Global Artificial Intelligence in Genomics Market Insights, Size, and Forecast By Application (Drug Discovery, Genomic Data Analysis, Personalized Medicine, Disease Diagnosis), By Deployment Mode (On-Premise, Cloud-Based), By End Use (Healthcare, Pharmaceuticals, Research Institutions, Biotechnology), By Technology (Machine Learning, Natural Language Processing, Deep Learning, Computational Genomics), By Region (North America, Europe, Asia-Pacific, Latin America, Middle East and Africa), Key Companies, Competitive Analysis, Trends, and Projections for 2026-2035
Key Market Insights
Global Artificial Intelligence in Genomics Market is projected to grow from USD 3.8 Billion in 2025 to USD 85.4 Billion by 2035, reflecting a compound annual growth rate of 17.8% from 2026 through 2035. This substantial growth underscores the transformative impact of AI on the field of genomics, which involves the study of an organism's entire genome. The market encompasses the integration of artificial intelligence and machine learning algorithms to analyze, interpret, and leverage genomic data for various applications, ranging from disease diagnosis to drug development. Key market drivers include the exponential increase in genomic data generation, necessitating advanced analytical tools, and the growing demand for personalized medicine. The ability of AI to identify complex patterns, predict disease susceptibility, and accelerate drug discovery processes is a significant catalyst. Furthermore, advancements in sequencing technologies and a rising prevalence of chronic and genetic diseases are fueling market expansion. However, significant market restraints exist, such as the high cost of AI integration and genomic sequencing, along with ethical concerns surrounding data privacy and the potential for AI bias in genomic interpretations. Regulatory complexities in different regions also pose challenges to widespread adoption. Despite these hurdles, the market presents immense opportunities in areas like preventative healthcare, biomarker discovery, and the development of novel therapeutic targets.
Global Artificial Intelligence in Genomics Market Value (USD Billion) Analysis, 2025-2035
2025 - 2035
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Important trends shaping the market include the increasing adoption of cloud based AI solutions for scalable data processing and analysis, the emergence of explainable AI to enhance trust and transparency in genomic predictions, and a growing focus on multi omics integration where AI combines genomic, proteomic, and metabolomic data for a holistic understanding of biological systems. The market is segmented by Application, Technology, End Use, and Deployment Mode, with Drug Discovery emerging as the leading segment due to the critical role AI plays in accelerating target identification, lead optimization, and predicting drug efficacy and toxicity. This application leverages AI to sift through vast biological databases, reducing the time and cost associated with traditional drug development pipelines. North America currently dominates the market, primarily driven by robust research and development infrastructure, significant investments in AI and genomics, and the presence of key industry players and academic institutions fostering innovation. The region benefits from strong government support for precision medicine initiatives and a high adoption rate of advanced technologies in healthcare.
Conversely, Asia Pacific is poised to be the fastest growing region, propelled by increasing healthcare expenditure, a large and diverse patient population, and growing awareness of personalized medicine. The region is witnessing a surge in government and private funding for genomic research and AI development, coupled with a rising number of collaborations between international and local companies. Key players in the global Artificial Intelligence in Genomics market include Element Biosciences, PerkinElmer, GRAIL, Zebra Medical Vision, YourGene Health, Illumina, Thermo Fisher Scientific, Sophia Genetics, Deep Genomics, and IBM. These companies are actively engaged in strategic initiatives such as partnerships, mergers and acquisitions, and product launches to expand their market footprint and technological capabilities. For instance, many are focusing on developing advanced AI platforms that can integrate seamlessly with existing genomic workflows, offering end to end solutions for research and clinical applications. Their strategies also involve investing heavily in R&D to enhance AI algorithms, improve data interpretation accuracy, and develop user friendly interfaces to cater to a broader spectrum of users, from research scientists to clinicians.
Quick Stats
Market Size (2025):
USD 3.8 BillionProjected Market Size (2035):
USD 85.4 BillionLeading Segment:
Drug Discovery (42.5% Share)Dominant Region (2025):
North America (41.2% Share)CAGR (2026-2035):
17.8%
What is Artificial Intelligence in Genomics?
Artificial intelligence in genomics integrates computational techniques with biological data to unlock profound insights. It employs machine learning algorithms, deep learning, and natural language processing to analyze vast genomic datasets. This includes DNA sequences, RNA expression, protein structures, and clinical phenotypes. AI deciphers complex patterns, predicts gene function, identifies disease drivers, and personalizes treatments. Applications span from variant calling and annotation, predicting drug responses, to discovering novel biomarkers for diagnostics and targeted therapies. It accelerates understanding of genetic diseases, enhances drug development, and revolutionizes precision medicine by making genomic data actionable for patient care and scientific discovery.
What are the Trends in Global Artificial Intelligence in Genomics Market
Genomic Insights Accelerated by Federated AI
Precision Medicine Powered by Explainable AI
AI Driven Multiomics Integration for Drug Discovery
Ethical AI Frameworks for Genomic Data Governance
Edge AI Solutions for Real Time Genomic Analysis
Genomic Insights Accelerated by Federated AI
The genomics field is rapidly advancing, generating enormous datasets. Traditionally, analyzing these vast collections across institutions faced significant challenges due to data privacy regulations and computational burdens. Federated AI emerges as a transformative solution, enabling collaborative model training without direct data sharing. This approach allows diverse genomic datasets from hospitals, research centers, and pharmaceutical companies to contribute to a shared AI model.
Instead of centralizing sensitive patient information, federated learning sends the AI model to each data silo. The model learns locally, and only the updated model parameters, not the raw data, are aggregated globally. This decentralised learning paradigm significantly accelerates the discovery of new disease markers, drug targets, and diagnostic tools. Genomic insights are thus amplified by leveraging distributed computational power and privacy preserving data collaboration, fostering faster breakthroughs in precision medicine and biotechnology.
Precision Medicine Powered by Explainable AI
Precision medicine is transforming healthcare by tailoring treatments to individual genetic profiles and lifestyle. This complex data driven approach often relies on sophisticated artificial intelligence models to identify subtle patterns and make accurate predictions for diagnosis, prognosis, and drug discovery. However, the black box nature of traditional AI can hinder its adoption in critical medical applications where understanding the rationale behind a prediction is paramount.
Explainable AI addresses this challenge by providing transparency into the decision making processes of these powerful algorithms. By elucidating how an AI model arrived at a particular recommendation, clinicians gain trust and can validate the scientific basis, ensuring patient safety and ethical considerations. This synergy between precision medicine's personalized treatments and explainable AI's transparent insights is accelerating the development of highly effective and trustworthy genomic applications globally, from targeted therapies to preventive interventions.
What are the Key Drivers Shaping the Global Artificial Intelligence in Genomics Market
Advancements in AI and Machine Learning Algorithms
Surging Demand for Personalized Medicine and Precision Diagnostics
Exponential Growth of Genomic Data and Bioinformatics
Increasing Investments and Funding in Genomics and AI Research
Rising Adoption of AI for Drug Discovery and Development in Genomics
Advancements in AI and Machine Learning Algorithms
Advancements in AI and machine learning algorithms are a pivotal driver of the Global Artificial Intelligence in Genomics Market. Sophisticated algorithms can now analyze vast genomic datasets with unprecedented speed and accuracy, identifying intricate patterns and correlations previously indiscernible. This capability accelerates drug discovery by pinpointing novel therapeutic targets and predicting drug efficacy. Machine learning enhances disease diagnosis through the early detection of genetic predispositions and personalized treatment strategies. Improved algorithms enable more precise genome editing, enhancing gene therapy development. Furthermore, these advancements are fueling the creation of more robust predictive models for understanding disease progression and identifying biomarkers, consequently boosting the adoption and application of AI across the genomics landscape.
Surging Demand for Personalized Medicine and Precision Diagnostics
The increasing adoption of personalized medicine and precision diagnostics is a major catalyst for the global artificial intelligence in genomics market. Patients and healthcare providers are increasingly seeking tailored medical treatments and accurate disease diagnoses based on an individual's unique genetic makeup. AI powered genomic analysis enables the identification of specific biomarkers and genetic variations that influence drug response and disease susceptibility. This personalized approach promises more effective therapies reduced adverse drug reactions and earlier disease detection. As the demand for these individualized healthcare solutions grows so too does the need for sophisticated AI tools to process interpret and leverage vast amounts of genomic data making AI in genomics indispensable for the future of healthcare.
Exponential Growth of Genomic Data and Bioinformatics
The exponential growth of genomic data is a primary driver in the artificial intelligence in genomics market. Next generation sequencing technologies generate vast amounts of raw genetic information including whole genome sequences transcriptomes and epigenomes at an unprecedented rate and decreasing cost. This deluge of complex biological data far exceeds human capacity for analysis and interpretation. Artificial intelligence tools like machine learning and deep learning are indispensable for processing this massive dataset identifying patterns uncovering novel biomarkers predicting disease susceptibility and personalizing treatments. AI enables efficient data management variant calling annotation and the integration of diverse genomic clinical and lifestyle information. The increasing availability and complexity of genomic data necessitate advanced computational approaches making AI a critical component for extracting actionable insights and driving innovation in precision medicine drug discovery and biotechnology.
Global Artificial Intelligence in Genomics Market Restraints
Ethical and Privacy Concerns Limiting AI Adoption
Ethical and privacy concerns significantly impede the widespread adoption of AI within the genomics market. A primary restraint is the potential for misuse of highly sensitive genomic data. Individuals and regulatory bodies express apprehension regarding data breaches, unauthorized access, and the subsequent discrimination or stigmatization based on genetic predispositions. There are profound questions surrounding data ownership, consent, and the anonymization of complex genomic information, which is notoriously difficult to fully de-identify. The fear of algorithmic bias in AI models, leading to unfair health outcomes for certain populations, further exacerbates these concerns. Moreover, the lack of clear, globally harmonized regulations governing AI use in genomics creates legal uncertainties and hesitancy among healthcare providers and technology developers alike. This collective apprehension about data security, patient rights, and responsible AI deployment limits investment and slows market penetration.
Lack of Standardized Data and Interoperability Challenges
A significant impediment to the global artificial intelligence in genomics market is the absence of standardized data and persistent interoperability issues. Genomic data originates from diverse sources, employing varying formats, terminologies, and collection protocols. This heterogeneity makes it exceedingly difficult to integrate, compare, and analyze vast datasets effectively. Consequently, AI models struggle to learn from a comprehensive and consistent pool of information, limiting their accuracy, generalizability, and ability to identify robust genomic insights. Furthermore, the lack of seamless data exchange between different platforms, research institutions, and healthcare providers hinders collaborative research and the widespread adoption of AI solutions. This fragmented landscape prevents the creation of large, high-quality reference datasets crucial for training sophisticated AI algorithms, thereby impeding market growth and the realization of AI's full potential in genomics.
Global Artificial Intelligence in Genomics Market Opportunities
AI-Powered Genomic Insights for Accelerated Drug Discovery and Precision Medicine Development
AI powered genomic insights revolutionize drug discovery by rapidly analyzing vast genomic datasets to pinpoint novel disease mechanisms and therapeutic targets. This significantly accelerates the identification and validation of promising drug candidates, streamlining the entire research and development pipeline. For precision medicine, AI interprets individual genetic profiles, predicting drug efficacy and potential adverse reactions with unprecedented accuracy. This enables the development of highly personalized treatment strategies, ensuring the right therapy for the right patient at the right time. The opportunity lies in leveraging AI to drastically shorten drug development cycles, reduce associated costs, and enhance success rates. It allows pharmaceutical companies to innovate faster, bringing life changing therapies to market more quickly. Furthermore, AI fuels the creation of advanced diagnostic tools and personalized interventions. This is particularly impactful in regions with growing genomic data and healthcare investments, driving global advancements in therapeutic innovation and patient specific care.
Enhancing Diagnostic Accuracy and Personalized Healthcare through AI-Driven Genomic Data Interpretation
The opportunity in AI driven genomic data interpretation is profound, revolutionizing healthcare by transforming how we diagnose and treat diseases. As genomic sequencing becomes more accessible, the sheer volume and complexity of data overwhelm traditional human analysis. Artificial intelligence excels at processing these vast datasets, identifying subtle patterns, biomarkers, and genetic predispositions that are crucial for precise medical insights.
This capability significantly enhances diagnostic accuracy, enabling earlier and more precise identification of diseases like cancer, rare genetic disorders, and infectious conditions. AI can predict disease progression and treatment responsiveness with unprecedented clarity. Consequently, it unlocks truly personalized healthcare. By understanding an individual's unique genetic makeup, clinicians can tailor therapeutic interventions, select optimal medications avoiding adverse drug reactions, and implement highly specific preventative strategies. This shift from generalized medicine to individualized care promises superior patient outcomes, drives targeted drug discovery, and fosters a more efficient global healthcare system, especially in rapidly growing markets demanding advanced solutions.
Global Artificial Intelligence in Genomics Market Segmentation Analysis
Key Market Segments
By Application
- •Drug Discovery
- •Genomic Data Analysis
- •Personalized Medicine
- •Disease Diagnosis
By Technology
- •Machine Learning
- •Natural Language Processing
- •Deep Learning
- •Computational Genomics
By End Use
- •Healthcare
- •Pharmaceuticals
- •Research Institutions
- •Biotechnology
By Deployment Mode
- •On-Premise
- •Cloud-Based
Segment Share By Application
Share, By Application, 2025 (%)
- Drug Discovery
- Genomic Data Analysis
- Personalized Medicine
- Disease Diagnosis
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Why is Drug Discovery dominating the Global Artificial Intelligence in Genomics Market?
Drug Discovery holds the largest share due to AI's transformative impact on accelerating preclinical research, identifying novel drug targets, and optimizing compound design. AI powered genomic analysis significantly reduces the time and cost associated with traditional drug development, enabling pharmaceutical companies to bring innovative therapies to market faster. The complex nature of genomic data makes AI indispensable for pinpointing disease mechanisms and predicting drug efficacy, driving its significant adoption within this application.
How do different technologies shape the Global Artificial Intelligence in Genomics Market?
The technology segment, particularly Machine Learning and Deep Learning, forms the backbone of AI in genomics. Machine Learning algorithms excel at pattern recognition in vast genomic datasets for variant calling and disease prediction, while Deep Learning further enhances capabilities in complex image analysis and biomarker discovery. Computational Genomics provides the foundational frameworks for integrating and interpreting diverse genomic information, underscoring the necessity of these advanced analytical tools for progress in the field.
What role does Deployment Mode play in the adoption of AI in Genomics?
The Deployment Mode segment highlights the critical choice between On Premise and Cloud Based solutions. Cloud Based platforms are increasingly popular due to their scalability, accessibility, and reduced infrastructure costs, enabling smaller research institutions and startups to leverage powerful AI tools. However, On Premise solutions remain vital for organizations dealing with highly sensitive patient data or requiring maximum control over their computational environment, ensuring data security and regulatory compliance.
What Regulatory and Policy Factors Shape the Global Artificial Intelligence in Genomics Market
The global Artificial Intelligence in Genomics market operates within a dynamic regulatory environment demanding stringent data privacy and security protocols, exemplified by GDPR and HIPAA, due to the highly sensitive nature of genomic information. Ethical considerations are paramount, focusing on informed consent, algorithmic explainability, bias mitigation, and equitable access to AI powered insights. Regulatory bodies like the FDA, EMA, and NMPA are actively developing and refining frameworks for the approval of AI driven genomic tools, often classifying them as medical devices or in vitro diagnostics. This necessitates rigorous validation, demonstration of clinical utility, and robust post market surveillance. Challenges include the lack of international harmonization in legal frameworks and ethical guidelines, complicating cross border data sharing and product deployment. Governments globally are simultaneously investing in policy initiatives and research funding to accelerate innovation while ensuring responsible development and deployment, balancing advancement with patient safety and societal implications.
What New Technologies are Shaping Global Artificial Intelligence in Genomics Market?
The Global Artificial Intelligence in Genomics Market thrives on continuous innovation. Advanced deep learning models are revolutionizing variant detection, disease risk prediction, and drug target identification. Emerging technologies focus on integrating diverse omics data including genomics, transcriptomics, and proteomics, enabling a holistic view of biological systems crucial for precision medicine. AI is optimizing gene editing technologies like CRISPR by improving guide RNA design and predicting off target effects, accelerating therapeutic development. Cloud based AI platforms are democratizing access to powerful genomic analysis tools, facilitating large scale research and clinical applications. Furthermore, advancements in explainable AI and federated learning are addressing data privacy and interpretability concerns, fostering greater trust and adoption. These innovations are critical for developing personalized therapies, enhancing diagnostic accuracy, and driving the future of healthcare.
Global Artificial Intelligence in Genomics Market Regional Analysis
Global Artificial Intelligence in Genomics Market
Trends, by Region
North America Market
Revenue Share, 2025
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Dominant Region
North America · 41.2% share
North America dominates the Global Artificial Intelligence in Genomics Market, commanding a substantial 41.2% market share. This leadership is fueled by several key factors. The region boasts a highly developed healthcare infrastructure and a robust ecosystem for research and development, particularly in genomics and AI. Significant investment in genomic sequencing projects and advanced AI technologies further propels its growth. Moreover, the presence of numerous leading pharmaceutical and biotechnology companies, coupled with strong government support for precision medicine initiatives, fosters a fertile ground for innovation and adoption of AI in genomics. Early adoption of cutting edge technologies and a large pool of skilled professionals also solidify North America's premier position.
Fastest Growing Region
Asia Pacific · 24.3% CAGR
The Asia Pacific Artificial Intelligence in Genomics market is poised for significant expansion, projected to be the fastest growing region globally with an impressive CAGR of 24.3% during the forecast period of 2026 to 2035. This remarkable growth is fueled by several converging factors. Increasing healthcare expenditure across emerging economies is a key driver, enabling greater investment in advanced genomic research and AI integration. A burgeoning patient pool, particularly with chronic and genetic diseases, further necessitates innovative diagnostic and therapeutic approaches. Moreover, growing awareness and acceptance of personalized medicine are accelerating the adoption of AI powered genomic solutions. Strategic government initiatives supporting precision medicine and robust R&D activities in countries like China, India, and Japan are creating a fertile ground for market expansion. Furthermore, the rising availability of vast genomic datasets and advancements in AI algorithms are enhancing the capabilities and applications of AI in genomics within the region.
Top Countries Overview
The U.S. leads globally in AI-driven genomics, propelled by vast datasets, advanced research, and significant investment. Its robust biotech sector, academic institutions, and tech giants foster innovation, particularly in drug discovery and personalized medicine. Regulatory frameworks and ethical considerations also shape this rapidly expanding market, reflecting the nation's key role in the global genomics AI landscape.
China is a major force in the global AI in genomics market. Driven by massive government investment, a large patient population, and a burgeoning tech sector, it's rapidly advancing in gene sequencing, precision medicine, and drug discovery. While data privacy and ethical concerns exist, China's focus on AI-powered genomic solutions positions it as a key innovator and competitor on the international stage.
India is emerging in the global AI in genomics market, leveraging its vast and diverse population for data collection and analysis. Significant government initiatives, coupled with a growing number of startups and research institutions, are driving innovation. While still in early stages, India presents a substantial opportunity due to its large patient base and increasing investment in healthcare technology, positioning itself as a key player in the future.
Impact of Geopolitical and Macroeconomic Factors
Geopolitical competition for AI dominance fuels strategic investments in genomics, particularly in countries with strong biotechnology sectors and national security interests. Data sovereignty concerns and cross border data sharing regulations will shape market access and collaboration, potentially fragmenting the global landscape. Ethical considerations surrounding genetic data privacy and the responsible use of AI in diagnostics will also influence public acceptance and government policies, impacting market adoption and innovation trajectories.
Macroeconomic conditions, including inflation and interest rates, will affect funding availability for research and development in AI genomics startups and established corporations. The economic prosperity of nations with robust healthcare systems will drive demand for advanced genomic solutions. Furthermore, the global shortage of skilled AI and bioinformatics professionals will pose a significant constraint on market growth and technological advancement, irrespective of capital availability.
Recent Developments
- March 2025
Illumina and Deep Genomics announced a strategic partnership to accelerate AI-driven drug discovery in rare genetic diseases. This collaboration will combine Illumina's high-throughput sequencing capabilities with Deep Genomics' AI platform to identify novel therapeutic targets and biomarkers.
- February 2025
Sophia Genetics launched a new AI-powered diagnostic platform, Sophia DDM CancerPlus, for comprehensive genomic profiling in oncology. This platform integrates next-generation sequencing data with advanced AI algorithms to provide more accurate and timely insights for personalized cancer treatment.
- April 2025
Element Biosciences acquired a leading AI bioinformatics company, GenAI Solutions, to enhance its genomic analysis offerings. This acquisition will integrate GenAI Solutions' cutting-edge machine learning algorithms directly into Element's AVITI System, providing customers with more sophisticated data interpretation tools.
- January 2025
PerkinElmer introduced a new AI-enabled software suite, 'OmicsVision AI', designed to streamline data analysis for multi-omics research. This software leverages deep learning to integrate and interpret genomic, proteomic, and metabolomic data, facilitating faster scientific discoveries.
- May 2025
GRAIL announced a strategic initiative to expand its early cancer detection platform by incorporating advanced AI models for liquid biopsy analysis. This initiative focuses on developing new AI algorithms to improve the sensitivity and specificity of detecting multiple cancer types from a single blood sample.
Key Players Analysis
Key players like Illumina and Thermo Fisher Scientific dominate with their sequencing technologies, driving market growth through extensive research and development in AI powered genomic analysis. Element Biosciences and Sophia Genetics focus on innovative sequencing platforms and data interpretation, respectively, often engaging in strategic partnerships to expand their market reach. Deep Genomics and IBM leverage their AI expertise for drug discovery and precision medicine, while GRAIL and Zebra Medical Vision specialize in AI for early disease detection and medical imaging. YourGene Health contributes with genetic testing services, further diversifying the market landscape.
List of Key Companies:
- Element Biosciences
- PerkinElmer
- GRAIL
- Zebra Medical Vision
- YourGene Health
- Illumina
- Thermo Fisher Scientific
- Sophia Genetics
- Deep Genomics
- IBM
- BenevolentAI
- BioAI
- Vir Biotechnology
- ArcherDX
- DNAnexus
- Entre Health
Report Scope and Segmentation
| Report Component | Description |
|---|---|
| Market Size (2025) | USD 3.8 Billion |
| Forecast Value (2035) | USD 85.4 Billion |
| CAGR (2026-2035) | 17.8% |
| Base Year | 2025 |
| Historical Period | 2020-2025 |
| Forecast Period | 2026-2035 |
| Segments Covered |
|
| Regional Analysis |
|
Table of Contents:
List of Figures
List of Tables
Table 1: Global Artificial Intelligence in Genomics Market Revenue (USD billion) Forecast, by Application, 2020-2035
Table 2: Global Artificial Intelligence in Genomics Market Revenue (USD billion) Forecast, by Technology, 2020-2035
Table 3: Global Artificial Intelligence in Genomics Market Revenue (USD billion) Forecast, by End Use, 2020-2035
Table 4: Global Artificial Intelligence in Genomics Market Revenue (USD billion) Forecast, by Deployment Mode, 2020-2035
Table 5: Global Artificial Intelligence in Genomics Market Revenue (USD billion) Forecast, by Region, 2020-2035
Table 6: North America Artificial Intelligence in Genomics Market Revenue (USD billion) Forecast, by Application, 2020-2035
Table 7: North America Artificial Intelligence in Genomics Market Revenue (USD billion) Forecast, by Technology, 2020-2035
Table 8: North America Artificial Intelligence in Genomics Market Revenue (USD billion) Forecast, by End Use, 2020-2035
Table 9: North America Artificial Intelligence in Genomics Market Revenue (USD billion) Forecast, by Deployment Mode, 2020-2035
Table 10: North America Artificial Intelligence in Genomics Market Revenue (USD billion) Forecast, by Country, 2020-2035
Table 11: Europe Artificial Intelligence in Genomics Market Revenue (USD billion) Forecast, by Application, 2020-2035
Table 12: Europe Artificial Intelligence in Genomics Market Revenue (USD billion) Forecast, by Technology, 2020-2035
Table 13: Europe Artificial Intelligence in Genomics Market Revenue (USD billion) Forecast, by End Use, 2020-2035
Table 14: Europe Artificial Intelligence in Genomics Market Revenue (USD billion) Forecast, by Deployment Mode, 2020-2035
Table 15: Europe Artificial Intelligence in Genomics Market Revenue (USD billion) Forecast, by Country/ Sub-region, 2020-2035
Table 16: Asia Pacific Artificial Intelligence in Genomics Market Revenue (USD billion) Forecast, by Application, 2020-2035
Table 17: Asia Pacific Artificial Intelligence in Genomics Market Revenue (USD billion) Forecast, by Technology, 2020-2035
Table 18: Asia Pacific Artificial Intelligence in Genomics Market Revenue (USD billion) Forecast, by End Use, 2020-2035
Table 19: Asia Pacific Artificial Intelligence in Genomics Market Revenue (USD billion) Forecast, by Deployment Mode, 2020-2035
Table 20: Asia Pacific Artificial Intelligence in Genomics Market Revenue (USD billion) Forecast, by Country/ Sub-region, 2020-2035
Table 21: Latin America Artificial Intelligence in Genomics Market Revenue (USD billion) Forecast, by Application, 2020-2035
Table 22: Latin America Artificial Intelligence in Genomics Market Revenue (USD billion) Forecast, by Technology, 2020-2035
Table 23: Latin America Artificial Intelligence in Genomics Market Revenue (USD billion) Forecast, by End Use, 2020-2035
Table 24: Latin America Artificial Intelligence in Genomics Market Revenue (USD billion) Forecast, by Deployment Mode, 2020-2035
Table 25: Latin America Artificial Intelligence in Genomics Market Revenue (USD billion) Forecast, by Country/ Sub-region, 2020-2035
Table 26: Middle East & Africa Artificial Intelligence in Genomics Market Revenue (USD billion) Forecast, by Application, 2020-2035
Table 27: Middle East & Africa Artificial Intelligence in Genomics Market Revenue (USD billion) Forecast, by Technology, 2020-2035
Table 28: Middle East & Africa Artificial Intelligence in Genomics Market Revenue (USD billion) Forecast, by End Use, 2020-2035
Table 29: Middle East & Africa Artificial Intelligence in Genomics Market Revenue (USD billion) Forecast, by Deployment Mode, 2020-2035
Table 30: Middle East & Africa Artificial Intelligence in Genomics Market Revenue (USD billion) Forecast, by Country/ Sub-region, 2020-2035