세계의 진단용 AI 시장(-2040년) : 컴포넌트 유형별, 진단 유형별, 최종사용자 유형별, 주요 지역별, 업계 동향, 예측

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

• 27.1. Leading Player 1
• 27.2. Leading Player 2
• 27.3. Leading Player 3
• 27.4. Leading Player 4
• 27.5. Leading Player 5
• 27.6. Leading Player 6
• 27.7. Leading Player 7
• 27.8. Leading Player 8

28. ADJACENT MARKET ANALYSIS

SECTION VII: STRATEGIC TOOLS

29. KEY WINNING STRATEGIES

30. PORTER'S FIVE FORCES ANALYSIS

31. SWOT ANALYSIS

32. VALUE CHAIN ANALYSIS

33. ROOTS STRATEGIC RECOMMENDATIONS

• 33.1. Chapter Overview
• 33.2. Key Business-related Strategies
  • 33.2.1. Research & Development
  • 33.2.2. Product Manufacturing
  • 33.2.3. Commercialization / Go-to-Market
  • 33.2.4. Sales and Marketing
• 33.3. Key Operations-related Strategies
  • 33.3.1. Risk Management
  • 33.3.2. Workforce
  • 33.3.3. Finance
  • 33.3.4. Others

SECTION VIII: OTHER EXCLUSIVE INSIGHTS

34. INSIGHTS FROM PRIMARY RESEARCH

35. REPORT CONCLUSION

SECTION IX: APPENDIX

36. TABULATED DATA

37. LIST OF COMPANIES AND ORGANIZATIONS

38. ROOTS SUBSCRIPTION SERVICES

39. AUTHOR DETAILS