세계의 진단용 AI 시장(-2040년) : 컴포넌트 유형별, 진단 유형별, 최종사용자 유형별, 주요 지역별, 업계 동향, 예측
Artificial Intelligence in Diagnostics Market, till 2040: Distribution by Type of Component, Type of Diagnosis, Type of End User, and Key Geographical Regions: Industry Trends and Global Forecasts
상품코드:1895187
리서치사:Roots Analysis
발행일:On Demand Report
페이지 정보:영문 134 Pages
라이선스 & 가격 (부가세 별도)
한글목차
진단용 AI 시장 전망
세계의 진단용 AI 시장 규모는 현재 23억 9,000만 달러에서 2040년까지 79억 1,000만 달러에 달할 것으로 추정되며, 2040년까지의 예측 기간에 CAGR로 8.91%의 성장이 전망됩니다.
진단용 AI는 머신러닝을 활용하여 방대한 환자 정보(영상, 기록, 검사 결과 등)를 분석하여 보다 신속하고 정확한 질병 식별, 패턴 인식, 위험 예측을 가능하게 합니다. 이는 의료진에게 대체 수단이 아닌 효율성과 정확성 향상, 개인 맞춤형 치료 실현을 통해 강력한 의사결정 지원 자원으로 작용할 수 있습니다. 특히 엑스레이, MRI 등 의료 영상에서의 적용이 두드러지며, 미세한 바이오마커를 식별하고 잠재적인 건강 상태를 조기에 예측할 수 있도록 돕습니다.
암, 심혈관 질환 등 조기 발견이 필요한 만성질환 증가, 의료 인력 부족, 전자건강기록(EHR) 및 영상 시스템에서 발생하는 의료 데이터의 급격한 증가 등 여러 요인으로 인해 진단용 AI 세계 시장은 견고한 성장세를 보이고 있습니다. 또한 딥러닝과 데이터 분석 기술의 지속적인 발전으로 보다 신속하고 정밀한 진단 솔루션이 실현되고 있습니다. 이러한 모멘텀은 의료의 효율성과 비용 효율성을 높이기 위한 정부와 민간 부문의 투자 확대에 의해 더욱 강화되고 있습니다.
경영진을 위한 전략적 인사이트
의료 진단에서 AI의 역할
AI는 진단 검사의 정확성과 효율성을 향상시킴으로써 의료 진단의 판도를 크게 바꾸고 있습니다. AI 알고리즘은 의료영상, 전자건강기록(EHR), 유전체 정보 등 방대하고 복잡한 데이터세트를 기존 기술보다 더 빠르고 정확하게 분석할 수 있는 능력을 가지고 있습니다. 이러한 접근 방식은 인적 오류를 줄이고 질병을 조기에 발견할 수 있도록 도와줍니다.
머신러닝과 딥러닝 기술을 활용하여 AI 시스템은 임상의가 간과하기 쉬운 의료 데이터의 미묘한 경향을 감지하여 진단 정확도를 높이고 적시에 개입할 수 있도록 돕습니다. 또한 AI는 진단 절차를 간소화하여 의료진이 환자 치료에 더 집중할 수 있도록 하는 한편, 근거에 기반한 제안과 예측 분석을 통해 임상적 의사결정 지원을 제공합니다. 또한 AI는 환자 개개인의 특성에 맞는 맞춤형 치료 전략을 통해 맞춤형 의료를 촉진하고, 원격의료 플랫폼에 통합되어 특히 의료 자원이 제한된 지역에서 양질의 진단에 대한 접근성을 확대할 수 있습니다.
AI 의료 진단의 급격한 성장을 지원하는 요인은?
의료 진단용 AI 시장의 성장은 여러 가지 상호 연관된 요인에 의해 촉진되고 있습니다. 암, 당뇨병, 심혈관 질환과 같은 만성질환의 유병률 증가로 인해 보다 빠르고 정확한 진단 솔루션에 대한 수요가 증가하고 있습니다. 딥러닝, 머신러닝, 자연 언어 처리의 발전으로 의료 영상, 전자건강기록(EHR), 유전체 분석, 웨어러블 기술에서 얻은 복잡한 데이터세트를 정밀하게 해석할 수 있게 되었습니다. 또한 R&D 투자 증가, 디지털 헬스 및 정밀의료를 추진하는 정부의 구상, NVIDIA, Siemens Healthineers, Aidoc, Google과 같은 업계 리더 간의 전략적 제휴가 혁신과 시장 확대를 가속화하고 있습니다.
이 업계의 기업 경쟁 구도
의료영상용 AI 시장 경쟁 구도는 대기업과 중소기업이 모두 참여하는 치열한 경쟁이 특징입니다. Microsoft, NVIDIA, IBM, Intel 등 주요 기술 기업은 병원 및 소프트웨어 기업과의 협력을 통해 수많은 다운스트림 진단 솔루션을 지원하는 클라우드, GPU, 모델 개발 인프라를 제공합니다. 이 분야에는 희귀질환 감지, 디지털 병리학 자동화, 아시아, 중동, 라틴아메리카 등 지역의 저자원 방사선 네트워크 등 특정 분야에 특화된 다양한 틈새 스타트업과 로컬 기업도 존재합니다. 또한 지속적인 인수합병, 전략적 제휴, 대규모 벤처캐피털 자금 조달로 인해 경쟁이 심화되고 있으며, 업체 간 통합이 진행되고 있습니다.
세계의 진단용 AI 시장에 대해 조사했으며, 시장 규모 추산과 기회의 분석, 경쟁 구도, 기업 개요 등의 정보를 제공하고 있습니다.
목차
섹션 1 : 리포트 개요
제1장 서문
제2장 조사 방법
제3장 시장 역학
제4장 거시경제 지표
섹션 2 : 정성적 인사이트
제5장 개요
제6장 서론
제7장 규제 시나리오
섹션 3 : 시장 개요
제8장 주요 기업의 종합적 데이터베이스
제9장 경쟁 구도
제10장 화이트 스페이스 분석
제11장 기업 경쟁력 분석
제12장 진단용 AI 시장의 스타트업 에코시스템
섹션 4 : 기업 개요
제13장 기업 개요
챕터 개요
Aidoc
AliveCor
Digital Diagnostics
GE Healthcare
HeartFlow
Imagen Technologies
Merative
NovaSignal
PathAI
Riverain Technologies
Roche
Siemens Healthineers
Vuno
Zebra Medical Vision
섹션 5 : 시장 동향
제14장 메가트렌드 분석
제15장 특허 분석
제16장 최근 발전
섹션 6 : 시장 기회 분석
제17장 세계의 진단용 AI 시장
제18장 시장 기회 : 컴포넌트 유형별
제19장 시장 기회 : 진단 유형별
제20장 시장 기회 : 최종사용자 유형별
제21장 북미의 진단용 AI 시장의 시장 기회
제22장 유럽의 진단용 AI 시장의 시장 기회
제23장 아시아의 진단용 AI 시장의 시장 기회
제24장 중동·북아프리카(MENA)의 진단용 AI 시장의 시장 기회
제25장 라틴아메리카의 진단용 AI 시장의 시장 기회
제26장 기타 지역의 진단용 AI 시장의 시장 기회
제27장 시장 집중 분석 : 주요 기업별
제28장 인접 시장 분석
섹션 7 : 전략적 툴
제29장 주요 성공 전략
제30장 Porter's Five Forces 분석
제31장 SWOT 분석
제32장 밸류체인 분석
제33장 Roots의 전략적 제안
섹션 8 : 기타 독점적 인사이트
제34장 1차 조사로부터의 인사이트
제35장 리포트 결론
섹션 9 : 부록
KSA
영문 목차
영문목차
Artificial Intelligence In Diagnostics Market Outlook
As per Roots Analysis, the global artificial intelligence in diagnostics market size is estimated to grow from USD 2.39 billion in the current year to USD 7.91 billion by 2040, at a CAGR of 8.91% during the forecast period, till 2040. The new study provides market size, growth scenarios, industry trend and future forecast.
AI in diagnostics leverages machine learning to analyze extensive patient information (such as images, records, and lab results) to facilitate quicker and more precise disease identification, recognize patterns, and foresee risks. This serves as a robust decision-support resource for healthcare providers rather than a substitute, by improving efficiency, accuracy, and tailored care. Its applications are particularly notable in medical imaging, such as X-rays and MRIs, where it assists in identifying subtle biomarkers and forecasting potential health conditions well in advance.
The global market for AI in diagnostics is witnessing robust growth, driven by a combination of factors including the rising incidence of chronic diseases such as cancer and cardiovascular disorders that demand early detection, shortage of healthcare professionals (at global level), and the exponential increase in healthcare data from electronic health records and imaging systems. Furthermore, continuous advancements in deep learning and data analytics technologies are enabling faster and more precise diagnostic solutions. This momentum is reinforced by growing government and private sector investments aimed at improving healthcare efficiency and cost-effectiveness.
Strategic Insights for Senior Leaders
Role of AI in Medical Diagnostics
Artificial intelligence (AI) is significantly changing the landscape of medical diagnostics by improving the accuracy and efficiency of diagnostic tests. AI algorithms have the capability to swiftly and precisely analyze extensive and intricate datasets, such as medical images, electronic health records, and genomic information, more effectively than conventional techniques. This approach diminishes human error and allows for the earlier identification of diseases.
By utilizing machine learning and deep learning techniques, AI systems can detect subtle trends in medical data that clinicians might overlook, enhancing diagnostic precision and aiding timely interventions. AI also simplifies diagnostic procedures, allowing healthcare professionals to concentrate more on patient care, while concurrently providing clinical decision support through evidence-based suggestions and predictive analytics. In addition, AI promotes personalized medicine by customizing treatment strategies to match individual patient characteristics, and its incorporation into telemedicine platforms broadens access to quality diagnostics, especially in areas with limited medical resources.
What's Powering the Surge in AI Medical Diagnostics?
The growth of the AI in medical diagnostics market is driven by several interrelated factors, including the rising prevalence of chronic diseases such as cancer, diabetes, and cardiovascular disorders, which amplify the demand for faster and more accurate diagnostic solutions. Advancements in deep learning, machine learning, and natural language processing enable precise interpretation of complex datasets from medical imaging, electronic health records, genomics, and wearable technologies. Moreover, increasing R&D investments, government initiatives promoting digital health and precision medicine, and strategic collaborations among industry leaders, such as NVIDIA, Siemens Healthineers, Aidoc, and Google, are accelerating innovation and market expansion.
Competitive Landscape of Companies in this Industry
The competitive landscape of AI in medical imaging market is characterized by intense competition, featuring a combination of large and smaller firms. Prominent technology firms such as Microsoft, NVIDIA, IBM, and Intel supply essential cloud, GPU, and model-development infrastructure that supports numerous downstream diagnostic solutions, by collaborating with hospitals and software companies. This domain also includes a variety of niche startups and local players focusing on specific areas like rare disease detection, digital pathology automation, and low-resource radiology networks in regions such as Asia, the Middle East, and Latin America. Further, the competitive environment is intensified by ongoing mergers and acquisitions, strategic partnerships, and substantial rounds of venture capital funding, resulting in consolidation among vendors.
Emerging Trends in the Artificial Intelligence in Diagnostics Industry
Emerging trends in this domain include federated learning, which enables model training across different institutions while preserving privacy, the development of explainable AI to enhance clinician trust. Further, the stakeholders are focused on the integration of AI in wearable devices that allow for real-time remote monitoring, facilitating proactive interventions through the analysis of various data types, such as ECGs, genomics, and electronic health records. Additionally, in the fields of pathology and genomics, AI improves workflows by automating tissue assessments and detecting rare genetic mutations, while point-of-care devices equipped with AI offer quick bedside diagnostics, helping to alleviate workforce shortages and increase accessibility in underserved regions.
Key Market Challenges
The field of artificial intelligence in diagnostics encounters numerous challenges, such as concerns over data privacy, ethical and regulatory issues, algorithmic biases, a lack of explainability, and obstacles to integration within clinical workflows. Researchers highlight uncertainties regarding legal liability for decisions made by AI, and the necessity for strong data protection in fragmented healthcare systems. Technical challenges include the lack of high-quality, standardized datasets, limitations in hardware like processing capabilities and interoperability. These factors undermine clinician trust despite their potential for high accuracy. Additionally, workflow obstacles, such as resistance to change, insufficient incentives for adoption, further complicates the adoption. To tackle these issues, interdisciplinary cooperation, governance structures, and standardization are essential to strike a balance between innovation and safety.
Artificial Intelligence In Diagnostics Market: Key Market Segmentation
Type of Component
Software
Hardware
Services
Type of Diagnosis
Neurology
Radiology
Oncology
Cardiology
Pathology
Others
Type of End User
Hospitals
Diagnostic Laboratories
Others
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 Artificial Intelligence in Diagnostics Market
Aidoc
AliveCor
Digital Diagnostics
GE Healthcare
HeartFlow
Imagen Technologies
Koninklijke Philips
Merative
NovaSignal
PathAI
Riverain Technologies
Roche
Siemens Healthineers
Vuno
Zebra Medical Vision
Artificial Intelligence In Diagnostics Market: Report Coverage
The report on the artificial intelligence in diagnostics market features insights on various sections, including:
Market Sizing and Opportunity Analysis: An in-depth analysis of the artificial intelligence in diagnostics market, focusing on key market segments, including [A] type of component, [B] type of diagnosis, [C] type of end user, [D] and key geographical regions
Competitive Landscape: A comprehensive analysis of the companies engaged in the artificial intelligence in diagnostics 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 Artificial intelligence in diagnostics 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] portfolio, [J] recent developments, and an informed future outlook.
Megatrends: An evaluation of ongoing megatrends in the artificial intelligence in diagnostics industry.
Patent Analysis: An insightful analysis of patents filed / granted in the artificial intelligence in diagnostics 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 Artificial intelligence in diagnostics 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 Artificial intelligence in diagnostics 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.
Value Chain Analysis: A comprehensive analysis of the value chain, providing information on the different phases and stakeholders involved in the artificial intelligence in diagnostics market.
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 Artificial Intelligence in Diagnostics Market
6.2.1. Historical Evolution
6.2.2. Core AI Technologies
6.2.3. Application Areas
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. Artificial Intelligence in Diagnostics 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 ARTIFICIAL INTELLIGENCE IN DIAGNOSTICS MARKET
12.1. Artificial Intelligence in Diagnostics 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. Aidoc*
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. Service / Product Portfolio (project specific)
13.2.9. MOAT Analysis
13.2.10. Recent Developments and Future Outlook
13.3. AliveCor
13.4. Digital Diagnostics
13.5. GE Healthcare
13.6. HeartFlow
13.7. Imagen Technologies
13.8. Merative
13.9. NovaSignal
13.10. PathAI
13.11. Riverain Technologies
13.12. Roche
13.13. Siemens Healthineers
13.14. Vuno
13.15. Zebra Medical Vision
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 ARTIFICIAL INTELLIGENCE IN DIAGNOSTICS 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 Artificial Intelligence in Diagnostics 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 COMPONENT
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. Artificial Intelligence in Diagnostics Market for Software: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
18.7. Artificial Intelligence in Diagnostics Market for Hardware: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
18.8. Artificial Intelligence in Diagnostics Market for Services: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
18.9. Data Triangulation and Validation
18.9.1. Secondary Sources
18.9.2. Primary Sources
18.9.3. Statistical Modeling
19. MARKET OPPORTUNITIES BASED ON TYPE OF DIAGNOSIS
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. Artificial Intelligence in Diagnostics Market for Neurology: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
19.7. Artificial Intelligence in Diagnostics Market for Radiology: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
19.8. Artificial Intelligence in Diagnostics Market for Oncology: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
19.9. Artificial Intelligence in Diagnostics Market for Cardiology: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
19.10. Artificial Intelligence in Diagnostics Market for Pathology: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
19.11. Artificial Intelligence in Diagnostics Market for Others: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
19.12. Data Triangulation and Validation
19.12.1. Secondary Sources
19.12.2. Primary Sources
19.12.3. Statistical Modeling
20. MARKET OPPORTUNITIES BASED ON TYPE OF END USER
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. Artificial Intelligence in Diagnostics Market for Hospitals: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
20.7. Artificial Intelligence in Diagnostics Market for Diagnostic Laboratories: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
20.8. Artificial Intelligence in Diagnostics Market for Others: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
20.9. Data Triangulation and Validation
20.9.1. Secondary Sources
20.9.2. Primary Sources
20.9.3. Statistical Modeling
21. MARKET OPPORTUNITIES FOR ARTIFICIAL INTELLIGENCE IN DIAGNOSTICS MARKET IN NORTH AMERICA
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. Artificial Intelligence in Diagnostics Market in North America: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
21.6.1. Artificial Intelligence in Diagnostics Market in the US: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
21.6.2. Artificial Intelligence in Diagnostics Market in Canada: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
21.6.3. Artificial Intelligence in Diagnostics Market in Mexico: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
21.6.4. Artificial Intelligence in Diagnostics Market in Other North American Countries: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
21.7. Data Triangulation and Validation
22. MARKET OPPORTUNITIES FOR ARTIFICIAL INTELLIGENCE IN DIAGNOSTICS MARKET IN EUROPE
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. Artificial Intelligence in Diagnostics Market in Europe: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
22.6.1. Artificial Intelligence in Diagnostics Market in Austria: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
22.6.2. Artificial Intelligence in Diagnostics Market in Belgium: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
22.6.3. Artificial Intelligence in Diagnostics Market in Denmark: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
22.6.4. Artificial Intelligence in Diagnostics Market in France: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
22.6.5. Artificial Intelligence in Diagnostics Market in Germany: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
22.6.6. Artificial Intelligence in Diagnostics Market in Ireland: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
22.6.7. Artificial Intelligence in Diagnostics Market in Italy: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
22.6.8. Artificial Intelligence in Diagnostics Market in Netherlands: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
22.6.9. Artificial Intelligence in Diagnostics Market in Norway: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
22.6.10. Artificial Intelligence in Diagnostics Market in Russia: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
22.6.11. Artificial Intelligence in Diagnostics Market in Spain: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
22.6.12. Artificial Intelligence in Diagnostics Market in Sweden: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
22.6.13. Artificial Intelligence in Diagnostics Market in Switzerland: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
22.6.14. Artificial Intelligence in Diagnostics Market in the UK: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
22.6.15. Artificial Intelligence in Diagnostics Market in Other European Countries: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
22.7. Data Triangulation and Validation
23. MARKET OPPORTUNITIES FOR ARTIFICIAL INTELLIGENCE IN DIAGNOSTICS MARKET IN ASIA
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. Artificial Intelligence in Diagnostics Market in Asia: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
23.6.1. Artificial Intelligence in Diagnostics Market in China: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
23.6.2. Artificial Intelligence in Diagnostics Market in India: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
23.6.3. Artificial Intelligence in Diagnostics Market in Japan: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
23.6.4. Artificial Intelligence in Diagnostics Market in Singapore: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
23.6.5. Artificial Intelligence in Diagnostics Market in South Korea: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
23.6.6. Artificial Intelligence in Diagnostics Market in Other Asian Countries: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
23.7. Data Triangulation and Validation
24. MARKET OPPORTUNITIES FOR ARTIFICIAL INTELLIGENCE IN DIAGNOSTICS MARKET IN MIDDLE EAST AND NORTH AFRICA (MENA)
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. Artificial Intelligence in Diagnostics Market in Middle East and North Africa (MENA): Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
24.6.1. Artificial Intelligence in Diagnostics Market in Egypt: Historical Trends (Since 2020) and Forecasted Estimates (Till 205)
24.6.2. Artificial Intelligence in Diagnostics Market in Iran: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
24.6.3. Artificial Intelligence in Diagnostics Market in Iraq: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
24.6.4. Artificial Intelligence in Diagnostics Market in Israel: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
24.6.5. Artificial Intelligence in Diagnostics Market in Kuwait: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
24.6.6. Artificial Intelligence in Diagnostics Market in Saudi Arabia: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
24.6.7. Artificial Intelligence in Diagnostics Market in United Arab Emirates (UAE): Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
24.6.8. Artificial Intelligence in Diagnostics Market in Other MENA Countries: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
24.7. Data Triangulation and Validation
25. MARKET OPPORTUNITIES FOR ARTIFICIAL INTELLIGENCE IN DIAGNOSTICS MARKET IN LATIN AMERICA
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. Artificial Intelligence in Diagnostics Market in Latin America: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
25.6.1. Artificial Intelligence in Diagnostics Market in Argentina: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
25.6.2. Artificial Intelligence in Diagnostics Market in Brazil: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
25.6.3. Artificial Intelligence in Diagnostics Market in Chile: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
25.6.4. Artificial Intelligence in Diagnostics Market in Colombia Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
25.6.5. Artificial Intelligence in Diagnostics Market in Venezuela: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
25.6.6. Artificial Intelligence in Diagnostics Market in Other Latin American Countries: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
25.7. Data Triangulation and Validation
26. MARKET OPPORTUNITIES FOR ARTIFICIAL INTELLIGENCE IN DIAGNOSTICS MARKET IN REST OF THE WORLD
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. Artificial Intelligence in Diagnostics Market in Rest of the World: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
26.6.1. Artificial Intelligence in Diagnostics Market in Australia: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
26.6.2. Artificial Intelligence in Diagnostics Market in New Zealand: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
26.6.3. Artificial Intelligence in Diagnostics Market in Other Countries
26.7. Data Triangulation and Validation
27. MARKET CONCENTRATION ANALYSIS: DISTRIBUTION BY LEADING PLAYERS
