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시장보고서
상품코드
2094158
유전체학용 인공지능(AI) 시장 - 세계 예측(2026-2032년)Artificial Intelligence in Genomics Market - Global Forecast 2026-2032 |
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360iResearch
유전체학용 인공지능(AI) 시장은 2032년까지 연평균 복합 성장률(CAGR) 27.74%로 성장해 101억 3,000만 달러 규모로 확대될 것으로 예측됩니다.
| 주요 시장 통계 | |
|---|---|
| 기준 연도(2025년) | 18억 2,000만 달러 |
| 추정 연도(2026년) | 23억 2,000만 달러 |
| 예측 연도(2032년) | 101억 3,000만 달러 |
| CAGR(%) | 27.74% |
유전체학용 인공지능(AI) 요약 보고서
유전체학용 인공지능(AI)은 생물학적 데이터의 해석, 검증, 그리고 이를 임상, 연구, 공중보건상의 성과로 전환하는 방식을 재정의하고 있습니다. 차세대 염기서열 분석, 멀티오믹스, 머신러닝, 딥러닝, 자연어 처리 및 클라우드 기반 바이오인포매틱스의 융합을 통해, 변이 해석의 가속화, 질병 위험 평가의 향상, 보다 정밀한 환자 계층화, 그리고 복잡한 유전체 데이터 세트 전반에 걸친 확장 가능한 발견이 가능해졌습니다. 시퀀싱 비용의 감소와 유전체 규모의 데이터셋 확장에 따라, 기존 기법으로는 감지하기 어려운 패턴을 특정하는 데 있어 AI의 중요성은 점점 더 커지고 있습니다.
유전체학용 인공지능(AI)의 혁신적인 변화
유전체학용 인공지능(AI) 동향은 시퀀싱 데이터의 급속한 확대, 머신러닝 아키텍처의 성숙, 그리고 일상적인 헬스케어 및 생의학 연구에서 유전체학의 활용 확대에 힘입어 혁신적인 변화를 이루고 있습니다. 딥러닝 모델은 변이 감지, 단백질 구조 및 기능 추론, 유전체 주석 부여, 종양 분류, 다유전자 위험도 분석 분야에서 점점 더 많이 활용되고 있는 반면, 트랜스포머 기반 접근법은 유전체 서열, 전자 건강 기록, 과학 문헌의 분석을 가속화하고 있습니다. 이러한 진보로 인해 유전자형, 표현형, 환경 및 치료 반응을 연관 짓는 능력이 향상되고 있습니다.
유전체학용 인공지능(AI)이 미치는 누적 영향
유전체학용 인공지능(AI)이 미치는 누적 영향은 발견 주기의 가속화와 대규모 데이터 세트 전반에 걸친 분석의 일관성 향상에서 가장 두드러지게 나타납니다. AI를 활용한 파이프라인은 방대한 시퀀싱 결과를 처리하고, 임상적으로 관련성이 높은 변이를 우선순위화하며, 구조적 변이를 감지하고, 불확실한 소견의 해석을 지원할 수 있습니다. 종양학 분야에서는 AI가 분자 종양 프로파일링, 네오항원 예측, 치료 반응 모델링 및 내성 메커니즘 분석을 지원하고 있습니다. 희귀질환 분야에서는 AI가 표현형과 유전자형의 대조 정확도를 향상시키고, 유전체 변이와 임상적 특징을 연관시킴으로써 진단의 복잡성을 줄일 수 있습니다.
유전체학용 인공지능(AI)에 대한 주요 지역별 인사이트
아시아태평양은 대규모 인구 유전체 프로그램, 시퀀싱 역량 확대, 디지털 헬스 투자, 그리고 강력한 학술 및 임상 연구 네트워크를 바탕으로 유전체학 분야에서 AI 활동이 활발한 지역으로 부상하고 있습니다. 이 지역의 각국에서는 AI를 활용하여 정밀 의학, 암 유전체학, 생식 의학, 감염병 감시 및 농업 유전체학을 지원하고 있습니다. 북미는 선진적인 생의학 연구 인프라, 임상 시퀀싱의 광범위한 도입, 성숙한 데이터 사이언스 역량, 그리고 책임 있는 AI, 데이터 개인정보 보호, 유전체 의료 도입에 관한 정책 활동에 힘입어 AI를 활용한 유전체 혁신의 주요 거점으로 자리매김하고 있습니다.
유전체학용 인공지능(AI)에 관한 주요 그룹 분석
아세안(ASEAN) 국가들에서는 디지털 헬스 현대화, 감염병 감시, 암 연구, 농업 생명공학을 통해 유전체학에 AI를 통합하는 움직임이 점점 더 활발해지고 있습니다. 이 지역의 다양한 인구 구성과 의료 시스템은 지역 실정에 맞는 유전체 AI 모델 개발 기회를 창출하고 있지만, 국경을 초월한 협력을 강화하기 위해서는 표준화 조화, 인재 양성, 그리고 안전한 데이터 공유 메커니즘이 요구되고 있습니다. GCC 국가들은 보다 광범위한 헬스케어 혁신 노력의 일환으로 유전체학을 활용하고 있으며, AI는 집단 선별 검사, 유전성 질환 연구, 맞춤형 의료 및 고급 임상 분석을 지원하고 있습니다. 국가 차원의 유전체 데이터셋과 정밀 의학에 대한 높은 관심으로 인해, 이 지역은 AI를 활용한 유전체 인프라의 중요한 도입 지역으로서의 위상을 확립해 가고 있습니다.
유전체학용 인공지능(AI)에 관한 주요 국가의 인사이트
미국은 광범위한 생의학 연구 네트워크, 임상 시퀀싱의 보급, 고도의 AI 인재, 그리고 정밀의료, 종양학, 희귀질환 진단, 약리유전체학 분야의 활발한 활동을 통해 유전체학 분야에서 AI의 중심적인 추진 역할을 하고 있습니다. 캐나다는 집단 건강 조사, AI 전문 지식, 유전체 의료 프로그램, 그리고 윤리적인 데이터 거버넌스 모델을 통해 기여하고 있습니다. 멕시코는 인구 다양성, 대사성 질환, 암, 공중보건 분야로의 응용과 관련된 유전체 조사를 확대하고 있는 반면, 브라질은 지속적으로 성장하는 생물정보학 생태계의 지원을 받아 감염병 감시, 생물다양성 연구, 종양학, 집단 유전학 분야에서 유전체 연구를 추진하고 있습니다.
업계 리더를 위한 실천적 권고
업계 리더는 AI를 활용한 유전체학에 대한 신뢰를 구축하기 위해, 임상적으로 검증된 AI 모델, 다양한 훈련 데이터 세트, 안전한 데이터 아키텍처, 그리고 투명성이 높은 거버넌스 프레임워크를 우선시해야 합니다. 투자는 시퀀싱 데이터를 표현형, 영상, 검사, 치료 및 예후 정보와 연결하는 상호 운용 가능한 데이터 파이프라인에 초점을 맞추어야 합니다. 조직은 규제 대응 및 임상 도입을 지원하기 위해 모델 모니터링, 편향 평가, 재현성 확인 및 설명 가능성 도구를 도입해야 합니다.
조사 방법론
본 요약 보고서는 유전체학용 인공지능(AI)과 관련된, 검증되고 공개된 증거 기반 정보원에 초점을 맞춘 체계적인 2차 조사 접근법을 통해 작성되었습니다. 이 조사 방법론은 동료 심사를 거친 과학 문헌, 규제 지침, 공중보건 관련 간행물, 유전체 의학 프레임워크, 표준 규격 문서, 그리고 정부 및 정부 간 기구의 자료를 중점적으로 다루고 있습니다. 분석은 시장 규모 추정이나 수익 예측이 아닌, 기술 도입 패턴, 임상 및 연구 적용, 데이터 거버넌스, 지역별 정책 환경, 그리고 검증된 이용 사례에 중점을 두고 있습니다.
결론
유전체학용 인공지능(AI)은 정밀의료, 생물 의학적 발견, 공중보건 감시, 그리고 데이터 기반 생명과학 혁신을 위한 핵심 역량이 되어가고 있습니다. 그 가치는 복잡한 유전체 및 멀티오믹스 데이터 세트를 해석하고, 변이 분석을 가속화하며, 환자 계층화를 개선하고, 대규모로 생물학적으로 의미 있는 패턴을 규명하는 능력에 있습니다. 가장 성공적인 도입 사례에서는 첨단 알고리즘과 고품질 데이터, 임상적 검증, 윤리적 거버넌스, 그리고 안전한 협력 모델이 결합됩니다.
자주 묻는 질문
목차
제1장 서문
제2장 조사 방법
제3장 주요 요약
제4장 시장 개요
제5장 시장 인사이트
제6장 AI의 누적 영향(2026년)
제7장 유전체학용 인공지능(AI) 시장 : AI 기술별
제8장 유전체학용 인공지능(AI) 시장 : 서비스별
제9장 유전체학용 인공지능(AI) 시장 : 시퀀싱 유형별
제10장 유전체학용 인공지능(AI) 시장 : 용도별
제11장 유전체학용 인공지능(AI) 시장 : 최종 사용자별
제12장 유전체학용 인공지능(AI) 시장 : 지역별
제13장 유전체학용 인공지능(AI) 시장 : 그룹별
제14장 유전체학용 인공지능(AI) 시장 : 국가별
제15장 경쟁 구도
제16장 기업 개요
KTH 26.07.29The Artificial Intelligence in Genomics Market is projected to grow by USD 10.13 billion at a CAGR of 27.74% by 2032.
| KEY MARKET STATISTICS | |
|---|---|
| Base Year [2025] | USD 1.82 billion |
| Estimated Year [2026] | USD 2.32 billion |
| Forecast Year [2032] | USD 10.13 billion |
| CAGR (%) | 27.74% |
Artificial Intelligence in Genomics Executive Summary
Artificial intelligence in genomics is redefining how biological data is interpreted, validated, and translated into clinical, research, and public health outcomes. The convergence of next-generation sequencing, multi-omics, machine learning, deep learning, natural language processing, and cloud-based bioinformatics is enabling faster variant interpretation, improved disease risk assessment, more precise patient stratification, and scalable discovery across complex genomic datasets. As sequencing costs have declined and genome-scale datasets have expanded, AI has become increasingly important for identifying patterns that are difficult to detect through conventional computational approaches.
Across healthcare, pharmaceutical research, agriculture, population genomics, and precision medicine, AI-driven genomics supports applications including rare disease diagnosis, oncology biomarker discovery, pharmacogenomics, infectious disease surveillance, synthetic biology, gene editing analysis, and drug target identification. The field is also shaped by rising demand for explainable AI, privacy-preserving analytics, federated learning, regulatory-grade validation, and interoperable data standards. As genomic information becomes more integrated into clinical decision-making, stakeholders are prioritizing accuracy, reproducibility, ethical governance, and secure data collaboration to ensure AI-enabled genomics delivers measurable value without compromising privacy or equity.
Transformative Shifts in the AI Genomics Landscape
The AI in genomics landscape is undergoing transformative shifts driven by the rapid expansion of sequencing data, the maturation of machine learning architectures, and the growing use of genomics in routine healthcare and biomedical research. Deep learning models are increasingly used for variant calling, protein structure-function inference, genome annotation, tumor classification, and polygenic risk analysis, while transformer-based approaches are accelerating analysis of genomic sequences, electronic health records, and scientific literature. These advances are improving the ability to connect genotype, phenotype, environment, and treatment response.
Another major shift is the movement from siloed genomic analysis toward integrated multi-omics intelligence. Combining genomics with transcriptomics, proteomics, epigenomics, metabolomics, imaging, and clinical data is allowing researchers and clinicians to better understand disease mechanisms and therapeutic response. At the same time, privacy-preserving technologies such as federated learning, secure multiparty computation, and differential privacy are gaining relevance because genomic data is inherently identifiable and sensitive. Regulatory bodies and healthcare institutions are also increasing scrutiny of algorithmic transparency, bias mitigation, clinical validation, and data provenance, making responsible AI a core requirement rather than an optional capability.
Cumulative Impact of Artificial Intelligence on Genomics
The cumulative impact of artificial intelligence in genomics is most visible in the acceleration of discovery cycles and the improvement of analytical consistency across large-scale datasets. AI-enabled pipelines can process high-volume sequencing outputs, prioritize clinically relevant variants, detect structural variation, and assist in interpreting uncertain findings. In oncology, AI supports molecular tumor profiling, neoantigen prediction, therapy response modeling, and resistance mechanism analysis. In rare diseases, AI improves phenotype-genotype matching and can reduce diagnostic complexity by linking genomic variants with clinical features.
In drug discovery and development, AI-enhanced genomics is strengthening target validation, biomarker discovery, patient selection, and adverse event risk assessment. Pharmacogenomics is also benefiting from AI models that evaluate how genetic variation influences drug metabolism and efficacy. Public health use cases are expanding through pathogen genomics, antimicrobial resistance tracking, and outbreak surveillance. However, the cumulative impact depends on high-quality reference datasets, diverse population representation, interoperable infrastructure, and rigorous model evaluation. Without these foundations, AI systems risk amplifying existing genomic data biases and producing results that are less generalizable across ancestry groups and healthcare settings.
Key Regional Insights for AI in Genomics
Asia-Pacific is emerging as a high-activity region for AI in genomics due to large-scale population genomics programs, expanding sequencing capacity, digital health investments, and strong academic-clinical research networks. Countries across the region are using AI to support precision medicine, cancer genomics, reproductive health, infectious disease surveillance, and agricultural genomics. North America remains a leading hub for AI-enabled genomic innovation, supported by advanced biomedical research infrastructure, extensive clinical sequencing adoption, mature data science capabilities, and policy activity around responsible AI, data privacy, and genomic medicine implementation.
Europe is advancing AI in genomics through cross-border research collaboration, biobank-linked datasets, health data governance frameworks, and strong emphasis on privacy, ethics, and interoperability. The region's policy environment supports responsible data sharing while maintaining stringent protections for genetic information. Latin America is building momentum through genomic diversity initiatives, infectious disease genomics, and expanding precision health research, although uneven sequencing infrastructure and limited access to specialized bioinformatics resources continue to influence adoption. The Middle East is increasingly investing in national genome initiatives, rare disease research, and AI-enabled healthcare modernization, with particular relevance for hereditary disease studies and population-specific reference data. Africa is gaining strategic importance because of its exceptional genomic diversity, which is critical for reducing global bias in genomic AI models; progress is supported by growing research networks, pathogen genomics capacity, and population health priorities, while infrastructure, funding continuity, and data sovereignty remain central considerations.
Key Group Insights for AI in Genomics
ASEAN countries are increasingly integrating AI in genomics through digital health modernization, infectious disease surveillance, cancer research, and agricultural biotechnology. The region's diverse populations and healthcare systems create opportunities for locally relevant genomic AI models, but harmonized standards, workforce development, and secure data-sharing mechanisms are needed to strengthen cross-border collaboration. GCC countries are using genomics as part of broader healthcare transformation agendas, with AI supporting population screening, inherited disease research, personalized medicine, and advanced clinical analytics. High interest in national genomic datasets and precision healthcare is positioning the region as an important adopter of AI-enabled genomic infrastructure.
The European Union is shaping AI in genomics through coordinated health data policy, research funding, cross-border data spaces, and strong regulatory expectations for privacy, transparency, and clinical reliability. Its emphasis on trusted AI and interoperable health data is influencing global best practices. BRICS countries collectively represent a major opportunity for genomic AI because they combine large and genetically diverse populations, expanding sequencing ecosystems, and growing biomedical research capabilities. Their priorities include population genomics, public health surveillance, oncology, rare disease research, and cost-effective precision medicine. G7 countries continue to influence the direction of AI in genomics through advanced research infrastructure, regulatory leadership, standards development, and clinical implementation of genomic medicine. NATO member countries, while not a genomics-specific bloc, are increasingly relevant in areas such as biosecurity, pathogen surveillance, secure data infrastructure, and resilience planning, where AI-enabled genomics can support preparedness and cross-border health security.
Key Country Insights for AI in Genomics
The United States is a central driver of AI in genomics due to extensive biomedical research networks, widespread clinical sequencing, advanced AI talent, and strong activity in precision medicine, oncology, rare disease diagnostics, and pharmacogenomics. Canada contributes through population health research, AI expertise, genomic medicine programs, and ethical data governance models. Mexico is expanding genomic research with relevance to population diversity, metabolic disease, cancer, and public health applications, while Brazil is advancing genomics in infectious disease surveillance, biodiversity research, oncology, and population genetics, supported by a growing bioinformatics ecosystem.
In Europe, the United Kingdom has strong capabilities in genomics-enabled healthcare, biobank-linked research, and AI-driven clinical discovery. Germany is advancing AI genomics through biomedical engineering, molecular diagnostics, translational medicine, and data infrastructure initiatives, while France is emphasizing genomic medicine, national health data assets, oncology, and rare disease research. Russia maintains strengths in computational biology, population genetics, and biomedical research, though international collaboration dynamics and data governance conditions shape development. Italy and Spain are building AI genomics capabilities in oncology, inherited disease research, population health, and clinical genomics, supported by academic medical centers and European research collaboration.
China is investing heavily in sequencing, AI, precision medicine, agricultural genomics, and population-scale biomedical research, with strong relevance across oncology, reproductive genetics, and infectious disease applications. India is advancing AI in genomics through large and diverse population datasets, rare disease programs, public health genomics, cancer research, and cost-sensitive bioinformatics innovation. Japan applies AI-enabled genomics in aging-related disease research, oncology, pharmacogenomics, regenerative medicine, and high-quality clinical research environments. Australia is strengthening genomic medicine, rare disease diagnosis, cancer genomics, indigenous health research governance, and pathogen genomics, with AI supporting both clinical and public health applications. South Korea is using AI in genomics across precision oncology, digital health, population genomics, and biotechnology research, supported by strong healthcare digitization and advanced sequencing capabilities.
Actionable Recommendations for Industry Leaders
Industry leaders should prioritize clinically validated AI models, diverse training datasets, secure data architectures, and transparent governance frameworks to build trust in AI-enabled genomics. Investment should focus on interoperable data pipelines that connect sequencing data with phenotype, imaging, laboratory, treatment, and outcomes information. Organizations should implement model monitoring, bias assessment, reproducibility checks, and explainability tools to support regulatory readiness and clinical adoption.
Leaders should also pursue privacy-preserving collaboration models that allow institutions to learn from distributed genomic datasets without unnecessary data movement. Workforce development is essential, requiring teams that combine genomics, bioinformatics, clinical science, machine learning, ethics, cybersecurity, and regulatory expertise. For commercial and clinical deployment, decision-makers should align AI genomics solutions with clear use cases such as variant interpretation, oncology profiling, pharmacogenomics, rare disease diagnosis, and public health surveillance. Partnerships with hospitals, laboratories, academic groups, public health agencies, and standards organizations can improve data quality, validation depth, and implementation success.
Research Methodology
This executive summary is developed using a structured secondary research approach focused on verified, publicly available, and evidence-based sources relevant to artificial intelligence in genomics. The methodology emphasizes peer-reviewed scientific literature, regulatory guidance, public health publications, genomic medicine frameworks, standards documentation, and government or intergovernmental resources. Analysis is centered on technology adoption patterns, clinical and research applications, data governance, regional policy environments, and validated use cases rather than market sizing or revenue forecasting.
The research process includes thematic synthesis of AI-enabled genomic applications, cross-regional assessment of healthcare and research infrastructure, evaluation of data privacy and interoperability considerations, and review of emerging implementation priorities. Sources are assessed for credibility, recency, methodological transparency, and relevance to genomics, machine learning, precision medicine, and biomedical data science. Insights are triangulated across multiple evidence categories to reduce reliance on single-source claims and to support balanced interpretation of opportunities, risks, and operational implications.
Conclusion
Artificial intelligence in genomics is becoming a foundational capability for precision medicine, biomedical discovery, public health surveillance, and data-driven life sciences innovation. Its value lies in the ability to interpret complex genomic and multi-omics datasets, accelerate variant analysis, improve patient stratification, and uncover biologically meaningful patterns at scale. The most successful implementations will combine advanced algorithms with high-quality data, clinical validation, ethical governance, and secure collaboration models.
As adoption expands, the field must address persistent challenges including data bias, underrepresentation of diverse ancestries, interoperability gaps, explainability, privacy protection, and regulatory alignment. Regions and organizations that invest in responsible AI infrastructure, diverse genomic datasets, multidisciplinary expertise, and validated clinical workflows will be best positioned to translate AI-enabled genomics into durable scientific and healthcare impact.
Table of Contents
1. Preface
- 1.1. Objectives of the Study
- 1.2. Market Definition
- 1.3. Market Segmentation & Coverage
- 1.4. Years Considered for the Study
- 1.5. Currency Considered for the Study
- 1.6. Language Considered for the Study
- 1.7. Key Stakeholders
2. Research Methodology
- 2.1. Introduction
- 2.2. Research Design
- 2.2.1. Primary Research
- 2.2.2. Secondary Research
- 2.3. Research Framework
- 2.3.1. Qualitative Analysis
- 2.3.2. Quantitative Analysis
- 2.4. Market Size Estimation
- 2.4.1. Top-Down Approach
- 2.4.2. Bottom-Up Approach
- 2.5. Data Triangulation
- 2.6. Research Outcomes
- 2.7. Research Assumptions
- 2.8. Research Limitations
3. Executive Summary
- 3.1. Introduction
- 3.2. CXO Perspective
- 3.3. Market Size & Growth Trends
- 3.4. New Revenue Opportunities
- 3.5. Next-Generation Business Models
- 3.6. Industry Roadmap
4. Market Overview
- 4.1. Introduction
- 4.2. Industry Ecosystem & Value Chain Analysis
- 4.2.1. Supply-Side Analysis
- 4.2.2. Demand-Side Analysis
- 4.2.3. Stakeholder Analysis
- 4.3. Market Dynamics
- 4.3.1. Key Drivers
- 4.3.2. Key Restraints
- 4.3.3. Key Opportunities
- 4.3.4. Key Challenges
- 4.4. Porter's Five Forces Analysis
- 4.5. PESTLE Analysis
- 4.6. Market Outlook
- 4.6.1. Near-Term Market Outlook (0-2 Years)
- 4.6.2. Medium-Term Market Outlook (3-5 Years)
- 4.6.3. Long-Term Market Outlook (5-10 Years)
- 4.7. Go-to-Market Strategy
5. Market Insights
- 5.1. Consumer Insights & End-User Perspective
- 5.2. Consumer Experience Benchmarking
- 5.3. Opportunity Mapping
- 5.4. Distribution Channel Analysis
- 5.5. Pricing Trend Analysis
- 5.6. Regulatory Compliance & Standards Framework
- 5.7. ESG & Sustainability Analysis
- 5.8. Disruption & Risk Scenarios
- 5.9. Return on Investment & Cost-Benefit Analysis
6. Cumulative Impact of Artificial Intelligence 2026
7. Artificial Intelligence in Genomics Market, by AI Technique
- 7.1. Introduction
- 7.2. Deep Learning
- 7.2.1. Autoencoders
- 7.2.2. Convolutional Neural Networks
- 7.2.3. Recurrent Neural Networks
- 7.3. Machine Learning
- 7.3.1. Reinforcement Learning
- 7.3.2. Supervised Learning
- 7.3.3. Unsupervised Learning
- 7.4. Natural Language Processing
- 7.4.1. Sentiment Analysis
- 7.4.2. Text Mining
8. Artificial Intelligence in Genomics Market, by Service
- 8.1. Introduction
- 8.2. Bioinformatics Services
- 8.2.1. Annotation
- 8.2.2. Data Analysis
- 8.2.3. Interpretation
- 8.3. Consulting
- 8.3.1. Implementation Support
- 8.3.2. Strategy Development
- 8.4. Sequencing Services
- 8.4.1. Exome Sequencing
- 8.4.2. Transcriptome Sequencing
- 8.4.3. Whole Genome Sequencing
- 8.5. Software & Platform
- 8.5.1. Cloud-Based
- 8.5.2. On-Premise
9. Artificial Intelligence in Genomics Market, by Sequencing Type
- 9.1. Introduction
- 9.2. Next Generation Sequencing
- 9.2.1. Illumina
- 9.2.2. Ion Torrent
- 9.2.3. PacBio
- 9.3. Sanger Sequencing
- 9.3.1. Capillary
- 9.3.2. Fluorescence
10. Artificial Intelligence in Genomics Market, by Application
- 10.1. Introduction
- 10.2. Agriculture & Animal Genomics
- 10.2.1. Crop Improvement
- 10.2.2. Livestock Breeding
- 10.3. Diagnostics
- 10.3.1. Clinical Diagnostics
- 10.3.2. Research Diagnostics
- 10.4. Drug Discovery
- 10.4.1. Lead Identification
- 10.4.2. Preclinical Testing
- 10.4.3. Target Validation
- 10.5. Precision Medicine
- 10.5.1. Companion Diagnostics
- 10.5.2. Personalized Therapeutics
- 10.5.3. Pharmacogenomics
11. Artificial Intelligence in Genomics Market, by End User
- 11.1. Introduction
- 11.2. Academic & Research
- 11.2.1. Research Institutes
- 11.2.2. Universities
- 11.3. Hospitals & Clinics
- 11.3.1. Diagnostic Laboratories
- 11.3.2. Medical Centers
- 11.4. Pharma & Biotech
- 11.4.1. Biotech Firms
- 11.4.2. Large Pharma
12. Artificial Intelligence in Genomics Market, by Region
- 12.1. Asia-Pacific
- 12.2. Europe
- 12.3. North America
- 12.4. Latin America
- 12.5. Africa
- 12.6. Middle East
13. Artificial Intelligence in Genomics Market, by Group
- 13.1. NATO
- 13.2. G7
- 13.3. BRICS
- 13.4. European Union
- 13.5. ASEAN
- 13.6. GCC
14. Artificial Intelligence in Genomics Market, by Country
- 14.1. China
- 14.2. United States
- 14.3. Japan
- 14.4. India
- 14.5. Germany
- 14.6. United Kingdom
- 14.7. Australia
- 14.8. France
- 14.9. South Korea
- 14.10. Italy
- 14.11. Canada
- 14.12. Russia
- 14.13. Brazil
- 14.14. Mexico
- 14.15. Spain
15. Competitive Landscape
- 15.1. Market Share Analysis, 2025
- 15.2. FPNV Positioning Matrix, 2025
- 15.3. Market Concentration Analysis, 2025
- 15.3.1. Concentration Ratio (CR)
- 15.3.2. Herfindahl Hirschman Index (HHI)
- 15.4. Recent Developments & Impact Analysis, 2025
- 15.5. Product Portfolio Analysis, 2025
- 15.6. Benchmarking Analysis, 2025
16. Company Profiles
- 16.1. 10x Genomics Inc
- 16.2. Agilent Technologies Inc
- 16.3. Alphabet Inc
- 16.4. BenevolentAI Ltd
- 16.5. CytoReason Ltd
- 16.6. Data4Cure Inc
- 16.7. Deep Genomics
- 16.8. DNAnexus Inc
- 16.9. Envisagenics Inc
- 16.10. Exscientia plc
- 16.11. Fabric Genomics Inc
- 16.12. Freenome Holdings Inc
- 16.13. Genomenon Inc
- 16.14. Genoox Ltd
- 16.15. Illumina Inc
- 16.16. Insilico Medicine
- 16.17. Lifebit Biotech Ltd
- 16.18. Microsoft Corporation
- 16.19. MolecularMatch Inc
- 16.20. NVIDIA Corporation
- 16.21. Owkin Inc
- 16.22. PathAI Inc
- 16.23. PrecisionLife Ltd
- 16.24. Qiagen NV
- 16.25. Recursion Pharmaceuticals Inc
- 16.26. SOPHiA GENETICS SA
- 16.27. Tempus AI Inc
- 16.28. Thermo Fisher Scientific Inc
- 16.29. Verge Genomics
- 16.30. WuXi NextCODE
