PUBLISHER: Global Industry Analysts, Inc. | PRODUCT CODE: 1737525
PUBLISHER: Global Industry Analysts, Inc. | PRODUCT CODE: 1737525
Global Artificial Intelligence in Omics Studies Market to Reach US$4.5 Billion by 2030
The global market for Artificial Intelligence in Omics Studies estimated at US$913.5 Million in the year 2024, is expected to reach US$4.5 Billion by 2030, growing at a CAGR of 30.4% over the analysis period 2024-2030. AI Software, one of the segments analyzed in the report, is expected to record a 27.7% CAGR and reach US$2.7 Billion by the end of the analysis period. Growth in the AI Services segment is estimated at 35.3% CAGR over the analysis period.
The U.S. Market is Estimated at US$240.1 Million While China is Forecast to Grow at 28.9% CAGR
The Artificial Intelligence in Omics Studies market in the U.S. is estimated at US$240.1 Million in the year 2024. China, the world's second largest economy, is forecast to reach a projected market size of US$679.7 Million by the year 2030 trailing a CAGR of 28.9% over the analysis period 2024-2030. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at a CAGR of 27.3% and 26.5% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 21.3% CAGR.
Why Is Artificial Intelligence Becoming Indispensable in the Evolution of Omics Research?
Artificial Intelligence (AI) is becoming a transformative force in omics studies-including genomics, transcriptomics, proteomics, metabolomics, and epigenomics-by enabling the analysis of vast, high-dimensional biological datasets that would otherwise be too complex and voluminous for traditional bioinformatics. Omics technologies generate massive quantities of multi-layered data, often requiring integrative and dynamic modeling approaches to uncover meaningful biological patterns, disease mechanisms, and therapeutic targets. AI, particularly machine learning (ML) and deep learning algorithms, is proving instrumental in identifying correlations, predicting gene-disease associations, modeling protein structures, and discovering biomarkers with far greater precision and scalability than conventional methods.
As life sciences shift toward personalized, systems-level medicine, the need for computational tools that can synthesize cross-omics data into actionable insights is becoming increasingly urgent. AI-driven platforms facilitate this by detecting hidden patterns, reducing dimensionality, and generating predictive models that are central to early diagnosis, drug response profiling, and disease progression monitoring. The convergence of omics research and AI is redefining how biomedical researchers, pharmaceutical companies, and precision medicine platforms develop hypotheses, accelerate discovery, and move closer to individualized treatment strategies.
How Are AI Models Enhancing Multi-Omics Integration and Predictive Accuracy?
The application of AI in omics is particularly transformative in multi-omics integration-combining data across genomic, transcriptomic, proteomic, and metabolomic layers to build holistic biological models. Machine learning algorithms, including support vector machines (SVMs), random forests, and deep neural networks, are enabling researchers to uncover latent associations between molecular signatures and complex phenotypes, such as cancer subtypes, neurodegenerative conditions, or rare diseases. AI models excel at managing data heterogeneity, missing values, and non-linear relationships-common challenges in multi-omics analytics.
Deep learning architectures, including convolutional neural networks (CNNs) and autoencoders, are being used to map genotype-to-phenotype links, predict protein folding (e.g., AlphaFold’s breakthrough in structural biology), and identify novel drug targets. Natural language processing (NLP) tools are extracting knowledge from unstructured biological literature to enrich omics annotations and hypotheses. Moreover, unsupervised learning is helping in clustering omics data for patient stratification and disease classification without prior labeling. These AI capabilities are empowering researchers to move from descriptive to predictive and even prescriptive analytics-creating dynamic, learning-based models of biology that can evolve with new data inputs.
Where Is AI in Omics Gaining Momentum and Which Fields Are Leading Applications?
AI applications in omics are gaining momentum in academic research, pharmaceutical R&D, precision oncology, rare disease diagnostics, and agricultural biotechnology. North America dominates the market, with leading institutions, genomics startups, and biopharma companies deploying AI to accelerate biomarker discovery, optimize trial designs, and develop companion diagnostics. Europe, particularly Germany, the U.K., and the Nordic countries, is seeing robust growth in AI-driven omics collaborations through EU-funded research frameworks. Meanwhile, Asia-Pacific-driven by China, Japan, and South Korea-is making strategic investments in AI genomics platforms to support national precision medicine initiatives and population-scale sequencing projects.
Cancer research remains the most active area, with AI models being applied to stratify tumors based on molecular subtypes, predict therapy response, and monitor resistance mechanisms using real-time omics data. In pharmacogenomics, AI is guiding drug metabolism studies by linking genotypes with drug response profiles. Neurodegenerative diseases such as Alzheimer’s and Parkinson’s are also gaining attention, where AI models analyze multi-omics and imaging data to uncover early biomarkers and progression patterns. In the agricultural space, AI-powered omics is enabling the development of climate-resilient crops, trait selection, and pathogen resistance optimization. This broad scope of application is catalyzing interdisciplinary collaborations and creating new commercialization pathways.
What Is Driving the Global Growth of Artificial Intelligence in Omics Studies?
The growth in artificial intelligence in omics studies is driven by several factors, including the explosion of high-throughput sequencing technologies, the affordability of multi-omics platforms, and the critical need for integrative analytics in personalized medicine. A key driver is the exponential increase in omics data generation from large-scale cohort studies, clinical trials, and population-wide genome initiatives. AI provides the computational infrastructure necessary to extract value from these datasets-turning them into actionable insights for diagnostics, therapeutics, and preventive healthcare.
Supportive policies and investments from governments, health systems, and private stakeholders are also fueling market expansion. Initiatives like the NIH’s All of Us Research Program, UK Biobank, and the China Precision Medicine initiative are deploying AI for genomic interpretation and risk modeling. Cloud-based bioinformatics platforms and AI-as-a-service models are further lowering barriers for adoption by research institutions and mid-sized biotech firms. Enhanced data sharing frameworks, coupled with advances in data anonymization and federated learning, are helping address privacy concerns while maximizing model robustness. As AI continues to reshape biomedical research, a critical question emerges: Can artificial intelligence enable a scalable, ethical, and clinically actionable integration of omics into mainstream healthcare and population-level disease prevention?
SCOPE OF STUDY:
The report analyzes the Artificial Intelligence in Omics Studies market in terms of units by the following Segments, and Geographic Regions/Countries:
Segments:
Offering (Software, Services); Technology (Sequencing, Epigenomics, Proteomics, Metabolomics, Other Technologies); Application (Oncology, Infectious Diseases, Neurology, Cardiovascular Diseases, Immunology, Other Applications); End-User (Academic & Research Institutes, Biopharmaceutical Company, Other End-Users)
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 32 Featured) -
TARIFF IMPACT FACTOR
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