의료용 AI 시장(-2040년) : 플랫폼 유형별, 컴포넌트 유형별, 용도 유형별, 기술 유형별, 최종 사용자별, 주요 지역별, 주요 기업별 산업 동향 및 예측
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
상품코드:1919789
리서치사:Roots Analysis
발행일:On Demand Report
페이지 정보:영문 198 Pages
라이선스 & 가격 (부가세 별도)
한글목차
의료용 AI 시장 전망
세계의 의료용 AI 시장 규모는 현재 485억 달러에서 2040년까지 1조 9,234억 달러에 이를 것으로 추정되며, 2040년까지 예측 기간 중 CAGR 30%의 성장이 예상됩니다.
AI는 의료 현장 전반에 걸친 질병의 검출, 치료, 관리 방법의 개량을 통해 의료를 급속히 변화시키고 있습니다. 이를 통해 임상의는 보다 효율적으로 업무를 수행하는 동시에 환자에게 보다 안전하고 개인화된 치료를 제공할 수 있습니다. AI 시스템은 영상, 검사 값, 전자 건강 기록과 같은 방대한 의료 데이터를 분석하여 질병의 조기 징후를 나타낼 수 있는 미묘한 패턴을 식별할 수 있습니다. 방사선과, 병리학, 심장병학, 종양학에서 알고리즘은 진단 정확도를 높이고 인적 오류의 위험을 줄임으로써 임상의를 지원합니다. 진단을 넘어 인공지능은 개인화된 치료 계획을 수립하고 질병 진행을 예측하고 약물 선택을 최적화하고 정밀의료로의 광범위한 전환을 지원합니다.
운영 측면에서 AI는 임상 문서 작성 자동화, 가상 어시스턴트, 병원 정보 시스템에 내장된 의사결정 지원 도구를 통해 워크플로우를 효율화합니다. 이러한 용도는 사무 부하를 줄여 의료 종사자가 직접적인 환자 대응과 복잡한 의사 결정에 집중할 수 있도록 합니다. 동시에 AI 대응 웨어러블을 통한 원격 환자 모니터링은 생체 신호의 지속적인 추적과 합병증의 조기 발견을 지원하여 의료를 대응형에서 예방형으로 전환시킵니다. 위의 요인을 고려하면 의료용 AI 시장은 예측 기간 중 상당한 성장이 예상됩니다.
고위 임원에 대한 전략적 지식
원격 모니터링 및 맞춤형 의료에서 AI의 변혁적 역할
AI는 지속적인 데이터 중심의 케어를 제공함으로써 원격 환자 모니터링(RPM)과 맞춤형 의료를 강화합니다. 웨어러블 디바이스와 가정용 센서는 심박수, 혈압, 산소 포화도, 혈당 등과 같은 생체 신호를 실시간으로 추적합니다. AI 알고리즘은 부정맥 및 산소 포화도 저하와 같은 미세한 이상을 감지하고 의료 종사자에게 적절한 시기에 경고를 발신하여 조기 개입을 촉진합니다. 이것은 외래 진료에서 당뇨병, 심혈관 질환, 만성 폐색성 폐 질환(COPD)과 같은 만성 질환의 관리를 최적화합니다.
맞춤형 의료에서는 AI가 개인의 유전체 프로파일, 생활 습관 요인, 과거의 건강 데이터를 통합하여 개별적으로 최적화된 치료 전략의 책정, 치료 효과의 예측, 투여량 및 치료 계획의 동적 조정을 실현합니다. 이러한 기능의 종합적인 활용은 의료비 절감, 의료 액세스 확대, 재택치료 모델의 촉진을 도모합니다.
의료용 AI 시장의 주요 성장 촉진요인
의료용 AI의 성장은 당뇨병이나 심혈관 질환 등의 만성 질환 증가, 세계적인 고령화 등 여러 요인에 의해 촉진되고 있습니다. 이로 인해 고급 진단 및 관리 솔루션에 대한 수요가 커지고 있습니다. 전자 건강 기록, 웨어러블 및 진단 검사에서 얻은 방대한 건강 데이터 세트의 보급으로 AI는 예측 분석, 진단 및 개인화 치료를 향상시킬 수 있습니다. 머신러닝 알고리즘의 발전, 비용 효율적인 컴퓨팅 인프라 및 확장 가능한 클라우드 기술이 AI의 보급을 촉진하고 있습니다. 임상의의 인력 부족은 일상 업무의 자동화, 비용 절감, 창약 프로세스의 가속을 촉진하고 있습니다. 많은 벤처 캐피탈과 기업 투자가 이 시장의 혁신과 성장을 더욱 가속화하고 있습니다.
의료용 AI의 진화 : 업계의 새로운 동향
의료용 AI의 새로운 추세로는 실시간 건강 모니터링을 가능하게 하고 부정맥과 같은 이상을 임상의에 미리 경고하는 고급 웨어러블 장치가 포함됩니다. 생성형 AI는 임상 문서 작성의 자동화, 안전한 모델 훈련을 위한 합성 환자 데이터의 생성, 신약 개발 프로세스의 가속화를 촉진합니다. 또한 자율 AI 에이전트는 가상 어시스턴트 역할을 하며 예약 스케줄링과 치료 후 후속 조치를 간소화합니다. 진단 강화는 영상에서 악성 종양과 뇌혈관 장애의 신속한 검출에 AI를 활용하고 있으며, 유전체 정보와 생활 습관 프로파일을 바탕으로 치료법을 맞춤화하는 정밀의료가 보완합니다. 예측 분석은 조기 경고를 통해 패혈증 등의 위험을 줄이고 확장된 원격 의료는 AI 채팅봇과 가상 간호를 통합하여 효율성, 비용 효율성, 세계 접근성을 최적화합니다. 이러한 진보로 AI는 차세대 의료의 기초가 되고 있으며, 환자의 치료 성과, 업무 효율, 세계의 공정한 접근에 혁신적인 개선을 추진하고 있습니다.
이 보고서는 세계의 의료용 AI 시장에 대해 조사했으며, 시장 규모의 추계와 기회 분석, 경쟁 구도, 기업 프로파일 등의 정보를 제공합니다.
목차
섹션 1 보고서 개요
제1장 서문
제2장 조사 방법
제3장 시장 역학
제4장 거시경제 지표
섹션 2 질적 지식
제5장 주요 요약
제6장 소개
제7장 규제 시나리오
섹션 3 시장 개요
제8장 주요 기업의 종합적인 데이터베이스
제9장 경쟁 구도
제10장 화이트 스페이스 분석
제11장 기업의 경쟁력 분석
제12장 의료용 AI 시장에서의 스타트업 에코시스템
섹션 4 기업 프로파일
제13장 기업 프로파일
장 개요
Google
GE Healthcare
IBM
Intel
Itrex
IQVIA
Microsoft
Medtronic
Medidata
Merck
NVIDIA
Oracle
섹션 5 시장 동향
제14장 메가트렌드 분석
제15장 특허 분석
제16장 최근의 발전
섹션 6 시장 기회 분석
제17장 세계의 의료용 AI 시장
제18장 시장 기회 : 플랫폼 유형별
제19장 시장 기회 : 컴포넌트 유형별
제20장 시장 기회 : 용도 유형별
제21장 시장 기회 : 기술 유형별
제22장 시장 기회 : 최종 사용자별
제23장 북미의 의료용 AI 시장 기회
제24장 유럽의 의료용 AI 시장 기회
제25장 아시아의 의료용 AI 시장 기회
제26장 중동 및 북아프리카(MENA)의 의료용 AI 시장 기회
제27장 라틴아메리카의 의료용 AI 시장 기회
제28장 기타 지역의 의료용 AI 시장 기회
제29장 시장 집중 분석 : 주요 기업별
제30장 인접 시장 분석
섹션 7 전략적 도구
제31장 중요한 성공 전략
제32장 Porter's Five Forces 분석
제33장 SWOT 분석
제34장 Roots의 전략적 제안
섹션 8 기타 독점적 발견
제35장 1차 조사의 인사이트
제36장 보고서 결론
섹션 9 부록
JHS
영문 목차
영문목차
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.
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.
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
