유전체학 AI 시장 : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the aI in genomics market size was valued at USD 1.25 billion in 2025 and is estimated to grow from USD 1.77 billion in 2026 to reach USD 9.93 billion by 2031, at a CAGR of 41.25% during the forecast period (2026-2031).

This report is Segmented by Component (Software, Services, Hardware), Technology (Machine Learning, Deep Learning, and More), Functionality (Genome Sequencing, Gene Editing, and More), Application (Drug Discovery & Development, Precision Medicine, and More), Deployment Model (Cloud-Based, and More), End User (Pharma & Biotech, and More), and Geography. Market Forecasts are Provided in Terms of Value (USD).

Global AI In Genomics Market Trends and Insights

Genomic Data Explosion Outpacing Manual Interpretation

A single whole-genome sequence can generate 200 GB to 300 GB of raw data, and annual genomic data output now runs into tens of exabytes globally. That data load is growing faster than manual analyst teams can review it, so the AI in genomics market is increasingly tied to interpretation speed rather than to data generation alone. This shift also favors vendors that combine algorithms with large, labeled, clinically validated variant libraries, because curation quality matters as much as model design once throughput becomes the constraint. SeqOne's acquisition of Congenica in September 2025 reflected that logic, as the combined platform brought AI sequencing analytics together with a clinical decision library derived from the Wellcome Sanger ecosystem. The combined operation processed more than 200,000 patient genomic analyses in 2025, which was a 3-fold increase over 2024 and shows how fast scaled interpretation platforms are moving into routine use. In practical terms, the AI in genomics market now rewards data curation depth, case volume, and workflow automation more than stand-alone model claims.

Precision Medicine Scaling Across Oncology and Rare Disease

The AI in genomics market is seeing stronger demand from precision medicine, especially where oncology and rare disease programs now rely on the same interpretation stack. A 2025 study in Clinical and Experimental Medicine found that an autonomous AI agent that combined histopathology and genomics reached 91% accuracy for microsatellite instability diagnosis, which is a key biomarker for immunotherapy selection. That result matters because it supports the use of unified models across tissue, molecular, and clinical data rather than isolated decision tools. In rare disease, GeneDx reported preliminary 2025 revenue of USD 427 million, up 41% year over year, while exome and genome revenue grew 54% as genomic newborn screening expanded through state-backed programs. Those two care pathways are no longer developing in isolation, and that is widening the addressable demand base for the AI in genomics market. The same operating model that supports therapy matching in oncology can also support fast diagnosis in pediatric and inherited disorders.

Data Privacy And Clinical AI Compliance Burden

The AI in genomics market faces a real regulatory barrier in Europe, where the EU AI Act treats genomic IVD systems in higher classes as high-risk AI systems. That framework requires risk management, technical documentation, human oversight, and cybersecurity controls, and full provisions for high-risk systems take effect from August 2027. The burden is heavier for vendors that update models continuously, because documentation must track material model changes rather than cover the entire platform once. France's national strategy for artificial intelligence and health data, published in 2025, also ties deployment more closely to secondary-use governance and interoperable health data rules. Those rules favor hybrid and locally controlled architectures, which adds cost and slows scale for smaller companies in the AI in genomics market. Compliance therefore acts as a market filter, not because the technology is weak, but because commercialization is becoming more documentation-heavy.

Other drivers and restraints analyzed in the detailed report include:

1. AI-Led Drug Discovery Shortening Hypothesis Cycles
2. Falling Sequencing Costs Widening Multi-Omic Adoption
3. Scarcity of AI-Genomics Talent and Curated Labels

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Software captured 42.1% of revenue in 2025, which made it the largest component in the AI in genomics market. The segment covers variant interpretation platforms, bioinformatics pipelines, clinical decision-support tools, and genomic foundation model APIs. Its lead position shows that the value stack is moving away from instruments and toward interpretation layers that can be sold on recurring terms. The AI in genomics market is therefore rewarding cloud-native software models that turn analytic logic into a repeatable revenue stream. QIAGEN's May 2025 acquisition of Genoox, valued at USD 70 million to USD 80 million, confirmed this direction by adding the Franklin AI cloud platform to its Digital Insights portfolio. Franklin was active in more than 4,000 healthcare organizations across 50 countries and had supported more than 750,000 case interpretations at the time of the deal.

Services is the fastest-growing component, with a projected CAGR of 42.87% from 2026 to 2031. That pace reflects how much implementation, validation, and ongoing model maintenance matter once AI tools enter regulated environments. The AI in genomics market increasingly depends on professional services because laboratories and health systems often need support for integration, audit trails, and post-deployment tuning. Hardware remains the slowest-growing component, but it still matters in throughput-heavy workflows where compute performance shapes turnaround time. NVIDIA stated in March 2025 that Parabricks v4.5 reduced whole-genome germline analysis to under 8 minutes using 4 GPUs and added support for Blackwell architecture. That matters because software gains are strongest when labs can also process data at scale without long compute bottlenecks. Over time, the AI in genomics industry is likely to see more end-to-end contracts that bundle software, implementation services, and hardware acceleration into a single operating model. Those bundled deals raise switching costs and can stretch customer lifetime value well beyond an initial software subscription.

Machine learning held 63.18% of revenue in 2025, which kept it at the center of the AI in genomics market. Machine learning supports variant-effect prediction, polygenic risk scoring, biomarker classification, and other core tasks across research and clinical use. Classical methods such as gradient boosting and random forests still fit smaller and lower-dimensional clinical datasets well. Deep learning is more useful when the workflow involves large multi-omic inputs and higher-dimensional feature fusion. A systematic review in Clinical and Experimental Medicine reported that graph neural networks and attention-based models delivered strong performance in multi-omic oncology settings. That mix means the AI in genomics market is not moving toward a single model architecture, but toward a layered toolkit where older and newer methods coexist.

Natural language processing is the fastest-growing technology, with a projected CAGR of 43.18% through 2031. That growth comes from foundation model architectures that can read clinical notes, scientific literature, and variant databases in the same workflow. Tempus One added GenAI capabilities in January 2025 that supported patient timeline synthesis, prior authorization assistance, and querying across large volumes of unstructured documents. In the AI in genomics market, this moves NLP from a background enrichment role into the main clinical interface layer. Computer vision also retains a meaningful role in spatial multi-omic and digital pathology workflows, especially where image and molecular data need to be read together. Other methods, including reinforcement learning for design tasks and Bayesian methods for uncertainty handling, are still smaller parts of the mix but continue to widen the technical base of the AI in genomics market.

Geography Analysis

North America held the largest regional position, with 38.52% share in 2025, and that reflects a mature base of sequencing capacity, commercial AI genomics platforms, and earlier reimbursement traction for advanced diagnostics. Tempus AI reported Q1 2026 revenue of USD 348.1 million, up 36.1% year over year, with hereditary testing volume growth of 54% and minimal residual disease test volume growth of more than 500%. Those operating figures show how the AI in genomics market is moving from specialist oncology settings toward broader clinical use in North America. The United States remains the regional center of commercial activity, while Canada signaled its intention in September 2025 to build sovereign genomic data and AI infrastructure through a national genomics strategy and precision health initiative.

Europe remains a major contributor to the AI in genomics market because it combines dense clinical genomics networks with a regulatory framework that increasingly shapes global product design. The EU AI Act is pushing vendors toward validated and auditable systems, and that favors platforms that can document model behavior and workflow controls in detail. The UK Cancer 2.0 program and wider clinical deployment activity across the region show that Europe is not only regulating AI genomics tools, but also expanding the use cases for them. reported in March 2026 that it ended 2025 with 528 core genomics customers across more than 90 countries, including new signings at the Royal Infirmary of Edinburgh, AZ Delta in Belgium, and Ruhr University Bochum in Germany. France's 2025 strategy for artificial intelligence and health data also shows a clear policy push toward interoperable genomic data systems at scale.

Asia-Pacific is the fastest-growing region in the AI in genomics market, with a projected CAGR of 42.81% through 2031. The regional outlook is supported by national genome programs, a broader build-out of AI-native diagnostic infrastructure, and a rising need for local data resources that improve cross-ancestry performance. The AI in genomics market in Asia-Pacific also benefits from the simple fact that new capacity is being added from a lower installed base, which supports faster expansion than in mature regions. Australia and South Korea continue to add weight through national genome initiatives and hospital-linked sequencing programs, while other markets in the region are building out clinical and translational capacity.

Middle East and Africa and South America remain smaller in current revenue terms, but both are moving further into the early commercial phase of adoption. stated in March 2026 that liquid biopsy adoption at King Abdullah International Medical Center in Saudi Arabia and platform use at Brazil's Human Genome and Stem Cell Research Center show that deployment is broadening outside the main established regions.

1. Benevolent AI
2. Complete Genomics
3. Congenica
4. Deep Genomics
5. DNAnexus
6. Fabric Genomics
7. Freenome
8. GeneDx
9. Genomenon
10. Genoox
11. Google
12. Illumina
13. Lifebit
14. NVIDIA
15. Oxford Nanopore Technologies
16. QIAGEN
17. SeqOne
18. SOPHiA GENETICS
19. Tempus AI
20. Velsera

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 Introduction

• 1.1 Study Assumptions & Market Definition
• 1.2 Scope of the Study

2 Research Methodology

3 Executive Summary

4 Market Landscape

• 4.1 Market Overview
• 4.2 Market Drivers
  • 4.2.1 Genomic Data Explosion Outpacing Manual Interpretation
  • 4.2.2 Precision Medicine Scaling Across Oncology and Rare Disease
  • 4.2.3 AI-Led Drug Discovery Shortening Hypothesis Cycles
  • 4.2.4 Falling Sequencing Costs Widening Multi-Omic Adoption
  • 4.2.5 Noncoding Variant Interpretation Lifting Diagnostic Yield
  • 4.2.6 Genomic Foundation Models Improving Multi-Task Inference
• 4.3 Market Restraints
  • 4.3.1 Data Privacy and Clinical AI Compliance Burden
  • 4.3.2 Scarcity of AI-Genomics Talent and Curated Labels
  • 4.3.3 Eurocentric Training Data Limiting Cross-Ancestry Accuracy
  • 4.3.4 Data Sovereignty and Compute Cost Inflation Slowing Scale
• 4.4 Value Chain Analysis
• 4.5 Regulatory Landscape
• 4.6 Technological Outlook
• 4.7 Porter's Five Forces
  • 4.7.1 Threat of New Entrants
  • 4.7.2 Bargaining Power of Suppliers
  • 4.7.3 Bargaining Power of Buyers
  • 4.7.4 Threat of Substitutes
  • 4.7.5 Competitive Rivalry

5 Market Size & Growth Forecasts (Value, USD)

• 5.1 By Component
  • 5.1.1 Software
  • 5.1.2 Services
  • 5.1.3 Hardware
• 5.2 By Technology
  • 5.2.1 Machine Learning
  • 5.2.2 Deep Learning
  • 5.2.3 Natural Language Processing
  • 5.2.4 Computer Vision
  • 5.2.5 Other AI Technologies
• 5.3 By Functionality
  • 5.3.1 Genome Sequencing
  • 5.3.2 Gene Editing
  • 5.3.3 Clinical Workflow
  • 5.3.4 Predictive Genetic Testing
  • 5.3.5 Other Functionalities
• 5.4 By Application
  • 5.4.1 Drug Discovery & Development
  • 5.4.2 Precision Medicine
  • 5.4.3 Clinical Diagnostics
  • 5.4.4 Agriculture & Animal Research
  • 5.4.5 Other Applications
• 5.5 By Deployment Model
  • 5.5.1 Cloud-Based
  • 5.5.2 On-Premise
  • 5.5.3 Hybrid
• 5.6 By End User
  • 5.6.1 Pharmaceutical & Biotechnology Companies
  • 5.6.2 Healthcare Providers
  • 5.6.3 Clinical Laboratories & Diagnostic Centers
  • 5.6.4 Academic & Research Institutes
  • 5.6.5 Other End Users
• 5.7 By Geography
  • 5.7.1 North America
    • 5.7.1.1 United States
    • 5.7.1.2 Canada
    • 5.7.1.3 Mexico
  • 5.7.2 Europe
    • 5.7.2.1 Germany
    • 5.7.2.2 United Kingdom
    • 5.7.2.3 France
    • 5.7.2.4 Italy
    • 5.7.2.5 Spain
    • 5.7.2.6 Rest of Europe
  • 5.7.3 Asia-Pacific
    • 5.7.3.1 China
    • 5.7.3.2 Japan
    • 5.7.3.3 India
    • 5.7.3.4 Australia
    • 5.7.3.5 South Korea
    • 5.7.3.6 Rest of Asia-Pacific
  • 5.7.4 Middle East and Africa
    • 5.7.4.1 GCC
    • 5.7.4.2 South Africa
    • 5.7.4.3 Rest of Middle East and Africa
  • 5.7.5 South America
    • 5.7.5.1 Brazil
    • 5.7.5.2 Argentina
    • 5.7.5.3 Rest of South America

6 Competitive Landscape

• 6.1 Market Concentration
• 6.2 Market Share Analysis
• 6.3 Company Profiles (includes Global level Overview, Market level overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share for key companies, Products & Services, and Recent Developments)
  • 6.3.1 BenevolentAI
  • 6.3.2 Complete Genomics
  • 6.3.3 Congenica
  • 6.3.4 Deep Genomics
  • 6.3.5 DNAnexus
  • 6.3.6 Fabric Genomics
  • 6.3.7 Freenome
  • 6.3.8 GeneDx
  • 6.3.9 Genomenon
  • 6.3.10 Genoox
  • 6.3.11 Google
  • 6.3.12 Illumina, Inc.
  • 6.3.13 Lifebit
  • 6.3.14 NVIDIA
  • 6.3.15 Oxford Nanopore Technologies
  • 6.3.16 QIAGEN
  • 6.3.17 SeqOne
  • 6.3.18 SOPHiA GENETICS
  • 6.3.19 Tempus AI
  • 6.3.20 Velsera

7 Market Opportunities & Future Outlook

• 7.1 White-space & unmet-need assessment