AI in Omics Studies Market

AI in Omics Studies Market Overview

The global AI in omics studies market size, valued at USD 0.84 billion in 2024, is projected to reach USD 1.18 billion in 2025 and USD 13.81 billion by 2035, representing a CAGR of 27.9% during the forecast period.

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AI in Omics Studies Market: In Summary

• Omics refers to the study of biological molecules specifically DNA, RNA, and proteins to understand their role in diseases and treatment. The discipline of omics, such as genomics, transcriptomics and proteomics, generates vast amount of data, which require automation tools to analyze and interpret data with higher accuracy.
• AI's ability to process, analyze, and interpret vast datasets in real-time is crucial for advancements in disease diagnosis, drug discovery, and tailored therapies is likely to fuel the demand for AI in omics studies.
• The global AI in omics studies industry market is anticipated to reach USD 13.81 billion by 2035, driven by the increasing demand for personalized medicine and the exponential growth and complexity of omics data, which traditional analytical methods struggle to handle efficiently.
• Market players are actively engaged in this field for driving the development of innovative technologies. Some of the recent instances of innovations, include AI-powered PanOmiQ™ platform developed by BioAro (Canada), AI Research's EXAONE Path 2.0 for multi-omics data interpretation developed by LG (South Korea), and Fauna Brain™ AI platform developed by Fauna Bio's (US).
• The software segment dominates the market, holding nearly 55% of the market share, driven by widespread adoption of AI-based solutions across various omics platforms. Software, such as DeepVariant (Google Genomics) and Deep Genomics' BigRNA platform are leveraging AI for highly accurate data analysis, RNA foundation modeling, and drug discovery.. The services segment is projected to grow at a higher CAGR.
• Machine learning holds the majority market share at 40%, essential for clinical data assessment and biomarkers identification. Natural Language Processing (NLP) is expected to exhibit the highest CAGR, due to its capability to analyze large volumes of unstructured text data in research.
• Drug development accounts for the highest share at 35%, leveraging AI predictive models for novel drug target identification. Personalized medicine is poised for the highest CAGR, reflecting the growing focus on tailored drugs based on omics data.
• North America dominates the market with a 38% market share, attributed to significant funding and demand for personalized medicine. Asia-Pacific is projected to expand at the highest CAGR, driven by substantial investments in healthcare infrastructure and government initiatives in AI-driven research.
• The future opportunities lie in single-cell omics and spatial transcriptomics. The integration of AI technology will help to automate data extraction and analysis, thereby saving enormous amounts of time spent with researchers during clinical trials.

AI In Omics Studies Market: Introduction

Artificial intelligence (AI) is a form of computational systems to perform tasks that typically require human intelligence. Technology possesses problem-solving, decision-making capabilities, and recognition patterns, which makes it an invaluable tool for omics studies in healthcare industry. Omics field encompasses genomics, metabolomics, and proteomics, which involves the comprehensive analysis of biological molecules and their intricate interactions within living systems. This domain is critical for advancements in disease diagnosis, personalized medicine, drug discovery, and the design of highly specific therapeutic interventions.

Notable, the study of biologics molecules generate vast amounts of data which can be challenging to analyze with a traditional analytical approach. Artificial intelligence, particularly machine learning and deep learning helps in analyzing biological molecules, discovering biomarkers, and protein structures in real-time with greater precision and scalability. The integration of AI technology in omics studies saves time and extensive resources required to analyze information and driving results to take an actionable insight.

With an increasing emphasis on omics for personalized drug creation and disease identification, resulting in a significant increase in the use of artificial intelligence (AI) in omics studies. The increased support of AI in the field provides significant opportunities for industry players to leverage collaborations and partnerships to drive innovations. For instance, in June 2025, BioAro (Canada) launched a groundbreaking PanOmiQ™ platform at the 2025 BIO International Convention in Boston. This is a clinical-grade genome analysis AI-powered platform for multi-omics and drug discovery solution. As more market players continue to innovate AI technologies for omics, we believe that AI in omics studies market will continue to grow at a higher rate in the future.

AI in Omics Studies Market: Report Attributes

Key Report Attributes	Details
Historical Trend	Since 2020
Forecast Period	Till 2035
Market Size 2025	USD 1.81 Billion
Market Size 2035	USD 13.81 Billion
CAGR (Till 2035)	27.9%

One of the key objectives of the AI in omics studies market report was to estimate the current size, opportunity, and the market growth potential, over the forecast period, till 2035. We have provided informed estimates on the likely evolution of the market for the forecast period.

The global AI in omics studies market analysis also features the likely distribution of the current and forecasted opportunity within the market across various segments, such as type of offering, type of AI technology, type of omics studies, application area, type of technology platform, end user, geographical regions, and leading players.

In order to account for future uncertainties associated with some of the key parameters and to add robustness to our model, we have provided three market forecast scenarios, namely conservative, base, and optimistic scenarios, representing different tracks of industry’s evolution.

Recent Developments in AI in Omics Studies Market

Several recent developments in the field of AI in omics studies industry have occurred, and we outlined some of these below.

• In July 2025, LG AI Research (South Korea) announced the official launch of EXAONE Path 2.0, next-generation precision medical AI model at ASCO 2025. EXAONE Path 2.0 has been trained using multi-omics data, including RNA and DNA, enabling this model to interpret biological processes and genetic insights, which is vital for drug discovery, diseases research and therapeutic drug development.
• In June 2025, Fauna Bio (US) launched Fauna Brain™, a proprietary AI platform specifically designed to enhance target discovery and streamline early-stage research and development process.
• In April 2025, Signios Biosciences (US) launched with AI-driven bioinformatics and advanced multiomics platform to empower precision medicine and drug discovery
• In January 2025, NVIDIA (US) announced a partnership with Illumina (US), IQVIA (US), Mayo Clinic (US) and Arc Institute (US) to transform the USD 10 trillion healthcare and life sciences industry. The partnership aims to leverage generative AI and NVIDIA’s (US) technologies to accelerate genomic research, drug discovery and development process. These solutions help to speed clinical trials and reduce the administrative burden from the healthcare industry.

AI in Omics Studies Market Segmentation Insights

The market report provides comprehensive information on AI in omics studies industry players who are currently active across various segments of the industry as mentioned in the table below.

AI In Omics Studies Market: Market Segmentation

Market Segmentations	Details
Type of Offering	Software Services
Type of AI Technology	Machine Learning Deep Learning Natural Language Processing Computer Vision Data Mining
Type of Omics Study	Genomics Transcriptomics Proteomics Metabolomics Epigenomics
Application Area	Disease Diagnosis Drug Development Personalized Medicine Biomarker Discovery Toxicology Studies
End User	Academic and Research Institutes Biotechnology Companies Healthcare Providers Other End Users
Geographical Regions	North America Europe Asia-Pacific Middle East and Africa Latin America
Market in North America	US Canada Mexico
Market in Europe	France Germany Italy Spain UK Rest of Europe
Market in Asia-Pacific	China India Japan South Korea New Zealand Rest of the Asia-Pacific
Market in Middle East and Africa	Egypt Saudi Arabia South Africa United Arab Emirates
Market in Latin America	Argentina Brazil

Industry Experts on AI in Omics Studies Market

The integration of artificial intelligence in omics has made significant progress, allowing researchers to get deep insights in real-time with higher precision. Recently, Morgan Cheatham, (Vice President at Bessemer Venture Partners), discussed about the evolving landscape of AI in healthcare and multi-omics.

“Morgan Cheatham, Raj Manrai and Andy Beam during NEJM AI Grand Rounds discussed the evolving landscape of artificial intelligence in health care, and the dynamic role of artificial intelligence in automating clinical documentation and transformative potential in genomic medicine. Morgan also stated that AI is reshaping everything from disease phenotyping and clinical decision-making to scaling precision medicine. He also reflects on his work evaluating ChatGPT’s performance on the USMLE, the growing importance of genomic learning health systems, and why the biggest challenge isn’t technological innovation, but aligning payment models to support AI-driven advancements in medicine.”

AI in Omics Studies Market Share Insights

Market Share by Type of Offering: Which Type of Offering Holds the Highest Share of the Market?

• Based on the type of offering, the global market is segmented into software and services. According to the AI in omics market report, the software segment is dominating by holding nearly 55% share of the market. The increasing usage of AI-based software solutions across healthcare and pharmaceutical industry for genomics, metabolomics, and transcriptomics. Notably, the AI-based data integration software, AI-based protein structure prediction software, AI-based drug discovery software and bioinformatics software are widely adopted for analyzing omics data to develop precision medicine, which further fueled its demand.
• Notably, the service segment is likely to grow at a higher CAGR during the forecast period. The increasing shift towards third-party services providers for cloud-based omics data storage and analysis services is likely to bolster the growth of this segment in the future.

Market Share by Type of AI Technology: Will Machine Learning Continue to Dominate the Market in the Future?

• The global market is segmented across different types of AI technology, such as machine learning, deep learning, natural language processing, computer vision, and data mining. According to the AI in omics studies market forecast, the machine learning segment occupies 40% of the market share. This can be attributed to the fact that machine learning technology is essential for clinical data assessment, management, prediction, and identification of biomarkers, which is expected to drive the demand for machine learning technology.
• It is important to highlight that the natural language processing segment is likely to grow at a higher CAGR during the forecast period. The capability to analyze and handle large volume of data, such as patient record, drug discovery analysis record, and unstructured text data generated during research is likely to increase its usage in pharmaceutical industry.

Market Share by Type of Omics Study: How Genomics Segment is Dominating the Market?

• This segment highlight the distribution of global market across different type of omics study, such as genomics, transcriptomics, proteomics, metabolomics, and epigenomics. Among these, the genomics segment holds 34% of the AI in omics studies market share. The expanding research on genomics to develop personalized medicine is likely to increase the demand for AI based software for genomics studies. The integration of artificial intelligence in genomics and on-going advancements in genomic sequencing technologies and software, such as DeepVariant (Google Genomics) and Deep Genomics' BigRNA platform helps in genomic data interpretation can also help to analyze complex genomics data within real-time. This integration also helps to interpret the role of gene mutation in diseases, enabling researchers to develop precision medicine.
• Proteomics segment is expected to grow at a higher CAGR during the forecast period. Proteins are constantly being synthesized and modified to develop protein based therapeutics. Owing to the increasing development of protein based therapies, research is leveraging AI based analytical technologies / platforms to understand the complex structure of proteins. Thus, continuous adoption of AI for proteomics is likely to enhance its adoption across pharmaceutical and biotechnology companies.

Market Share by Application Area: Which Application Area Show Robust CAGR in the Future?

• Based on the application area, the global market is segmented into disease diagnosis, drug development, personalized medicine, biomarker discovery, and toxicology studies. According to the AI in omics studies market analysis, the drug development segment dominates the market by holding 35% of the overall revenue share. AI predictive models can help to analyze vast omics datasets including genomics, proteomics, and transcriptomics to identify novel drug targets. These tools also help to identify pathway associated with the diseases, and drug binding properties with higher precision, thereby enabling researchers to develop tailored drugs.
• While the drug development segment holds the largest share, personalized medicine segment is expected to grow at a higher CAGR during the forecast period. With increasing focus on omics data (genomics, metabolomics, and proteomics) to develop personalized drugs, this segment is likely to show lucrative growth in the future.

Market Share by End User: Why Biotechnology Companies Segment Occupies the Highest Share?

• This segment is distributed across end users, such as academic and research institutes, biotechnology companies, healthcare providers, and other end users. Among these, the biotechnology companies segment holds 45% market share of AI in omics studies, and the trend is likely to remain unchanged in the future. This dominance reflects from the heightened demand for AI and machine learning technologies for clinical data analysis, identification of biomarkers and development of personalized drugs.
• Notably, the academic and research institutes segment is likely to propel at a higher CAGR during the forecast period. The growing requirement for advanced analytical tools to interpret complex biological data generated from genomics and metabolomics research, and the focus on translating research into clinical applications are driving significant AI adoption in this field.

Market Share by Geographical Regions: How North America Holds the Highest Share?

• The global market is distributed across different geographical regions, North America, Europe, Asia-Pacific, Middle East and Africa, and Latin America. Currently, North America occupies 38% share of the AI in omics studies market, and the trend of dominance is likely to remain unchanged in the future. This dominance is attributed due to the increasing funding for research on genomics and rising demand for personalized medicine in this region. Additionally, the supportive regulatory framework to integrate cutting-edge AI technologies for genomic research is likely to fuel the market for AI in omics in this region.
• In Asia-Pacific, the market for AI in omics studies is likely to expand at a higher CAGR in the coming years. Notably, the countries, such as China, India, Japan, and South Korea are making substantial investments in healthcare infrastructure, biomedical research, and precision medicine initiatives, which is expected to drive the demand for AI technologies for omics studies in this region. Additionally, the government initiatives for funding AI-driven research and development, specifically in genomics, support the highest CAGR of this region.

AI in Omics Studies Market: Report Deliverables

Report Deliverables	Details
Excel Data Packs (Complimentary)	Available
Key AI in Omics Studies Companies Profiled	Agilent Technologies (US) Abbott Laboratories (US) BioRad Laboratories (US) BD (US) Illumina (US) Myriad Genetics (US) Novogene (China) Oxford Nanopore Technologies (UK) PerkinElmer (US) Pacific Biosciences of California (US) QIAGEN (Germany) Roche (Switzerland) Sequenom (US) Thermo Fisher Scientific (US) Trimble (US) (A complete list of companies captured is available in the report)
PowerPoint Presentation (Complimentary)	Available
Customization Scope	15% Free Customization

AI in Omics Studies Market Major Takeaways

The “AI in Omics Studies Market Report: Industry Trends and Global Forecasts, till 2035” market report features an extensive study of the current market landscape, market size, market share, market growth, market trends, market value, market forecast, market outlook, statistics, and future opportunities for AI in omics studies industry players. The market research report lays emphasis on the AI in omics software / services that are being developed for pharmaceutical industry. Key takeaways of the market report are briefly discussed below.

Market Landscape: Competitive Landscape and Key Players in AI in Omics Studies Market

The current market landscape of AI in omics studies witnesses the presence of several well-established and new entrants. Market players, such as NVIDIA (US), Illumina (US), QIAGEN (Germany), and Agilent Technologies (US), are undertaking strategic partnerships to accelerate the innovation of advanced technologies for omics studies. For instance, Cell.Ai (UK) signed a partnership with BioSkryb Genomics (US) to co-develop and commercialize next-generation single cell multi-omics solutions. Under this partnership, BioSkryb’s (US) single cell amplification and multi-omics chemistry will be merged with Cell.Ai’s (UK) Oncolncytes platform, AI enabled platform multi-omics platform for analyzing rare cell population, such as circulating tumor cells (CTCs), enabling early detection of cancer and targeted therapy development.

Market Drivers: What are the Key Drivers for Growth in the AI in Omics Studies Market?

The AI in omics studies market growth is propelled by several factors that are listed below:

Growing Demand for Personalized Medicine

The global market for AI in omics studies is propelled by the accelerated demand for personalized medicine to treat chronic diseases. It is worth noting that healthcare industry is shifting from “one-size-fit-all” all approach to tailored drugs that are specifically designed based on unique genetic makeup of a patient. This personalized medicine approach has surged the demand for AI based models for understanding disease mechanism and the development of therapeutic drugs.

Increasing Volume and Complexity of Omics Data

The exponential growth of AI in omics studies market is also driven by the increasing volume and complexity of omics data. Notably, genomic research has been pivotal for the development of targeted therapies. These research generates high volume of omics data that is complex and challenging to interpret with traditional approach. AI technologies, such as machine learning and deep learning for omics, play a significant role in handling and analyzing complex genomics data, allowing researchers to extra invaluable insight of clinical studies.

Market Challenges: What Challenges Affect the Implementation of AI in Omics Studies?

Despite the immense growth potential, there are several challenges that may hinder market progress. Some of the potential challenges are listed below:

Challenges in AI-Powered Omics Data Interpretation

Omics data, such as genomics, and proteomics often contains millions of data sets and missing values due to experimental limitations, sample quality issues, and sensitivity thresholds. Therefore, unstructured data may impact the accuracy of results, thereby creating challenges in data interpretation. Additionally, data produced in different laboratories, using different cloud-based omics analysis tools in different sessions may have systematic differences known as batch effects. These phenomena may cause challenges in data interpretation and provide wrong conclusions.

Furthermore, many AI-powered omics platforms / models function as "black boxes," making it difficult for researchers to interpret results and undermining trust and reproducibility in findings

High Implementation and Operational Costs

Advanced AI solutions and high-throughput omics platforms require significant investment, which can be a barrier for smaller institutions. Furthermore, multi-omics data analysis tools have complex design, therefore it required skilled forces to operate appropriately. The requirement of skilled personnel to operate AI-based tools for data analysis may enhance the overall expenditure, limiting its adoption across small to mid-sized pharmaceutical companies.

Market Trends: What are the Emerging Trends in Cloud-Based and Explainable AI For Omics?

With a significant rise of digital technologies in omics studies, there is a massive impact of AI on multi-omics data integration, making data analysis process simpler and more accurate. The benefits offered by the omics technologies create enormous opportunities for market players as listed below:

Increasing Advancements in AI Technology

Considering the ongoing demand of machine learning in omics, market players are encouraging technological innovations to enhance speed, accuracy, and data-interpretation abilities in real-time. Notably, the current focus of industrial players will be on improving natural language processing, and deep learning to empower pattern recognition, mutation analysis, and variant calling in complex omics datasets. These significant advancements can help to improve disease diagnostics, and drug discovery pipelines results, thereby accelerating clinical trials as well as bridge the gap between drug discovery, development, and commercialization.

Rising Partnerships and Investment in Omics Studies

Notably, the rising partnerships for AI in drug discovery and development may create enormous opportunities for innovation in this field. The partnerships between AI tech firms and omics research organizations, as well as increased funding from initiatives, such as the U.S. All of Us Research Program, are accelerating AI adoption in the field. For instance, in May 2025, MGI Tech (China) unveiled cutting-edge multi-omics technologies at the European Society of Human Genetics (ESHG) 2025 conference in Milan. During this conference, this company has highlighted its advanced sequencing and automation platforms, including the new mid-throughput sequencer DNBSEQ-T1+ and the single-cell library preparation workstation DNBelab C-YellowR 16. These multi-omics data interpretation models are designed to enhance precision and efficiency in omics research.

Market Size Analysis: What Role Do Government Initiatives Play In AI-Driven Omics Research?

In 2025, the global AI in omics studies market size is valued USD 1.18 billion and is poised to reach USD 13.81 billion by 2035. This massive growth is driven by the rising preferences for personalized medicine that demand detailed analysis of genes. Notably, AI in genomics market has made a significant way to fast pace the data analysis process, making it an invaluable tool to strengthen clinical research with accurate data. In addition, several government initiatives to support genomics research have further fueled the demand for AI technology in omics studies.

How Does the Integration with Single-Cell Omics and Spatial Transcriptomics Drives the Market?

The integration of single-cell omics and spatial transcriptomics is amongst the notable AI in omics studies market trends for the industrial players. The integration of these technologies with deeper integrating AI will greatly improve the capabilities for understanding disease heterogeneity and help in formulating more precise personalized diagnostics. In addition, spatial transcriptomics, which studies gene expression in tissues in situ, produces enormous and intricate data.

AI-powered techniques are causing this transformation in the field through the implementation of data extraction automation. Therefore, the identification of expression patterns of genes in different parts of the tissues, and the increase in confidence in the discovery of biomarkers.

How Does AI Accelerate Biomarker Discovery in Omics Research?

Diseases are so complicated and consist of various interactions across diverse biological levels. AI is very suitable for fusing data from different omics platforms (joining genomic mutations with proteomic expression changes and metabolomic profiles). The multi-omics integration makes it possible to acquire a deeper understanding of disease mechanisms and the discovery of more reliable and specific biomarkers that are not evident from a single omics type.

Furthermore, AI for biomarker discovery tool can help to identify the biological signs that indicate the course of a disease (prognostic biomarkers) or the effect of a particular drug on a patient's organism (predictive biomarkers). It is the basis for precision medicine, enabling doctors to customize treatments for the highest efficiency and least adverse reactions.

AI in Omics Studies Market Report Coverage

The market report presents an in-depth analysis, highlighting the capabilities of various stakeholders in the AI in omics studies industry, across different geographies. Amongst other elements, the market report includes:

• An Infographic Executive Summary
• Regulatory Scenario
• Comprehensive Database of Leading Players
• Competitive Landscape
• Porter’s Five Forces Analysis
• Company Competitiveness Analysis
• Startup Ecosystem in AI in Omics Studies Market
• Company Profiles
• Patent Analysis
• Market Forecast and Opportunity Analysis
• Adjacent Market Analysis
• Key Winning Strategies
• Value Chain Analysis

Glossary

• Omics: A collective term for various fields in molecular biology that involve the comprehensive study of biological molecules within a specific biological context.
• Genomics: The study of an organism's complete set of DNA, including its structure, function, evolution, mapping, and editing.
• Epigenomics: The study of the complete set of epigenetic modifications on a cell's genetic material (the epigenome), which are changes in gene expression without altering the DNA sequence.
• Machine Learning (ML): A subset of AI that uses algorithms to enable systems to learn from data, identify patterns, and make predictions or decisions without explicit programming.
• Deep Learning (DL): A subset of machine learning that uses multi-layered artificial neural networks to learn and extract complex patterns from large datasets.
• Biomarker: A measurable indicator of some biological state or condition, often used for disease diagnosis, prognosis, or monitoring treatment response.

From the Author’s Desk

Author’s View on AI in Omics Studies Market

The convergence of Artificial Intelligence and omics studies represents one of the most transformative frontiers in modern science and healthcare. Notably, the in-depth analysis underscores a fundamental shift in how biological research is conducted and how medical interventions are conceived by using machine learning in omics. While the market's growth drives due to the escalating demand for personalized medicine, the challenges are equally significant. Issues surrounding data fragmentation, noise, the "black box" nature of some AI models, and the shortage of skilled professionals demand concerted effort. Addressing them will necessitate continued interdisciplinary collaboration, robust data governance frameworks, and a commitment to advancements in deep learning for omics research.

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