AI is accelerating genomic analysis, drug discovery, precision medicine and CRISPR, creating opportunities in multi-omics, generative AI, diagnostics and industry partnerships.

Dublin, Sept. 18, 2026 (GLOBE NEWSWIRE) -- "AI in Genomics Market, till 2040: Distribution by Type of Component, Type of Technology, Type of Functionality, Type of Application, Type of End User, Company Size and Key Geographical Regions: Industry Trends and Global Forecasts" has been added to ResearchAndMarkets.com's offering.

The global AI in genomics market is projected to expand from USD 1.97 billion in 2026 to USD 317.4 billion by 2040, representing a compound annual growth rate of 43.75% during the forecast period. A new market study examines industry trends, growth scenarios, competitive dynamics, market segmentation, and the long-term outlook for artificial intelligence across genomic research and clinical applications.

Artificial intelligence is transforming genomics by accelerating the analysis of large and complex datasets generated through next-generation sequencing and other advanced research platforms. Machine learning, deep learning, computer vision, and natural language processing are increasingly used to analyze gene expression, predict disease risk, interpret genetic variants, model protein structures, and integrate multi-omics datasets.

AI Accelerates Drug Discovery and Genomic Research

AI-driven platforms are improving efficiency and precision throughout drug discovery and development. These technologies support molecular interaction prediction, lead compound optimization, drug candidate identification, biomarker discovery, and therapeutic response forecasting. In genomic research, AI enables investigators to identify disease-associated genes, evaluate genetic variations, and generate insights that support targeted therapies.

AI applications are also contributing to more efficient clinical trial design through patient stratification, outcome prediction, and improved identification of suitable trial participants. Their adoption is expected to shorten research timelines, reduce development costs, and support the global transition toward precision medicine.

Key AI in Genomics Market Growth Drivers

Rapid growth in genomic data is a primary factor driving demand for AI-based analysis. The volume and complexity of sequencing data increasingly exceed the capabilities of conventional analytical approaches, strengthening the need for scalable computational platforms and advanced pattern-recognition technologies.

Additional market drivers include rising investment in personalized medicine, continued improvements in data-processing infrastructure, declining computational costs, and growing collaboration among pharmaceutical companies, biotechnology firms, healthcare organizations, academic institutions, and technology providers. Strategic partnerships, funding activity, and patent development are expected to accelerate commercialization and expand the range of available solutions.

Emerging Applications and Industry Trends

Precision medicine remains one of the most significant applications for AI in genomics. AI platforms can integrate genomic sequencing data with electronic health records, medical imaging, environmental information, and data from connected health devices. These capabilities support early disease detection, biomarker identification, therapeutic response prediction, and individualized treatment planning.

Other prominent trends include multi-omics integration, generative AI for protein structure prediction and synthetic sequence development, and AI-supported CRISPR workflows. Advanced algorithms can improve guide design, assess off-target effects, and enhance the precision of genome-editing programs. These developments are creating new opportunities in oncology, rare diseases, diagnostics, prognostics, agriculture, and animal genetics.

Market Challenges

Despite strong growth potential, the AI in genomics market faces challenges related to data quality, interoperability, standardization, privacy, and regulatory compliance. Genomic datasets obtained from heterogeneous sources may contain inconsistencies or biases that affect model performance and reproducibility.

Compliance with GDPR, HIPAA, and other data-protection requirements remains essential because of the sensitivity of genetic information. High computational requirements, shortages of professionals with combined AI and genomics expertise, and limited model interpretability may also restrict clinical adoption. Addressing these barriers will be important for increasing institutional trust and enabling broader commercialization.

AI in Genomics Market Segmentation

Component: Hardware, software, and services
Technology: Machine learning, computer vision, natural language processing, and other AI technologies
Functionality: Genome sequencing, gene editing, clinical workflow analysis, predictive genetic testing, and related functions
Application: Drug discovery and development, precision medicine, diagnostics and prognostics, agriculture, and animal genetics
End User: Pharmaceutical and biotechnology companies, healthcare providers, hospitals, research institutions, government organizations, and contract research organizations
Geography: North America, Europe, Asia, Latin America, the Middle East and North Africa, and the rest of the world

Report Coverage and Strategic Value

The AI in genomics market report provides market sizing and revenue forecasts across major segments and geographical regions. It includes competitive landscape assessment, company profiles, megatrend evaluation, patent analysis, recent industry developments, Porter's Five Forces analysis, and a SWOT framework.

The study addresses the current and future AI in genomics market size, leading companies, major growth drivers, regional opportunities, partnership and funding trends, and the distribution of revenue across key segments. It also maps competitive activity and identifies potential growth areas for established market participants and emerging entrants.

Designed to support strategic planning, investment assessment, partnership development, and market-entry decisions, the report includes expert-validated findings, analytical dashboards, customizable content, interactive research-team walkthroughs, and qualifying report updates.

Key Topics Covered:

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
*similar detail is presented for other below mentioned companies based on information in the public domain
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

A selection of companies mentioned in this report includes, but is not limited to:

. 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
For more information about this report visit https://www.researchandmarkets.com/r/pdmxnv

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AI in Genomics Market Set for Transformative Growth Through 2040, Driven by Advanced Technologies, Expanding Applications and Rising Global Adoption