Artificial Intelligence (AI) in Genomics

Global Artificial Intelligence (AI) in Genomics Market to Reach US$6.3 Billion by 2030

The global market for Artificial Intelligence (AI) in Genomics estimated at US$879.6 Million in the year 2024, is expected to reach US$6.3 Billion by 2030, growing at a CAGR of 38.8% over the analysis period 2024-2030. Machine Learning Technology, one of the segments analyzed in the report, is expected to record a 37.7% CAGR and reach US$3.9 Billion by the end of the analysis period. Growth in the Other Technologies segment is estimated at 40.8% CAGR over the analysis period.

The U.S. Market is Estimated at US$298.4 Million While China is Forecast to Grow at 44.6% CAGR

The Artificial Intelligence (AI) in Genomics market in the U.S. is estimated at US$298.4 Million in the year 2024. China, the world`s second largest economy, is forecast to reach a projected market size of US$1.3 Billion by the year 2030 trailing a CAGR of 44.6% over the analysis period 2024-2030. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at a CAGR of 31.0% and 33.8% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 37.4% CAGR.

Global Artificial Intelligence (AI) in Genomics Market – Key Trends & Drivers Summarized

Exploring the Disruptive Power of AI in Genomics

Artificial Intelligence (AI) is accelerating the evolution of genomics from a data-intensive science into a predictive, precision-driven discipline. As genomic sequencing generates massive and complex datasets, traditional bioinformatics methods often fall short in interpreting the volume, variability, and velocity of genetic information. AI—through machine learning, deep learning, and natural language processing—is emerging as a vital enabler, capable of decoding intricate patterns in DNA, RNA, and protein data faster and with greater accuracy. The global application of AI in genomics is rapidly expanding across domains such as disease gene identification, variant classification, precision medicine, and drug discovery. Research institutes, biotech firms, healthcare providers, and pharmaceutical companies are increasingly adopting AI-powered genomic platforms to enhance diagnostic precision and accelerate therapeutic development.

A key trend driving the market is the integration of AI with next-generation sequencing (NGS) technologies. AI algorithms are being used to improve base calling accuracy, detect rare variants, and streamline the annotation of large genomic datasets. Another critical trend is the rise of multimodal AI models that combine genomic data with phenotypic, clinical, and environmental information to deliver holistic insights into disease mechanisms and treatment outcomes. Cloud-based AI platforms are enabling decentralized genomic analysis, allowing researchers to collaborate globally and process data in real time. Furthermore, the growing availability of open-source genomic datasets and AI development toolkits is democratizing access and accelerating innovation across academic and commercial research ecosystems.

How Is AI Enhancing Genomic Analysis and Precision Medicine?

AI is revolutionizing the analysis and interpretation of genomic data by identifying meaningful patterns that human researchers or traditional computational methods may overlook. In variant calling and annotation, AI algorithms can distinguish between benign and pathogenic mutations by analyzing not just genetic sequences but also gene expression profiles, evolutionary conservation, and population-level data. This is significantly improving diagnostic accuracy for rare and complex genetic disorders. Machine learning models are also predicting the functional impact of non-coding DNA—regions once considered "junk DNA"—revealing their roles in gene regulation, disease susceptibility, and developmental biology.

In the realm of precision medicine, AI is enabling more personalized treatment plans by linking a patient’s genomic profile with likely drug responses, toxicity risks, and disease progression patterns. Predictive modeling is being used to stratify patient populations in oncology, neurology, and cardiology, allowing clinicians to select targeted therapies and monitor patient outcomes more effectively. AI tools are also optimizing companion diagnostics by identifying biomarkers that predict response to specific drugs. These capabilities are transforming clinical genomics into a real-time, decision-support tool that empowers physicians to move from generalized protocols to highly individualized treatment pathways.

Where Is AI Driving Innovation in Drug Discovery and Genomic Research?

AI is playing a pivotal role in genomic-driven drug discovery, helping pharmaceutical and biotech companies identify novel therapeutic targets and accelerate pipeline development. By integrating genomic, proteomic, and transcriptomic data, AI models can map complex biological pathways and uncover molecular interactions involved in disease processes. This allows for faster identification of potential drug targets and a better understanding of drug-disease relationships. Generative AI models are also being explored for designing new molecular structures based on the predicted interactions with genetic targets, reducing both time and cost in early-stage drug discovery.

In genomic research, AI is facilitating the creation of disease models by simulating gene-environment interactions and predicting the phenotypic outcomes of specific genetic variants. It is also powering population-scale genomics projects aimed at understanding the genetic basis of diseases across ethnicities and geographies. AI tools are being used to mine scientific literature and clinical databases to synthesize knowledge, prioritize genes of interest, and suggest novel hypotheses. In agricultural genomics, AI is helping researchers identify genes responsible for traits such as drought resistance, crop yield, and disease tolerance—enabling faster and more sustainable crop breeding strategies. Across disciplines, AI is becoming indispensable in managing, mining, and translating genomic data into actionable science.

What’s Fueling the Growth in the AI in Genomics Market?

The growth in the AI in genomics market is driven by several factors closely linked to technological advancements, expanding biomedical datasets, and increasing demand for personalized healthcare solutions. One of the strongest growth drivers is the decreasing cost and rising speed of genome sequencing, which is generating exponential amounts of data that require AI for meaningful interpretation. The advancement of AI techniques—particularly deep learning, convolutional neural networks, and ensemble models—is enhancing the granularity and reliability of genomic predictions.

Cloud computing infrastructure is another key enabler, allowing large-scale genomic datasets to be stored, accessed, and analyzed with minimal latency. The integration of AI platforms with electronic health records (EHRs), laboratory information systems (LIS), and clinical decision support tools is further enhancing their utility in clinical settings. Additionally, increasing investments from governments, research institutions, and venture capital firms into genomics and AI startups are fueling innovation and market expansion. Cross-disciplinary collaborations between AI engineers, geneticists, bioinformaticians, and clinicians are accelerating the development of AI-native genomic tools with real-world clinical utility.

The rising prevalence of genetic and chronic diseases, coupled with the global shift toward preventive and precision medicine, is amplifying the demand for accurate, scalable genomic analysis tools. Ethical and regulatory advances around data sharing, consent, and transparency are also supporting the responsible deployment of AI in genomics. Taken together, these drivers are positioning AI not just as a tool, but as a foundational pillar of the future genomics ecosystem—where data, algorithms, and biology converge to deliver smarter, faster, and more precise healthcare solutions.

SCOPE OF STUDY:

The report analyzes the Artificial Intelligence (AI) in Genomics market in terms of units by the following Segments, and Geographic Regions/Countries:

Segments:
Component (Software Component, Services Component, Hardware Component); Technology (Machine Learning Technology, Other Technologies); Functionality (Genome Sequencing Functionality, Gene Editing Functionality, Other Functionalities); Application (Drug Discovery & Development Application, Precision Medicine Application, Diagnostics Application, Other Applications); End-Use (Pharma & Biotech Companies End-Use, Hospitals & Healthcare Providers End-Use, Research Centers End-Use, Other End-Uses)

Geographic Regions/Countries:
World; United States; Canada; Japan; China; Europe (France; Germany; Italy; United Kingdom; and Rest of Europe); Asia-Pacific; Rest of World.

Select Competitors (Total 23 Featured) -

• Data4Cure
• Deep Genomics
• Fabric Genomics (formerly Omicia)
• Freenome, Inc.
• Genoox
• IBM Corporation
• Illumina, Inc.
• Microsoft Corporation
• NVIDIA Corporation
• Sophia Genetics

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