유전체학용 AI 시장(-2040년) : 컴포넌트 유형별, 기술 유형별, 기능성 유형별, 용도 유형별, 최종 사용자 유형별, 기업 규모별, 주요 지역별, 산업 동향 및 예측

Ai in Genomics Market Outlook

As per Roots Analysis, the global AI in genomics market size is estimated to grow from USD 1.97 billion in the current year to USD 317.4 billion by 2040, at a CAGR of 43.75% during the forecast period, till 2040. The new study provides market size, growth scenarios, industry trend and future forecast.

Artificial intelligence is transforming the field of genomics by processing huge datasets from DNA sequencing to reveal insights that conventional techniques overlook. Advanced technologies like next-generation sequencing produce extensive genetic data. AI applications such as machine learning and deep learning are proficient at forecasting disease risks, determining protein structures, analyzing gene expressions, and synthesizing multi-omics information for personalized medicine. This fosters quicker drug discovery, accurate genome editing through CRISPR, and customized treatments tailored to individual genetic blueprints.

The market for AI in genomics is expected to grow significantly due to the massive increase in genomic data from next-generation sequencing technologies, which outpaces traditional analysis methods and requires AI's pattern recognition capabilities.

Strategic Insights for Senior Leaders

Transformative Role of Artificial Intelligence in Drug Discovery and Genomic Research

Artificial Intelligence (AI) is playing a transformative role in revolutionizing drug discovery and genomic research by enhancing efficiency, accuracy, and decision-making. Traditionally, drug development has been a lengthy and costly process, often taking several years and substantial investment; however, AI-driven tools now enable rapid analysis of vast biomedical datasets. In drug discovery, AI algorithms can predict molecular interactions, optimize lead compounds, and identify potential drug candidates with higher precision.

Within genomic research, AI facilitates the interpretation of complex genomic data, enabling the identification of disease-associated genes and the understanding of genetic variations influencing drug response. These advancements are accelerating the development of targeted and personalized therapies. Furthermore, AI applications support the design of more efficient clinical trials by improving patient selection and predicting therapeutic outcomes. Overall, the integration of AI into drug discovery and genomics is reshaping the healthcare landscape, expediting innovation, and advancing the global shift toward precision medicine.

Key Drivers Propelling Growth of AI in genomics Market

The growth of artificial intelligence (AI) in genomics market is being driven by several key factors enhancing the efficiency and accuracy of genomic research and drug discovery. The rapid increase in genomic data generated by next-generation sequencing technologies has created a strong demand for AI-based tools capable of managing and analyzing complex datasets. Machine learning algorithms are enabling faster identification of genetic patterns, disease prediction, and drug target discovery, thereby significantly reducing both cost and time in research and development.

The expanding focus on personalized medicine is another major driver, as AI supports the interpretation of individual genetic profiles to develop targeted and more effective treatment strategies. Additionally, decreasing computational costs, coupled with advancements in data processing infrastructure, have made AI technologies more accessible. Substantial investments from major technology companies and growing collaborations between pharmaceutical, biotechnology, and AI firms are further accelerating innovation in this field.

Emerging Applications of Artificial Intelligence in Precision Medicine

Artificial Intelligence (AI) is driving significant advancements in precision medicine by enabling data-driven personalization of healthcare. Precision medicine aims to tailor diagnosis and treatment strategies based on an individual's genetic profile, lifestyle, and environmental factors. AI technologies facilitate this approach by efficiently processing and interpreting large-scale data from genomic sequencing, electronic health records, medical imaging, and wearable devices. Through machine learning algorithms, AI can uncover complex patterns and correlations that inform early disease detection, predict therapeutic responses, and assist in developing targeted treatment plans. In oncology, for instance, AI models are being utilized to predict tumor behavior, optimize drug selection, and design personalized interventions. Furthermore, AI accelerates genomic data analysis by improving variant interpretation and identifying clinically relevant biomarkers. It also enhances diagnostic accuracy through image-based analytics in radiology and pathology. Collectively, these applications position AI as a key enabler of precision medicine, improving clinical decision-making, reducing trial-and-error treatments, and ultimately enhancing patient outcomes.

AI in genomics Evolution: Emerging Trends in the Industry

Artificial intelligence is revolutionizing genomics by making it faster and more accurate to analyze large genetic datasets. One major trend is multi-omics integration, where AI combines data from genomics, proteomics, and other biological sources to better understand how genes influence diseases and to identify new drug targets. Generative AI models, such as those that predict protein structures or create synthetic gene sequences, help scientists design new therapies and speed up drug discovery. Another growing area is AI-powered CRISPR, where advanced algorithms like CRISPR-GPT make gene editing safer and more precise by predicting and avoiding unwanted effects. Overall, AI is enabling more personalized treatments, improving disease prediction, and transforming how genetic research is done.

Key Market Challenges

The AI in genomics market faces several critical challenges that hinder its full-scale adoption. These include data quality and standardization issues, as genomic datasets often originate from heterogeneous sources, leading to inconsistencies and biases in model performance. Data privacy and compliance with stringent regulations, such as GDPR and HIPAA remain significant concerns due to the sensitive nature of genetic information. Additionally, the high computational costs, limited availability of skilled AI professionals, and the lack of model interpretability ("black box" problem) restrict clinical trust and integration. Collectively, these barriers continue to slow commercialization despite strong market potential.

AI In Genomics Market: Key Market Segmentation

Type of Component

• Hardware
• Software
• Services

Type of Technology

• Machine Learning
• Computer Vision
• Natural Language Processing
• Others

Type of Functionality

• Genome Sequencing
• Gene Editing
• Clinical Workflow Analysis
• Predictive Genetic Testing
• Others

Type of Application

• Drug Discovery & Development
• Precision Medicine
• Diagnostics / Prognostics
• Agriculture / Animal Genetics
• Others

Type of End-User

• Pharmaceutical & Biotechnology Companies
• Healthcare Providers / Hospitals
• Research & Academia / Government
• CROs
• 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 AI in Genomics Market

• 23andMe
• Cradle Bio
• Deep Genomics
• DNAnexus
• Fabric Genomics
• Gencove
• Google DeepMind
• IBM Watson Health
• Illumina
• Immunai
• Lila Sciences
• Owkin
• Recursion Pharmaceuticals
• Sophia Genetics
• Tempus AI

AI In Genomics Market: Report Coverage

The report on the Ai in genomics market features insights on various sections, including:

• Market Sizing and Opportunity Analysis: An in-depth analysis of the AI in genomics market, focusing on key market segments, including [A] type of component, [B] type of technology, [C] type of functionality, [D] type of application, [E] type of end user, [F] company size, and [G] key geographical regions
• Competitive Landscape: A comprehensive analysis of the companies engaged in the AI in genomics 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 genomics 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 Ai in genomics industry.
• Patent Analysis: An insightful analysis of patents filed / granted in the AI in genomics 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 genomics 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 genomics 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 Genomics 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 Genomics 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 GENOMICS MARKET

• 12.1. AI in Genomics 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. 23andMe*
  • 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. Cradle Bio
• 13.4. Deep Genomics
• 13.5. DNAnexus
• 13.6. DNAnexus
• 13.7. Fabric Genomics
• 13.8. Gencove
• 13.9. Google DeepMind
• 13.10. IBM Watson Health
• 13.11. Immunai
• 13.12. Recursion Pharmaceuticals
• 13.13. Sophia Genetics
• 13.14. Tempus AI
• 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 AI IN GENOMICS 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 Genomics 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. AI in Genomics Market for Software: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
• 18.7. AI in Genomics Market for Hardware: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
• 18.8. AI in Genomics 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 TECHNOLOGY

• 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 Genomics Market for Machine Learning: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
• 19.7. AI in Genomics Market for Computer Vision: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
• 19.8. AI in Genomics Market for Natural Language Processing: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
• 19.9. AI in Genomics 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 FUNCTIONALITY

• 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 Genomics Market for Genome Sequencing: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
• 20.7. AI in Genomics Market for Gene Editing: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
• 20.8. AI in Genomics Market for Clinical Workflow Analysis: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
• 20.8. AI in Genomics Market for Predictive Genetic Testing: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
• 20.8. AI in Genomics 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 BASED ON TYPE OF APPLICATION

• 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 Genomics Market for Drug Discovery & Development: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
• 21.7. AI in Genomics Market for Precision Medicine: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
• 21.8. AI in Genomics Market for Diagnostics / Prognostics: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
• 21.9. AI in Genomics Market for Agriculture / Animal Genetics: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
• 21.10. AI in Genomics Market for Others: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
• 21.11. Data Triangulation and Validation
  • 21.11.1. Secondary Sources
  • 21.11.2. Primary Sources
  • 21.11.3. Statistical Modeling

22. MARKET OPPORTUNITIES BASED ON TYPE OF 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 Genomics Market for Pharmaceutical & Biotechnology Companies: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
• 22.7. AI in Genomics Market for Healthcare Providers / Hospitals: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
• 22.8. AI in Genomics Market for Research & Academia / Government: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
• 22.9. AI in Genomics Market for CROs: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
• 22.10. AI in Genomics Market for Others: 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 GENOMICS 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 Genomics Market in North America: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 23.6.1. AI in Genomics Market in the US: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 23.6.2. AI in Genomics Market in Canada: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 23.6.3. AI in Genomics Market in Mexico: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 23.6.4. AI in Genomics 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 GENOMICS 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 Genomics Market in Europe: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 24.6.1. AI in Genomics Market in Austria: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 24.6.2. AI in Genomics Market in Belgium: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 24.6.3. AI in Genomics Market in Denmark: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 24.6.4. AI in Genomics Market in France: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 24.6.5. AI in Genomics Market in Germany: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 24.6.6. AI in Genomics Market in Ireland: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 24.6.7. AI in Genomics Market in Italy: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 24.6.8. AI in Genomics Market in Netherlands: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 24.6.9. AI in Genomics Market in Norway: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 24.6.10. AI in Genomics Market in Russia: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 24.6.11. AI in Genomics Market in Spain: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 24.6.12. AI in Genomics Market in Sweden: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 24.6.13. AI in Genomics Market in Switzerland: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 24.6.14. AI in Genomics Market in the UK: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 24.6.15. AI in Genomics 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 GENOMICS 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 Genomics Market in Asia: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 25.6.1. AI in Genomics Market in China: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 25.6.2. AI in Genomics Market in India: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 25.6.3. AI in Genomics Market in Japan: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 25.6.4. AI in Genomics Market in Singapore: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 25.6.5. AI in Genomics Market in South Korea: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 25.6.6. AI in Genomics 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 GENOMICS 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 Genomics Market in Middle East and North Africa (MENA): Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 26.6.1. AI in Genomics Market in Egypt: Historical Trends (Since 2020) and Forecasted Estimates (Till 205)
  • 26.6.2. AI in Genomics Market in Iran: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 26.6.3. AI in Genomics Market in Iraq: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 26.6.4. AI in Genomics Market in Israel: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 26.6.5. AI in Genomics Market in Kuwait: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 26.6.6. AI in Genomics Market in Saudi Arabia: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 26.6.7. AI in Genomics Market in United Arab Emirates (UAE): Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 26.6.8. AI in Genomics 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 GENOMICS 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 Genomics Market in Latin America: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 27.6.1. AI in Genomics Market in Argentina: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 27.6.2. AI in Genomics Market in Brazil: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 27.6.3. AI in Genomics Market in Chile: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 27.6.4. AI in Genomics Market in Colombia Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 27.6.5. AI in Genomics Market in Venezuela: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 27.6.6. AI in Genomics 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 GENOMICS 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 Genomics Market in Rest of the World: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 28.6.1. AI in Genomics Market in Australia: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 28.6.2. AI in Genomics Market in New Zealand: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 28.6.3. AI in Genomics Market in Other Countries
• 28.7. Data Triangulation and Validation

29. MARKET CONCENTRATION ANALYSIS: DISTRIBUTION BY LEADING PLAYERS

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

30. ADJACENT MARKET ANALYSIS

SECTION VII: STRATEGIC TOOLS

31. KEY WINNING STRATEGIES

32. PORTER'S FIVE FORCES ANALYSIS

33. SWOT ANALYSIS

34. ROOTS STRATEGIC RECOMMENDATIONS

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

SECTION VIII: OTHER EXCLUSIVE INSIGHTS

35. INSIGHTS FROM PRIMARY RESEARCH

36. REPORT CONCLUSION

SECTION IX: APPENDIX

37. TABULATED DATA

38. LIST OF COMPANIES AND ORGANIZATIONS

39. ROOTS SUBSCRIPTION SERVICES

40. AUTHOR DETAILS