work more efficiently while offering patients safer, more personalized care. AI systems can analyze vast volumes of medical data, such as images, lab values, and electronic health records, to identify subtle patterns that may signal early disease. In radiology, pathology, cardiology, and oncology, algorithms support clinicians by enhancing diagnostic accuracy and reducing the risk of human error. Beyond diagnosis, AI helps design tailored treatment plans, predict disease progression, and optimize drug selection, supporting the broader move toward precision medicine.

Operationally, AI streamlines workflows through automated clinical documentation, virtual assistants, and decision-support tools embedded in hospital information systems. These applications reduce administrative burden, allowing healthcare professionals to focus more on direct patient interaction and complex decision-making. At the same time, remote patient monitoring powered by AI-enabled wearables supports continuous tracking of vital signs and early detection of complications, shifting healthcare from reactive to preventive models. Considering the above mentioned factors, the AI in healthcare market is expected to grow significantly throughout the forecast period.

Artificial Intelligence (AI) in Healthcare Market - IMG1

Strategic Insights for Senior Leaders

Transformative Role of Artificial Intelligence in Remote Monitoring and Personalized Medicine

Artificial Intelligence (AI) enhances remote patient monitoring (RPM) and personalized medicine by enabling continuous, data-driven care delivery. Wearable devices and home sensors track vital signs, including heart rate, blood pressure, oxygen saturation, and glucose levels in real time. AI algorithms detect subtle anomalies, such as irregular cardiac rhythms or declining oxygenation, triggering timely alerts to clinicians and facilitating early interventions. This optimizes management of chronic conditions like diabetes, cardiovascular disease, and chronic obstructive pulmonary disease (COPD) in outpatient settings.

In personalized medicine, AI integrates individual genomic profiles, lifestyle factors, and historical health data to develop tailored treatment strategies, forecast therapeutic responses, and dynamically adjust dosages or regimens. Collectively, these capabilities reduce healthcare costs, expand access, and facilitate home-based models.

Key Drivers Propelling Growth of AI in Healthcare Market

The growth of artificial intelligence (AI) in healthcare is being driven by several key factors including the rising prevalence of chronic diseases, such as diabetes and cardiovascular conditions along with a globally aging population. This generates substantial demand for advanced diagnostic and management solutions. The proliferation of vast health datasets from electronic health records, wearables, and diagnostic tests enables AI to enhance predictive analytics, diagnostics, and personalized therapies. Advancements in machine learning algorithms, cost-effective computing infrastructure, and scalable cloud technologies facilitate broader AI adoption. Workforce shortages among clinicians drive automation of routine tasks, cost reductions, and accelerated drug discovery processes. Substantial venture capital and corporate investments further fuels innovation and growth within this market.

AI in Healthcare Evolution: Emerging Trends in the Industry

Emerging trends in artificial intelligence (AI) for healthcare include advanced wearable devices that enable real-time health monitoring and proactive clinician alerts for anomalies such as arrhythmias. Generative AI facilitates automated clinical documentation, synthetic patient data generation for secure model training, and accelerated drug discovery pipelines. Further, autonomous AI agents function as virtual assistants, streamlining appointment scheduling and post-care follow-up. Enhanced diagnostics leverage AI for rapid detection of malignancies and cerebrovascular events in imaging. complemented by precision medicine that customizes therapies based on genomic and lifestyle profiles. Predictive analytics mitigates risks like sepsis through early warnings, while expanded remote care integrates AI-driven chatbots and virtual nursing, optimizing efficiency, cost-effectiveness, and global accessibility. These advancements collectively position AI as a cornerstone of next-generation healthcare, driving transformative improvements in patient outcomes, operational efficiency, and equitable access worldwide.

Key Market Challenges

The AI in healthcare market faces several critical challenges that hinder its full-scale adoption. These include data quality issues, with healthcare datasets frequently fragmented, incomplete, which complicates accurate model training. Privacy and cybersecurity vulnerabilities are also prominent, as patient data demands stringent protection against breaches. Further, high implementation costs, regulatory ambiguity, and ethical concerns over accountability foster reluctance among clinicians and institutions. To overcome these issues, the industry needs better data standards, skilled professionals, and strong policies to unlock the full potential of AI in healthcare.

AI in Healthcare Market: Key Market Segmentation

Type of Platform

  • Solutions
  • Services

Type of Component

  • Hardware
  • Software Solution
  • Services
  • Others

Type of Application

  • Robot-Assisted Surgery
  • Virtual Assistants
  • Administrative Workflow Assistants
  • Connected Medical Devices
  • Medical Imaging & Diagnostics
  • Clinical Trials
  • Fraud Detection
  • Cybersecurity
  • Dosage Error Reduction
  • Precision Medicine
  • Drug Discovery & Development
  • Lifestyle Management & Remote Patient Monitoring Wearables
  • Other Applications

Type of Technology

  • Machine Learning
  • Natural Language Processing
  • Context-aware Computing
  • Computer Vision

End User

  • Healthcare Providers
  • Healthcare Payers
  • Healthcare Companies
  • Patients
  • Other End Users

Geographical Regions

  • North America
  • US
  • Canada
  • Mexico
  • Other North American countries
  • Europe
  • Austria
  • Belgium
  • Denmark
  • France
  • Germany
  • Ireland
  • Italy
  • Netherlands
  • Norway
  • Russia
  • Spain
  • Sweden
  • Switzerland
  • UK
  • Other European countries
  • Asia
  • China
  • India
  • Japan
  • Singapore
  • South Korea
  • Other Asian countries
  • Latin America
  • Brazil
  • Chile
  • Colombia
  • Venezuela
  • Other Latin American countries
  • Middle East and North Africa
  • Egypt
  • Iran
  • Iraq
  • Israel
  • Kuwait
  • Saudi Arabia
  • UAE
  • Other MENA countries
  • Rest of the World
  • Australia
  • New Zealand
  • Other countries

Example Players in AI in Healthcare Market

  • Google
  • GE Healthcare
  • IBM
  • Intel
  • Itrex
  • IQVIA
  • Microsoft
  • Medtronic
  • Medidata
  • Merck
  • NVIDIA
  • Oracle

AI in Healthcare Market: Report Coverage

The report on the AI in healthcare market features insights on various sections, including:

  • Market Sizing and Opportunity Analysis: An in-depth analysis of the AI in healthcare market, focusing on key market segments, including [A] type of platform, [B] type of component, [C] type of application, [D] type of technology, [E] end user, and [F] key geographical regions.
  • Competitive Landscape: A comprehensive analysis of the companies engaged in the AI in healthcare market, based on several relevant parameters, such as [A] year of establishment, [B] company size, [C] location of headquarters and [D] ownership structure.
  • Company Profiles: Elaborate profiles of prominent players engaged in the AI in healthcare market, providing details on [A] location of headquarters, [B] company size, [C] company mission, [D] company footprint, [E] management team, [F] contact details, [G] financial information, [H] operating business segments, [I] technology / platform portfolio, [J] recent developments, and an informed future outlook.
  • Megatrends: An evaluation of ongoing megatrends in the AI in healthcare industry.
  • Patent Analysis: An insightful analysis of patents filed / granted in the AI in healthcare domain, based on relevant parameters, including [A] type of patent, [B] patent publication year, [C] patent age and [D] leading players.
  • Recent Developments: An overview of the recent developments made in the AI in healthcare market, along with analysis based on relevant parameters, including [A] year of initiative, [B] type of initiative, [C] geographical distribution and [D] most active players.
  • Porter's Five Forces Analysis: An analysis of five competitive forces prevailing in the AI in healthcare market, including threats of new entrants, bargaining power of buyers, bargaining power of suppliers, threats of substitute products and rivalry among existing competitors.
  • SWOT Analysis: An insightful SWOT framework, highlighting the strengths, weaknesses, opportunities and threats in the domain. Additionally, it provides Harvey ball analysis, highlighting the relative impact of each SWOT parameter.

Key Questions Answered in this Report

  • What is the current and future market size?
  • Who are the leading companies in this market?
  • What are the growth drivers that are likely to influence the evolution of this market?
  • What are the key partnership and funding trends shaping this industry?
  • Which region is likely to grow at higher CAGR till 2040?
  • How is the current and future market opportunity likely to be distributed across key market segments?

Reasons to Buy this Report

  • Detailed Market Analysis: The report provides a comprehensive market analysis, offering detailed revenue projections of the overall market and its specific sub-segments. This information is valuable to both established market leaders and emerging entrants.
  • In-depth Analysis of Trends: Stakeholders can leverage the report to gain a deeper understanding of the competitive dynamics within the market. Each report maps ecosystem activity across partnerships, funding, and patent landscapes to reveal growth hotspots and white spaces in the industry.
  • Opinion of Industry Experts: The report features extensive interviews and surveys with key opinion leaders and industry experts to validate market trends mentioned in the report.
  • Decision-ready Deliverables: The report offers stakeholders with strategic frameworks (Porter's Five Forces, value chain, SWOT), and complimentary Excel / slide packs with customization support.

Additional Benefits

  • Complimentary Dynamic Excel Dashboards for Analytical Modules
  • Exclusive 15% Free Content Customization
  • Personalized Interactive Report Walkthrough with Our Expert Research Team
  • Free Report Updates for Versions Older than 6-12 Months

TABLE OF CONTENTS

SECTION I: REPORT OVERVIEW

1. PREFACE

  • 1.1. Introduction
  • 1.2. Market Share Insights
  • 1.3. Key Market Insights
  • 1.4. Report Coverage
  • 1.5. Key Questions Answered
  • 1.6. Chapter Outlines

2. RESEARCH METHODOLOGY

  • 2.1. Chapter Overview
  • 2.2. Research Assumptions
  • 2.3. Database Building
    • 2.3.1. Data Collection
    • 2.3.2. Data Validation
    • 2.3.3. Data Analysis
  • 2.4. Project Methodology
    • 2.4.1. Secondary Research
      • 2.4.1.1. Annual Reports
      • 2.4.1.2. Academic Research Papers
      • 2.4.1.3. Company Websites
      • 2.4.1.4. Investor Presentations
      • 2.4.1.5. Regulatory Filings
      • 2.4.1.6. White Papers
      • 2.4.1.7. Industry Publications
      • 2.4.1.8. Conferences and Seminars
      • 2.4.1.9. Government Portals
      • 2.4.1.10. Media and Press Releases
      • 2.4.1.11. Newsletters
      • 2.4.1.12. Industry Databases
      • 2.4.1.13. Roots Proprietary Databases
      • 2.4.1.14. Paid Databases and Sources
      • 2.4.1.15. Social Media Portals
      • 2.4.1.16. Other Secondary Sources
    • 2.4.2. Primary Research
      • 2.4.2.1. Introduction
      • 2.4.2.2. Types
        • 2.4.2.2.1. Qualitative
        • 2.4.2.2.2. Quantitative
      • 2.4.2.3. Advantages
      • 2.4.2.4. Techniques
        • 2.4.2.4.1. Interviews
        • 2.4.2.4.2. Surveys
        • 2.4.2.4.3. Focus Groups
        • 2.4.2.4.4. Observational Research
        • 2.4.2.4.5. Social Media Interactions
      • 2.4.2.5. Stakeholders
        • 2.4.2.5.1. Company Executives (CXOs)
        • 2.4.2.5.2. Board of Directors
        • 2.4.2.5.3. Company Presidents and Vice Presidents
        • 2.4.2.5.4. Key Opinion Leaders
        • 2.4.2.5.5. Research and Development Heads
        • 2.4.2.5.6. Technical Experts
        • 2.4.2.5.7. Subject Matter Experts
        • 2.4.2.5.8. Scientists
        • 2.4.2.5.9. Doctors and Other Healthcare Providers
      • 2.4.2.6. Ethics and Integrity
        • 2.4.2.6.1. Research Ethics
        • 2.4.2.6.2. Data Integrity
    • 2.4.3. Analytical Tools and Databases

3. MARKET DYNAMICS

  • 3.1. Forecast Methodology
    • 3.1.1. Top-Down Approach
    • 3.1.2. Bottom-Up Approach
    • 3.1.3. Hybrid Approach
  • 3.2. Market Assessment Framework
    • 3.2.1. Total Addressable Market (TAM)
    • 3.2.2. Serviceable Addressable Market (SAM)
    • 3.2.3. Serviceable Obtainable Market (SOM)
    • 3.2.4. Currently Acquired Market (CAM)
  • 3.3. Forecasting Tools and Techniques
    • 3.3.1. Qualitative Forecasting
    • 3.3.2. Correlation
    • 3.3.3. Regression
    • 3.3.4. Time Series Analysis
    • 3.3.5. Extrapolation
    • 3.3.6. Convergence
    • 3.3.7. Forecast Error Analysis
    • 3.3.8. Data Visualization
    • 3.3.9. Scenario Planning
    • 3.3.10. Sensitivity Analysis
  • 3.4. Key Considerations
    • 3.4.1. Demographics
    • 3.4.2. Market Access
    • 3.4.3. Reimbursement Scenarios
    • 3.4.4. Industry Consolidation
  • 3.5. Robust Quality Control
  • 3.6. Key Market Segmentations
  • 3.7. Limitations

4. MACRO-ECONOMIC INDICATORS

  • 4.1. Chapter Overview
  • 4.2. Market Dynamics
    • 4.2.1. Time Period
      • 4.2.1.1. Historical Trends
      • 4.2.1.2. Current and Forecasted Estimates
    • 4.2.2. Currency Coverage
      • 4.2.2.1. Overview of Major Currencies Affecting the Market
      • 4.2.2.2. Impact of Currency Fluctuations on the Industry
    • 4.2.3. Foreign Exchange Impact
      • 4.2.3.1. Evaluation of Foreign Exchange Rates and Their Impact on Market
      • 4.2.3.2. Strategies for Mitigating Foreign Exchange Risk
    • 4.2.4. Recession
      • 4.2.4.1. Historical Analysis of Past Recessions and Lessons Learnt
      • 4.2.4.2. Assessment of Current Economic Conditions and Potential Impact on the Market
    • 4.2.5. Inflation
      • 4.2.5.1. Measurement and Analysis of Inflationary Pressures in the Economy
      • 4.2.5.2. Potential Impact of Inflation on the Market Evolution
    • 4.2.6. Interest Rates
      • 4.2.6.1. Overview of Interest Rates and Their Impact on the Market
      • 4.2.6.2. Strategies for Managing Interest Rate Risk
    • 4.2.7. Commodity Flow Analysis
      • 4.2.7.1. Type of Commodity
      • 4.2.7.2. Origins and Destinations
      • 4.2.7.3. Values and Weights
      • 4.2.7.4. Modes of Transportation
    • 4.2.8. Global Trade Dynamics
      • 4.2.8.1. Import Scenario
      • 4.2.8.2. Export Scenario
    • 4.2.9. War Impact Analysis
      • 4.2.9.1. Russian-Ukraine War
      • 4.2.9.2. Israel-Hamas War
    • 4.2.10. COVID Impact / Related Factors
      • 4.2.10.1. Global Economic Impact
      • 4.2.10.2. Industry-specific Impact
      • 4.2.10.3. Government Response and Stimulus Measures
      • 4.2.10.4. Future Outlook and Adaptation Strategies
    • 4.2.11. Other Indicators
      • 4.2.11.1. Fiscal Policy
      • 4.2.11.2. Consumer Spending
      • 4.2.11.3. Gross Domestic Product (GDP)
      • 4.2.11.4. Employment
      • 4.2.11.5. Taxes
      • 4.2.11.6. R&D Innovation
      • 4.2.11.7. Stock Market Performance
      • 4.2.11.8. Supply Chain
      • 4.2.11.9. Cross-Border Dynamics

SECTION II: QUALITATIVE INSIGHTS

5. EXECUTIVE SUMMARY

6. INTRODUCTION

  • 6.1. Chapter Overview
  • 6.2. Overview of AI in Healthcare Market
    • 6.2.1. Historical Evolution
    • 6.2.2. Key Applications
    • 6.2.3. Impact on Healthcare
  • 6.3. Future Perspective

7. REGULATORY SCENARIO

SECTION III: MARKET OVERVIEW

8. COMPREHENSIVE DATABASE OF LEADING PLAYERS

9. COMPETITIVE LANDSCAPE

  • 9.1. Chapter Overview
  • 9.2. AI in Healthcare Market: Overall Market Landscape
    • 9.2.1. Analysis by Year of Establishment
    • 9.2.2. Analysis by Company Size
    • 9.2.3. Analysis by Location of Headquarters
    • 9.2.4. Analysis by Ownership Structure

10. WHITE SPACE ANALYSIS

11. COMPANY COMPETITIVENESS ANALYSIS

12. STARTUP ECOSYSTEM IN THE AI IN HEALTHCARE MARKET

  • 12.1. Ai in healthcare Market: Market Landscape of Startups
    • 12.1.1. Analysis by Year of Establishment
    • 12.1.2. Analysis by Company Size
    • 12.1.3. Analysis by Company Size and Year of Establishment
    • 12.1.4. Analysis by Location of Headquarters
    • 12.1.5. Analysis by Company Size and Location of Headquarters
    • 12.1.6. Analysis by Ownership Structure
  • 12.2. Key Findings

SECTION IV: COMPANY PROFILES

13. COMPANY PROFILES

  • 13.1. Chapter Overview
  • 13.2. Google*
    • 13.2.1. Company Overview
    • 13.2.2. Company Mission
    • 13.2.3. Company Footprint
    • 13.2.4. Management Team
    • 13.2.5. Contact Details
    • 13.2.6. Financial Performance
    • 13.2.7. Operating Business Segments
    • 13.2.8. Technology / Platform Portfolio
    • 13.2.9. MOAT Analysis
    • 13.2.10. Recent Developments and Future Outlook
  • 13.3. GE Healthcare
  • 13.4. IBM
  • 13.5. Intel
  • 13.6. Itrex
  • 13.7. IQVIA
  • 13.8. Microsoft
  • 13.9. Medtronic
  • 13.10. Medidata
  • 13.11. Merck
  • 13.12. NVIDIA
  • 13.13. Oracle

SECTION V: MARKET TRENDS

14. MEGA TRENDS ANALYSIS

15. PATENT ANALYSIS

16. RECENT DEVELOPMENTS

  • 16.1. Chapter Overview
  • 16.2. Recent Funding
  • 16.3. Recent Partnerships
  • 16.4. Other Recent Initiatives

SECTION VI: MARKET OPPORTUNITY ANALYSIS

17. GLOBAL AI IN HEALTHCARE MARKET

  • 17.1. Chapter Overview
  • 17.2. Key Assumptions and Methodology
  • 17.3. Trends Disruption Impacting Market
  • 17.4. Demand Side Trends
  • 17.5. Supply Side Trends
  • 17.6. Global AI in Healthcare Market, Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 17.7. Multivariate Scenario Analysis
    • 17.7.1. Conservative Scenario
    • 17.7.2. Optimistic Scenario
  • 17.8. Investment Feasibility Index
  • 17.9. Key Market Segmentations

18. MARKET OPPORTUNITIES BASED ON TYPE OF PLATFORM

  • 18.1. Chapter Overview
  • 18.2. Key Assumptions and Methodology
  • 18.3. Revenue Shift Analysis
  • 18.4. Market Movement Analysis
  • 18.5. Penetration-Growth (P-G) Matrix
  • 18.6. AI in Healthcare Market for Solutions: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 18.7. AI in Healthcare Market for Services: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 18.8. Data Triangulation and Validation
    • 18.8.1. Secondary Sources
    • 18.8.2. Primary Sources
    • 18.8.3. Statistical Modeling

19. MARKET OPPORTUNITIES BASED ON TYPE OF COMPONENT

  • 19.1. Chapter Overview
  • 19.2. Key Assumptions and Methodology
  • 19.3. Revenue Shift Analysis
  • 19.4. Market Movement Analysis
  • 19.5. Penetration-Growth (P-G) Matrix
  • 19.6. AI in Healthcare Market for Hardware: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 19.7. AI in Healthcare Market for Software Solutions: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 19.8. AI in Healthcare Market for Services: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 19.9. AI in Healthcare Market for Others: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 19.10. Data Triangulation and Validation
    • 19.10.1. Secondary Sources
    • 19.10.2. Primary Sources
    • 19.10.3. Statistical Modeling

20. MARKET OPPORTUNITIES BASED ON TYPE OF APPLICATION

  • 20.1. Chapter Overview
  • 20.2. Key Assumptions and Methodology
  • 20.3. Revenue Shift Analysis
  • 20.4. Market Movement Analysis
  • 20.5. Penetration-Growth (P-G) Matrix
  • 20.6. AI in Healthcare Market for Robot-Assisted Surgery: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 20.7. AI in Healthcare Market for Virtual Assistants: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 20.9. AI in Healthcare Market for Administrative Workflow Assistants: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 20.10. AI in Healthcare Market for Connected Medical Devices: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 20.11. AI in Healthcare Market for Medical Imaging & Diagnostics: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 20.12. AI in Healthcare Market for Clinical Trials: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 20.13. AI in Healthcare Market for Fraud Detection: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 20.14. AI in Healthcare Market for Cybersecurity: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 20.15. AI in Healthcare Market for Dosage Error Reduction: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 20.16. AI in Healthcare Market for Precision Medicine: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 20.17. AI in Healthcare Market for Drug Discovery & Development: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 20.18. AI in Healthcare Market for Lifestyle Management & Remote Patient Monitoring Wearables: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 20.19. AI in Healthcare Market for Other Applications: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 20.20. Data Triangulation and Validation
    • 20.20.1. Secondary Sources
    • 20.20.2. Primary Sources
    • 20.20.3. Statistical Modeling

21. MARKET OPPORTUNITIES BASED ON TYPE OF TECHNOLOGY

  • 21.1. Chapter Overview
  • 21.2. Key Assumptions and Methodology
  • 21.3. Revenue Shift Analysis
  • 21.4. Market Movement Analysis
  • 21.5. Penetration-Growth (P-G) Matrix
  • 21.6. AI in Healthcare Market for Machine Learning: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 21.7. AI in Healthcare Market for Natural Language Processing: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 21.8. AI in Healthcare Market for Context-aware Computing: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 21.9. AI in Healthcare Market for Computer Vision: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 21.10. Data Triangulation and Validation
    • 21.10.1. Secondary Sources
    • 21.10.2. Primary Sources
    • 21.10.3. Statistical Modeling

22. MARKET OPPORTUNITIES BASED ON END USER

  • 22.1. Chapter Overview
  • 22.2. Key Assumptions and Methodology
  • 22.3. Revenue Shift Analysis
  • 22.4. Market Movement Analysis
  • 22.5. Penetration-Growth (P-G) Matrix
  • 22.6. AI in Healthcare Market for Healthcare Providers: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 22.7. AI in Healthcare Market for Healthcare Payers: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 22.8. AI in Healthcare Market for Healthcare Companies: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 22.9. AI in Healthcare Market for Patients: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 22.10. AI in Healthcare Market for Other End Users: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 22.11. Data Triangulation and Validation
    • 22.11.1. Secondary Sources
    • 22.11.2. Primary Sources
    • 22.11.3. Statistical Modeling

23. MARKET OPPORTUNITIES FOR AI IN HEALTHCARE MARKET IN NORTH AMERICA

  • 23.1. Chapter Overview
  • 23.2. Key Assumptions and Methodology
  • 23.3. Revenue Shift Analysis
  • 23.4. Market Movement Analysis
  • 23.5. Penetration-Growth (P-G) Matrix
  • 23.6. AI in Healthcare Market in North America: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 23.6.1. AI in Healthcare Market in the US: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 23.6.2. AI in Healthcare Market in Canada: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 23.6.3. AI in Healthcare Market in Mexico: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 23.6.4. AI in Healthcare Market in Other North American Countries: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 23.7. Data Triangulation and Validation

24. MARKET OPPORTUNITIES FOR AI IN HEALTHCARE MARKET IN EUROPE

  • 24.1. Chapter Overview
  • 24.2. Key Assumptions and Methodology
  • 24.3. Revenue Shift Analysis
  • 24.4. Market Movement Analysis
  • 24.5. Penetration-Growth (P-G) Matrix
  • 24.6. AI in Healthcare Market in Europe: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 24.6.1. AI in Healthcare Market in Austria: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 24.6.2. AI in Healthcare Market in Belgium: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 24.6.3. AI in Healthcare Market in Denmark: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 24.6.4. AI in Healthcare Market in France: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 24.6.5. AI in Healthcare Market in Germany: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 24.6.6. AI in Healthcare Market in Ireland: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 24.6.7. AI in Healthcare Market in Italy: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 24.6.8. AI in Healthcare Market in Netherlands: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 24.6.9. AI in Healthcare Market in Norway: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 24.6.10. AI in Healthcare Market in Russia: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 24.6.11. AI in Healthcare Market in Spain: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 24.6.12. AI in Healthcare Market in Sweden: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 24.6.13. AI in Healthcare Market in Switzerland: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 24.6.14. AI in Healthcare Market in the UK: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 24.6.15. AI in Healthcare Market in Other European Countries: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 24.7. Data Triangulation and Validation

25. MARKET OPPORTUNITIES FOR AI IN HEALTHCARE MARKET IN ASIA

  • 25.1. Chapter Overview
  • 25.2. Key Assumptions and Methodology
  • 25.3. Revenue Shift Analysis
  • 25.4. Market Movement Analysis
  • 25.5. Penetration-Growth (P-G) Matrix
  • 25.6. AI in Healthcare Market in Asia: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 25.6.1. AI in Healthcare Market in China: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 25.6.2. AI in Healthcare Market in India: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 25.6.3. AI in Healthcare Market in Japan: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 25.6.4. AI in Healthcare Market in Singapore: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 25.6.5. AI in Healthcare Market in South Korea: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 25.6.6. AI in Healthcare Market in Other Asian Countries: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 25.7. Data Triangulation and Validation

26. MARKET OPPORTUNITIES FOR AI IN HEALTHCARE MARKET IN MIDDLE EAST AND NORTH AFRICA (MENA)

  • 26.1. Chapter Overview
  • 26.2. Key Assumptions and Methodology
  • 26.3. Revenue Shift Analysis
  • 26.4. Market Movement Analysis
  • 26.5. Penetration-Growth (P-G) Matrix
  • 26.6. AI in Healthcare Market in Middle East and North Africa (MENA): Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 26.6.1. AI in Healthcare Market in Egypt: Historical Trends (Since 2020) and Forecasted Estimates (Till 205)
    • 26.6.2. AI in Healthcare Market in Iran: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 26.6.3. AI in Healthcare Market in Iraq: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 26.6.4. AI in Healthcare Market in Israel: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 26.6.5. AI in Healthcare Market in Kuwait: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 26.6.6. AI in Healthcare Market in Saudi Arabia: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 26.6.7. AI in Healthcare Market in United Arab Emirates (UAE): Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 26.6.8. AI in Healthcare Market in Other MENA Countries: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 26.7. Data Triangulation and Validation

27. MARKET OPPORTUNITIES FOR AI IN HEALTHCARE MARKET IN LATIN AMERICA

  • 27.1. Chapter Overview
  • 27.2. Key Assumptions and Methodology
  • 27.3. Revenue Shift Analysis
  • 27.4. Market Movement Analysis
  • 27.5. Penetration-Growth (P-G) Matrix
  • 27.6. AI in Healthcare Market in Latin America: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 27.6.1. AI in Healthcare Market in Argentina: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 27.6.2. AI in Healthcare Market in Brazil: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 27.6.3. AI in Healthcare Market in Chile: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 27.6.4. AI in Healthcare Market in Colombia Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 27.6.5. AI in Healthcare Market in Venezuela: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 27.6.6. AI in Healthcare Market in Other Latin American Countries: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 27.7. Data Triangulation and Validation

28. MARKET OPPORTUNITIES FOR AI IN HEALTHCARE MARKET IN REST OF THE WORLD

  • 28.1. Chapter Overview
  • 28.2. Key Assumptions and Methodology
  • 28.3. Revenue Shift Analysis
  • 28.4. Market Movement Analysis
  • 28.5. Penetration-Growth (P-G) Matrix
  • 28.6. AI in Healthcare Market in Rest of the World: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 28.6.1. AI in Healthcare Market in Australia: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 28.6.2. AI in Healthcare Market in New Zealand: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 28.6.3. AI in Healthcare Market in Other Countries
  • 28.7. Data Triangulation and Validation

29. MARKET CONCENTRATION ANALYSIS: DISTRIBUTION BY LEADING PLAYERS

  • 29.1. Leading Player 1
  • 29.2. Leading Player 2
  • 29.3. Leading Player 3
  • 29.4. Leading Player 4
  • 29.5. Leading Player 5
  • 29.6. Leading Player 6
  • 29.7. Leading Player 7
  • 29.8. Leading Player 8

30. ADJACENT MARKET ANALYSIS

SECTION VII: STRATEGIC TOOLS

31. KEY WINNING STRATEGIES

32. PORTER'S FIVE FORCES ANALYSIS

33. SWOT ANALYSIS

34. ROOTS STRATEGIC RECOMMENDATIONS

  • 34.1. Chapter Overview
  • 34.2. Key Business-related Strategies
    • 34.2.1. Research & Development
    • 34.2.2. Product Manufacturing
    • 34.2.3. Commercialization / Go-to-Market
    • 34.2.4. Sales and Marketing
  • 34.3. Key Operations-related Strategies
    • 34.3.1. Risk Management
    • 34.3.2. Workforce
    • 34.3.3. Finance
    • 34.3.4. Others

SECTION VIII: OTHER EXCLUSIVE INSIGHTS

35. INSIGHTS FROM PRIMARY RESEARCH

36. REPORT CONCLUSION

SECTION IX: APPENDIX

37. TABULATED DATA

38. LIST OF COMPANIES AND ORGANIZATIONS

39. ROOTS SUBSCRIPTION SERVICES

40. AUTHOR DETAILS