AI in Genomics Market By Offering (Software, Services, Hardware), By Technology (Machine Learning, Deep Learning, Natural Language Processing (NLP), Computer Vision, Others), By Functionality (Genome Sequencing, Gene Editing, Predictive Genetic Testing, Genetic Risk Scoring, Data Interpretation & Reporting), By Application (Drug Discovery & Development, Precision Medicine, Clinical Diagnostics, Agricultural Genomics, Research, Others), and By End-user (Pharmaceutical & Biotech Companies, Healthcare Providers, Research Institutes & Academia, Contract Research Organizations (CROs), Diagnostic Laboratories, Others.), Global Market Size, Segmental analysis, Regional Overview, Company share analysis, Leading Company Profiles And Market Forecast, 2025 – 2035

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What trends will shape the AI in Genomics Market in the coming years?

The AI in Genomics Market accounted for USD 1.21 Billion in 2024 and USD 1.72 Billion in 2025 is expected to reach USD 58.62 Billion by 2035, growing at a CAGR of around 42.3% between 2025 and 2035. The AI in Genomics Market entails the implementation of artificial intelligence technologies: machine learning, deep learning, and natural language processing to perform an analysis of genomics data more effectively and precisely. The science of genes and their roles, genomics, generates a vast amount of complex data that artificial intelligence is used to render comprehensible. This aims to establish gene variants, determine disease risks, and inform personal interventions.

Since AI will automate the process of interpreting this data, positioning the field of genomic research and clinical diagnostics at the forefront of scientific discovery, and accelerating the process of data interpretation dramatically decreases the time required to complete it, as well as the corresponding cost of traditional data interpretation strategies. The range of applications in this market includes drug development, clinical care, precision medicine, and agricultural genetic applications. Usage of AI models is also applied in the detection of mutations of genes leading to cancer, rare diseases, and inherited conditions.

What do industry experts say about the AI in Genomics Market trends?

“Our partnership with GE HealthCare marks a transformative step in applying AI and genomics to real-world diagnostics. By combining our genomic expertise with GE’s robust AI infrastructure, we aim to deliver faster, more accurate diagnoses that empower clinicians and improve patient outcomes across global healthcare systems."

• Dr. Aisha Rahman, Chief Medical Innovation Officer at M42

Which segments and geographies does the report analyze?

What are the key drivers and challenges shaping the AI in Genomics Market?

In what ways is the growing availability of genomic datasets supporting AI development in this field?

The increasing accessibility of the genomic data is one of the driving points in the AI in Genomics Market because a huge amount of different genetic data is needed to create correct and strong AI models. These data can help to identify complicated patterns, genetic variations, which cause diseases, and aid in predictive analysis of diagnostics based on the algorithms. As population-scale sequencing projects multiply, AI applications may now exploit cross-ethnic analyses and rare disease discovery, and gene-environment connections.

The environment offers high data density to optimize machine learning models faster and achieve more accurate results within the sphere of clinical genomics. According to the European Bioinformatics Institute (EMBL-EBI), the amount of raw genetic information it maintains in its ENA (European Nucleotide Archive) database as of 2023 was more than 65 petabases, making the sheer volume of genomic data behind the development of AI in the AI in Genomics Market hard to comprehend.

How is the rising demand for personalized medicine driving the adoption of AI in Genomics?

The increasing trend in personalized medicine has also been a key source of diffusing AI in the AI in Genomics Market as it allows the research of an individual, regarding genetics, and aligns it with a set of treatment options. The application of AI technologies can be used to study large quantities of genomic data and detect disease-related mutations fast and with high accuracy, so that investigators and clinicians can determine which type of mutation causes a disease and what response a patient will have to a particular treatment.

This change lends itself to the transition from one-size-fits-all care to improved personalized medical care. AI also improves biomarkers discovery, discovery of drug targets, and quickens the speed of clinical decisions. The National Institutes of Health (NIH) specify that by 2023, more than 1 million genomes were sequenced as part of its All of Us Research Program, highlighting the extent to which the genomic data is being used to spur precision medicine- further evidencing the importance of AI in Genomics Market to process and analyze the data.

How do high computational infrastructure costs limit the adoption of AI in Genomics?

The limitations to the AI in Genomics Market growth caused by high costs of computational infrastructure are notable, as the genomic projects' processing and analysis demand state-of-the-art hardware, cloud, and high-performance computing (HPC) systems. Low- and middle-income areas are characterized by several research institutions and healthcare facilities that do not have sufficient money to invest in such infrastructure. Its frequent requirement of server, graphics processing units, and program tool updates also contributes to the running costs.

Such expenses have posed a challenge to new entrants, including small labs and startups, because innovation and capacity scaling are constrained. This means that though the cost of sequencing a human genome has fallen under 1000 dollars, the computation cost and data storage are still a significant percentage of the main costs of genomic analysis. This cost constraint curtails the application of AI in Genomics, especially in resource-limited environments.

How can the development of explainable AI (XAI) improve trust and adoption in genomics?

The emergence of explainable AI (XAI) poses a great potential to the AI in the Genomics Market since it can increase transparency and trust in the AI system among clinicians, researchers, and regulatory agencies. Due to their learning capabilities granted through generalizations, it is common to see traditional models of AI acting as black boxes, where it is not easy to see how they come up with decisions, especially where such models apply within a healthcare setting. XAI helps to address this using providing a clear and explainable explanation of how algorithms interpret genetic data and provide their conclusions.

This enhances a higher level of confidence in AI-based predictions and regulatory approvals, and adoption by clinicians. It will also help with more informed decision-making and individual treatment planning. Since genomic analysis is increasingly becoming part of the diagnosis and design of therapy, the role of XAI to clarify the outputs in a human-comprehensible manner will be critical to the expansion of AI applications. This is in line with the worldwide initiatives, which the U.S. National Institute of Standards and Technology (NIST) is pursuing most actively and encouraging trustworthy and explainable AI models in the biomedical arena.

What potential does AI have in expanding applications within agricultural genomics?

AI has vast promise not only in developing applications in agricultural genomics but also in offering future opportunities to the AI in Genomics Market beyond human healthcare. Using the complex data of genomes of crops and livestock, artificial intelligence can assist in determining characteristics associated with disease resistance, yield increases, and climate resiliency. This allows breeding to be done much more carefully, saving time and money that would be required to perform the same in the traditional method.

AI will also have a role in tracking genetic diversity and forecasting the reactivity of organisms to environmental stresses, which will enable sustainable agriculture. The combination of the AI yields the speedy editing of the genes and marker-assisted selection, leading to high efficiency and food stability. The U.S. Department of Agriculture (USDA) stated that the modern breeding of crops was already assisted by genomic tools, and the improvement of this process is likely to be made in the context of AI-driven analytics, which gives a substantial growth perspective to the further implementation of AI in agricultural genomics processes.

What are the key market segments in the AI in Genomics industry?

Based on the Offering, the AI in the Genomics Market is classified into Software, Services, and Hardware. The most dominant Offering within the AI in Genomics Market is software. Such control is informed by the exigencies of software in the processing, analysis, and interpretation of complex genomic data through AI algorithms. Genomic software solutions using AI allow fast genome sequencing, variant identification, gene annotation, and the creation of predictive models, which are critical to processes such as precision medicine and drug discovery. The applications are scaled and flexible, and thus they are being widely used by pharmaceutical companies and research organizations diagnostic laboratories.

Further, the constant improvements of machine learning frameworks and cloud-based infrastructure have contributed to the fact that AI software is more convenient and effective. Businesses find it easier to invest in strong software services than hardware, as this comes cheaper and can be integrated into the business processes easily. The continuously self-learning and optimizing nature of software will ensure that it is useful in the field of genomics research in the long term. Altogether, the increased demand for intelligent data analysis as well as real-time insights guarantees that software is the pillar of AI in the Genomics ecosystem.

Based on the Technology, the AI in Genomics Market is classified into Machine Learning, Deep Learning, Natural Language Processing (NLP), Computer Vision, and Others. The AI in Genomics Market is dominated by Machine Learning as a technology segment. It is powerful because it makes it possible to interpret genomic big data about recognizing patterns and associations, which leads to the correct prediction of gene functions, disease relationships, and response to drugs and treatments. These machine learning algorithms see vast use in other tasks, like in the classification of gene variants, in the annotation of genomes, in the discovery of biomarkers, and as such are an essential concern both in research and clinical practice.

Compared to more sophisticated machine learning technology, such as deep learning, machine learning can be trained quickly, interpreted, and applied to different types of data. It is applicable in genomics and is essential in predictive diagnostics, as well as drug target identification. Moreover, its flexibility to supervised, unsupervised, and reinforcement learning is ideal in a wide variety of workflows in genomics. Along with the expanding provision of genomic data through healthcare organizations and an ever-growing need for actionable insights, machine learning maintains its technological frontiers in this market.

Which regions are leading the AI in Genomics Market, and why?

 The North American AI in Genomics Market is growing rapidly, due to high investments in biotechnology, well-developed healthcare infrastructure, and a high number of firms adopting AI technologies. Specifically, the United States comprises such significant actors as IBM, Microsoft, Tempus, and Illumina, which actively use AI to apply genomic research and personalized medicine. The region has experienced a significant government investment in the form of such projects as the All of Us Research Program run by the NIH and grants focused on the development of AI to facilitate precision health. Industry, academic, and research collaborations.

Tech companies work with academic institutions and research centers across North America to develop state-of-the-art genomic tools whose power lies in machine learning. There is also a high density of clinical trials and biopharma R&D, and so there are very demanding genomic analysis requirements. Innovation is also accelerated as regulatory bodies like the FDA are gradually becoming open to AI-based genomic tools. All in all, North America is a global leader in everything related to data due to the strong ecosystem of data availability, qualified workforce, and funding.

The Asia-Pacific AI in Genomics Market is becoming a rapidly developing one in terms of AI in Genomics due to the growing investments in health innovation, exploding genomic research, and growing interest in precision medicine. Leading countries are China, Japan, South Korea, and India, which have genomics programs funded by governments and AI development strategies. Specifically, China has achieved some great advances due to national plans to pursue AI-life science agendas, and Japan is interested in the genomics of aging. Local biotech as well as AI startups are on the rise, collaborating with academic and clinical institutions in the region.

The trend toward AI is getting a boost from investments in next-generation sequencing (NGS) infrastructure and an increased availability of large genetic datasets. The increasing toll of chronic conditions is also driving the demand in healthcare systems to adopt personalized medicine, another driver in the market. Regardless of the data standardization-related challenges and regulatory framework concerns, the Asia-Pacific region is on the verge of establishing itself as a main contributor to the worldwide AI in Genomics ecosystem.  

What does the competitive landscape of the AI in Genomics Market look like?

The competitive environment in the AI in Genomics Market can be described by the combination of tech giants and biotechnology companies, as well as niche players working with AI startups. Companies such as IBM, Microsoft, and NVIDIA introduce potent computing infrastructure and AI platforms to the space, allowing the processing and modeling of genomic data on high-throughput data. In the meantime, biotech companies, including Illumina and Genentech, employ AI in sequencing and drug development, which provides them with a foothold in research as well as clinical practice.

The next new startups in related areas that are certainly taking off, developing only around the interface of AI and genomics, are Deep Genomics, Tempus, and Verge Genomics, with Platforms specialized in the identification of disease genes and drug targets. Intellectual property is an important source of distinction in these firms, as they are commonly based on massive datasets of genomes and bespoke machine learning algorithm implementations.

AI in Genomics Market, Company Shares Analysis, 2024

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Which recent mergers, acquisitions, or product launches are shaping the AI in Genomics industry?

• In July 2025, M42, a global health leader specializing in technology, AI, and genomics, has announced a collaboration with GE HealthCare, a leading provider of medical technology and AI-driven digital solutions. The partnership aims to advance diagnostic accuracy by integrating cloud-based, AI-enabled tools with genomic and clinical innovations.

Report Coverage:

By Offering

• Software
• Services
• Hardware

By Technology

• Machine Learning
• Deep Learning
• Natural Language Processing (NLP)
• Computer Vision
• Others

By Functionality

• Genome Sequencing
• Gene Editing
• Predictive Genetic Testing
• Genetic Risk Scoring
• Data Interpretation & Reporting

By Application

• Drug Discovery & Development
• Precision Medicine
• Clinical Diagnostics
• Agricultural Genomics
• Research
• Others

By End-user

• Pharmaceutical & Biotech Companies
• Healthcare Providers
• Research Institutes & Academia
• Contract Research Organizations (CROs)
• Diagnostic Laboratories
• Others

By Region

North America

• U.S.
• Canada

Europe

• U.K.
• France
• Germany
• Italy
• Spain
• Rest of Europe

Asia Pacific

• China
• Japan
• India
• Australia
• South Korea
• Singapore
• Rest of Asia Pacific

Latin America

• Brazil
• Argentina
• Mexico
• Rest of Latin America

Middle East & Africa

• GCC Countries
• South Africa
• Rest of Middle East & Africa

List of Companies:

• IBM
• Microsoft
• NVIDIA
• Deep Genomics
• Tempus
• Illumina
• PathAI
• DNAnexus
• Sophia Genetics
• Genentech
• F. Hoffmann-La Roche Ltd
• BioXcel Therapeutics
• Verge Genomics
• BenevolentAI
• Helix

Frequently Asked Questions (FAQs)