AI in Genomics Market Enhances Drug Discovery & Precision Medicine

AI in Genomics Market Applications and Future Trends

The AI in genomics market size is forecast to grow at a CAGR of 23.6%, from USD 1.67 billion in 2025 to USD 11.26 billion by 2034, over the forecast period from 2025 to 2034, as a result of growing cross-industry collaborations and increasing R&D.

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Key Takeaways

• North America held a major share of the market in 2024.
• Asia-Pacific is projected to host the fastest-growing market in the coming years.
• By component, the software segment held a dominant presence in the market in 2024 and is projected to expand rapidly in the market in the coming years.
• By technology, the machine learning segment held the largest share of the market in 2024.
• By technology, the computer vision segment is predicted to witness significant growth in the market over the forecast period.
• By functionality, the genome sequencing segment led the global market in 2024 and is anticipated to grow with the highest CAGR in the market during the studied years.
• By application, the drug discovery & development segment registered its dominance over the global market in 2024.
• By application, the precision medicine segment is expected to grow at the fastest rate in the market during the forecast period.
• By end-user, the pharmaceutical & biotechnology companies segment dominated the global AI in genomics market in 2024.
• By end-user, the healthcare providers segment is estimated to show the fastest growth in the market over the forecast period.

AI in Genomics Market Trends

• In February 2025, Tempus AI, Inc. announced the acquisition of Ambry Genetics to strengthen the abilities of both to deliver cutting-edge solutions. Tempus A is a tech company leasing the adoption of AI to advance precision medicine, and Ambry Genetics is a leader in genetic testing.
• In February 2023, Integrate.ai announced its central role in the Canadian Platform for Genomics and Precision Health. The platform was developed in collaboration with DNAstack to help deliver better outcomes for Canadians.
• In February 2023, Accenture announced a strategic investment in Ocean Genomics to assist biopharma companies in discovering and developing more effective diagnostics and therapeutics. The investment was made to accelerate AI-driven drug discovery and development of personalized medicines.

Embracing the AI Era in Genomics:

The field of genomics has been revolutionized by the integration of artificial intelligence (AI) technologies. AI in genomics combines the power of advanced algorithms and machine learning techniques with large-scale genomic data to unlock new insights into the human genome and drive advancements in personalized medicine. AI in the genomics market has witnessed significant growth in recent years, with a wide range of applications across research, clinical diagnostics, drug discovery, and precision medicine.

One of the prominent areas where AI has made a substantial impact in genomics is research. AI algorithms can analyze vast amounts of genomic data, identify patterns, and uncover hidden relationships between genes, genetic variations, and diseases. This has led to the discovery of novel disease biomarkers, potential therapeutic targets, and a deeper understanding of the genetic basis of complex diseases. In the field of clinical diagnostics, AI in genomics has enabled more accurate and efficient interpretation of genomic data for disease diagnosis and risk assessment. AI algorithms can analyze genetic variants, predict disease susceptibility, and provide personalized treatment recommendations based on an individual's genomic profile. This has the potential to revolutionize healthcare by enabling tailored and precise medical interventions.

The growth of AI in genomics is also driving advancements in precision medicine. With the integration of AI technologies, healthcare providers can leverage genomic data to make informed decisions about disease prevention, diagnosis, and treatment. By considering an individual's unique genomic profile, AI-powered precision medicine approaches can deliver more targeted and effective interventions, reducing adverse reactions and improving patient outcomes.

Thriving Together: Market Growth Propelled by New Product Launches and Collaborative Ventures

The AI in the genomics market is experiencing significant growth, and one of the key drivers behind this expansion is the continuous launch of new products and the formation of collaborative ventures within the industry. These factors are contributing to the development of innovative solutions and the advancement of AI technologies in genomics.

Furthermore, collaboration between different stakeholders in the genomics and AI sectors is a significant driver of market growth. Academic institutions, research organizations, technology companies, and healthcare providers are joining forces to combine their expertise and resources in developing AI-driven genomics solutions. Collaborative ventures facilitate the exchange of knowledge, access to diverse datasets, and the pooling of resources, accelerating the development and adoption of AI.

For instance,

• In April 2023, Ultima Genomics announced a collaboration, combining the "$100 genome" technology with Genome Insight's AI-powered analysis. This partnership aims to bring together advanced genetic sequencing capabilities with cutting-edge artificial intelligence algorithms for genomic data analysis. By integrating these technologies, Ultima Genomics seeks to provide cost-effective and efficient solutions for genomic analysis, enabling researchers and healthcare professionals to gain valuable insights from genomic data at an affordable price. This collaboration has the potential to revolutionize genomic research and contribute to advancements in personalized medicine and other areas of healthcare.

Building a Smarter Healthcare System: Government Funding Boosts AI Research

The AI and Health and Care Awards is an initiative led by the NHS AI Lab, with the aim of promoting and facilitating the responsible adoption of artificial intelligence in the field of health and care. Recognizing the transformative potential of AI, the awards program seeks to accelerate the integration of AI technologies in a safe, ethical, and effective manner. By supporting innovative AI projects and solutions, the program aims to improve patient outcomes, enhance healthcare delivery, and drive advancements in the health and care sector. Through collaboration with various stakeholders, the NHS AI Lab is working towards harnessing the power of AI to benefit patients, clinicians, and the overall healthcare system.

• In May 2023, The U.S. National Science Foundation (NSF) made an exciting announcement regarding the expansion of its National Artificial Intelligence Research Institutes initiative. Building on the success of the initial round of funding in 2020, the NSF is establishing 11 new institutes, bringing the total number to 18. This expansion represents a combined investment of $220 million, allowing the institutes to have a broader reach across the United States.
• Through three rounds of awards, a total of around $138 million had been invested in 86 AI technologies as part of the AI and Health and Care Awards program. These investments have had a significant impact, benefiting over 300,000 patients and leading to improvements in the care and treatment of various health conditions. AI technologies have been applied in areas such as cancer, heart disease, diabetes, mental health, and neurological disorders, contributing to advancements in diagnosis, treatment, and patient care. The investments enabled the development and deployment of innovative AI solutions that have the potential to revolutionize healthcare and make a positive difference in the lives of patients across the UK.
• The third round of the AI in Health and Care Awards resulted in funding being awarded to nine companies. These awards aim to accelerate the testing and deployment of the most promising AI technologies in the healthcare sector. Since their establishment in 2019, the awards have focused on developing AI technology to assist patients in managing long-term conditions, enhancing the speed and accuracy of diagnosis, and addressing the challenges posed by the Covid-19 pandemic, such as backlogs and waiting lists. The awarded companies will play a crucial role in advancing healthcare through the application of AI, ultimately improving patient outcomes and the overall delivery of healthcare services.

Customized Care: Market Growth Driven by Precision Medicine and Personalized Healthcare

The AI in genomics market is witnessing significant growth, and one of the major drivers behind this expansion is the increasing focus on precision medicine and personalized healthcare. The convergence of AI and genomics is revolutionizing healthcare by enabling tailored treatments and individualized care based on a patient's genetic makeup. This trend is propelling market growth and opening up new opportunities for AI in genomics.

Precision medicine aims to provide targeted treatments based on an individual's unique genetic characteristics. AI algorithms and machine learning techniques are used to analyze large-scale genomic data and identify specific genetic markers associated with diseases, drug responses, and treatment outcomes. By leveraging AI in genomics, healthcare providers can develop personalized treatment plans and interventions, leading to improved patient outcomes. The growing adoption of precision medicine approaches is driving the demand for AI technologies in genomics.

In addition, personalized healthcare goes beyond precision medicine and encompasses a holistic approach to patient care. AI in genomics plays a crucial role in providing personalized healthcare by analyzing genomic data and integrating it with other health-related information such as medical records, lifestyle data, and environmental factors. This comprehensive analysis enables healthcare professionals to develop personalized prevention strategies, early detection methods, and targeted interventions. The increasing emphasis on personalized healthcare is fueling the demand for AI in genomics solutions.

Furthermore, AI-powered tools and algorithms are essential for the analysis of large-scale genomic data. Genomic sequencing technologies generate vast amounts of data, and AI can effectively process, interpret, and extract meaningful insights from this data. By combining AI with genomics, researchers and clinicians can uncover patterns, identify genetic variants, and predict disease risks. The ability to efficiently analyze genomic data is crucial for advancing precision medicine and personalized healthcare, driving the adoption of AI in genomics.

Moreover, the increasing adoption of genomic testing for disease diagnosis and risk assessment is contributing to the growth of AI in genomics. Genomic tests provide valuable insights into an individual's genetic predispositions, enabling early detection and personalized interventions. AI algorithms can enhance the accuracy and efficiency of genomic testing, aiding in the interpretation of test results and guiding healthcare decisions. The rising demand for genomic testing is driving the adoption of AI technologies in genomics.

From Data to Insights: Overcoming Data Quality and Standardization Barriers in AI Genomics

One of the key restraints in the AI in genomics market is the challenge of data quality and standardization. Genomic data is complex and heterogeneous, often originating from different sources and generated using various platforms and technologies. This variability in data formats, quality, and standards poses significant challenges for AI algorithms and analysis pipelines. Several aspects related to data quality and standardization can hinder the progress of AI in genomics market.

Genomic data can exhibit significant variability due to differences in sequencing technologies, sample preparation methods, and data generation protocols. This variability can affect the performance and generalizability of AI algorithms, making it challenging to draw accurate conclusions from the data. In addition, genomic data is prone to errors, including sequencing errors, alignment errors, and variant calling errors. These errors can impact the reliability and accuracy of AI models trained on such data. Moreover, incomplete or missing data can introduce biases and affect the robustness of AI algorithms.

Furthermore, genomic data is often stored in various file formats, such as FASTQ, BAM, VCF, and others. The absence of standardized formats across different sequencing platforms and analysis tools can make it difficult to integrate and compare data from different sources. It requires additional preprocessing and harmonization steps to ensure compatibility and interoperability. Interpreting genomic data requires accurate and up-to-date annotations that link genomic variants to relevant biological information and disease associations. However, the annotation of genomic data is an ongoing process, and new discoveries continually emerge. The lack of standardized and comprehensive annotation resources can impact the interpretation and utilization of genomic data in AI applications.

Moreover, genomic data is highly sensitive and contains personal health information. Ensuring data privacy, security, and compliance with ethical guidelines are paramount in genomic research and AI applications. These concerns can limit the availability and sharing of high-quality genomic data, which is essential for training robust AI models.

Addressing these challenges requires concerted efforts from researchers, clinicians, and regulatory bodies. Here are some potential strategies to improve data quality and standardization in AI-driven genomics:

• Standardization Efforts: Establishing standardized data formats, protocols, and quality control measures can enhance data interoperability and comparability across different genomic datasets and platforms. Initiatives like the Global Alliance for Genomics and Health (GA4GH) aim to develop standards and best practices for genomic data sharing and analysis.
• Data Quality Control: Implementing rigorous quality control measures during data generation, processing, and analysis can help identify and mitigate data quality issues. Standardized quality control metrics and guidelines can assist in identifying and filtering out low-quality data points.
• Data Sharing and Collaboration: Encouraging data sharing and collaboration among researchers, institutions, and organizations can facilitate the pooling of large-scale, high-quality genomic datasets. This sharing promotes data harmonization, enables benchmarking of AI models, and improves the generalizability of AI algorithms.
• Improved Annotation Resources: Continued efforts to enhance genomic annotation resources and databases can support accurate interpretation and analysis of genomic data. Collaborative initiatives to curate and update annotation databases can ensure that AI algorithms have access to comprehensive and reliable information.
• Regulatory Frameworks: Developing and implementing regulatory frameworks that address data privacy, security, and ethical considerations can foster responsible data sharing and utilization in AI-driven genomics. Ensuring compliance with regulations and guidelines can build trust and facilitate the responsible use of genomic data.

By addressing data quality and standardization challenges, the AI in genomics market can unlock its full potential in transforming healthcare, enabling personalized medicine, and advancing our understanding of genetic diseases.

Unleashing the Power of AI: Seizing the Opportunity in Genomics with Next-Generation Sequencing (NGS)

The field of genomics has experienced a significant transformation with the advent of Next-Generation Sequencing (NGS) technologies. These advanced sequencing platforms have enabled the generation of vast amounts of genomic data, opening up new opportunities for AI applications in genomics. The combination of NGS and AI has the potential to revolutionize various aspects of genomics research, diagnosis, and personalized medicine. 

NGS generates massive datasets that require sophisticated analysis methods to extract meaningful insights. AI algorithms, such as machine learning and deep learning, can process and analyze these large-scale datasets, uncovering hidden patterns, identifying genetic variants, and predicting disease outcomes. AI-driven data analysis accelerates the discovery of novel genetic markers, biomarkers, and therapeutic targets, leading to advancements in disease understanding and treatment. In addition, NGS combined with AI holds immense potential for advancing precision medicine. By analyzing an individual's genomic data, AI algorithms can identify specific genetic variations that influence disease susceptibility, treatment response, and prognosis. This enables the development of personalized treatment plans tailored to an individual's genetic profile. AI can also predict drug responses, identify potential adverse reactions, and guide the selection of targeted therapies, improving patient outcomes.

AI-powered analysis of NGS data can enhance diagnostic capabilities in genomics. It can aid in the identification of disease-causing mutations, genetic predispositions, and hereditary conditions. AI algorithms can interpret complex genomic data and provide accurate and rapid diagnoses for various genetic disorders and cancers. This expedites the diagnostic process, enabling timely interventions and improving patient management. Furthermore, NGS data combined with AI can significantly impact drug discovery and development. AI algorithms can analyze genomic data from patient populations, identify genetic signatures associated with specific diseases, and assist in target identification and validation. This facilitates the development of more targeted therapies and improves the success rate of drug discovery programs.

AI-driven genomics can provide valuable decision-support tools for clinicians. By integrating patient-specific genomic data with AI algorithms, clinicians can make more informed treatment decisions, select appropriate therapies, and monitor treatment response. This empowers healthcare professionals to deliver personalized care, optimize treatment strategies, and improve patient outcomes. Moreover, NGS generates vast amounts of data that need to be integrated with other biomedical datasets to fully understand the complexities of human biology and disease. AI techniques can facilitate the integration of diverse datasets, such as genomic, transcriptomic, proteomic, and clinical data, enabling comprehensive analysis and knowledge discovery. AI-powered tools can uncover novel associations, biomarkers, and therapeutic targets that would be challenging to identify using traditional methods.

The integration of AI and NGS has the potential to transform genomics research, diagnosis, and patient care. It enables the extraction of valuable insights from large-scale genomic datasets, facilitates personalized medicine approaches, accelerates drug discovery, and enhances clinical decision-making. As AI continues to advance and NGS technologies become more accessible, the opportunities in genomics will continue to expand, ushering in a new era of precision healthcare.

Segmental Insights

Software Segment Dominated

By component, the software segment held a dominant presence in the AI in genomics market in 2024 and is projected to expand rapidly in the market in the coming years. The software provides advanced facilities and state-of-the-art services for genomics research and development activities. It helps researchers set up complex genomic sequencing projects. It is primarily used to analyze and organize large data after experimentation. Companies develop GUI software to save time for researchers in learning the software algorithms and focus more on their research outcomes. It aids in end-to-end integration of all data types and workflows, reproducible and scalable sample processing, and efficient data management.

Machine Learning Segment Held the Largest Share

By technology, the machine learning segment held the largest share of the market in 2024. Machine learning (ML) plays a vital role in genomics research to analyze and interpret vast amounts of genetic data. It can be segregated into supervised and unsupervised manner to make predictions about genomics data. The increasing number and complexity of data sets necessitate researchers to adopt ML technology in genomics. In the medical field, ML can identify the primary kind of cancer from a liquid biopsy and predict the progression of a disease. Deep learning can be used to improve the function of gene editing tools such as CRISPR.

Computer Vision Segment: Significantly Growing

By technology, the computer vision segment is predicted to witness significant growth in the AI in genomics market over the forecast period. Computer vision can process huge amounts of data, reducing human errors and enhancing precision. It is most widely used for phenotyping, variant analysis, and genome editing. Hence, it helps to capture and interpret data by reviewing genetic images to check for abnormalities.

Genome Sequencing Segment Led in 2024

By functionality, the genome sequencing segment led the global market in 2024 and is anticipated to grow with the highest CAGR in the market during the studied years. Genome sequencing is one of the most essential techniques of genomics to identify the DNA sequence of an organism’s genome. The rising incidences and prevalence of genetic and rare disorders promote genome sequencing research. This technique is used to identify inherited disorders and track disease outbreaks. It can predict a disease outcome in an individual by analyzing its genetic information. The increasing investments and collaboration with tech-based companies potentiate the segment’s growth.

Drug Discovery & Development Segment Dominated

By application, the drug discovery & development segment registered its dominance over the global AI in genomics market in 2024. The rising prevalence of chronic disorders and growing research and development activities facilitate novel drug discovery research. AI helps to streamline the entire research workflow, leading to more effective drugs with fewer side effects. AI in genomics can identify potential targets and biomarkers responsible for disease progression. This helps scientists design drugs based on that particular target. AI in genomics helps in developing personalized medicines and enhancing patient stratification.

Precision Medicine Segment: Fastest-Growing

By application, the precision medicine segment is expected to grow at the fastest rate in the market during the forecast period. The growing demand for personalized medicines due to rapidly changing demographics fuels the segment’s growth. Precision medicines are developed based on patient’s genomic data. AI in genomics in precision medicine has the potential to improve patient healthcare. AI-powered pharmacogenomics aids in identifying genetic biomarkers, predicting treatment outcomes, and developing medicines based on the data.

Pharmaceutical & Biotechnology Companies Segment Dominated

By end-user, the pharmaceutical & biotechnology companies segment dominated the global AI in genomics market in 2024. The availability of skilled professionals and suitable capital investments favor the segment’s growth. The growing research and development activities and the increasing number of pharma and biotech start-ups promote the use of AI in genomics. The increasing investment in genomics start-ups by venture capitalists potentiates the adoption of AI.

Healthcare Providers Segment: Fastest-Growing

By end-user, the healthcare providers segment is estimated to show the fastest growth in the market over the forecast period. The expanded applications of genomics in the clinical field increase the demand for qualified genetic professionals or healthcare providers. The rising prevalence of chronic disorders potentiates the demand for genomics in disease diagnosis, prevention, and treatment. Healthcare providers can also assess the risk of future disease in patients.

Regional Insights

Increasing Investments Dominated North America

North America held a major share of the AI in genomics market in 2024. Technological advancements and the presence of key players drive the market. Favorable government support and increasing investments boost the market. Genome Canada invested a total of $105.5 million in genomics research in the year 2023-24. The Government of Canada announced an investment of $175.1 million over 7 years starting in 2024-25 to support the Canadian Genomics Strategy (CGS). The National Human Genome Research Institute (NHGRI) provides funding to conduct research based on genomics. The U.S. Food and Drug Administration published “Considerations for the Use of Artificial Intelligence to Support Regulatory Decision Making for Drug and Biological Products”, encouraging pharma and biotech companies to adopt AI in genomics.

Favorable Government Support Drives Asia-Pacific

Asia-Pacific is projected to host the fastest-growing market in the coming years. The rising prevalence of chronic disorders and increasing investments drive the market. The burgeoning pharmaceutical and biotechnology sector and the increasing number of start-ups augment market growth. Favorable government support promotes the market. The Chinese government has prioritized genomics and treats its genomic data as a strategic resource. India has around 9,000 biotech startups. The Indian government also supports AI and genomics research in India for individualized disease management for each patient. The rapidly expanding healthcare sector and growing awareness for early disease detection also contribute to the market.

Latest Announcement by Industry Leaders

Thierry Colin, Vice-President of Multimodal Research and Development at SOPHiA GENETICS, emphasized the significance of AI as the volume and complexity of available health data continue to increase and can assist in personalized diagnostics and treatment. The company is pleased with the findings of their study conducted in collaboration with UroCCR to help predict post-operative outcomes for those facing renal cell carcinoma (RCC).

Recent Developments

• In December 2024, Unite Genomics announced the launch of its direct-to-consumer platform that uses AI to analyze medical care patients and alert them to missing tests or treatment options. The platform could provide access to electronic health records and unify medical data for 90% of U.S. patients.
• In September 2023, researchers from Google DeepMind, an AI firm, announced the development of AI to identify changes in human DNA that might cause diseases. The development could accelerate diagnosis and help in the search for better treatments.

AI in Genomics Market Companies

• IBM
• NVIDIA Corporation
• Benevolent AI
• Verge Genomics
• MolecularMatch, Inc.
• SOPHiA GENETICS
• PrecisionLife Ltd.
• Lifebit
• FDNA, Inc.
• Empiric Logic
• Microsoft
• Deep Genomics
• Fabric Genomics Inc.
• Freenome Holdings, Inc.
• Cambridge Cancer Genomics
• Data4Cure Inc.
• Engine Biosciences Pte. Ltd
• Genoox Ltd.
• Diploid
• DNAnexus Inc.

Segments Covered in AI in Genomics Market Report

By Component

• Hardware
• Software
• Services

By Application

• Drug Discovery & Development
• Precision Medicine
• Diagnostics
• Others

By End User

• Pharmaceutical & Biotech Companies
• Healthcare Providers
• Research Centers
• Others

By Technology

• Machine Learning
  
  • Deep Learning
  • Supervised Learning
  • Reinforcement Learning
  • Unsupervised Learning
  • Other
• Other Technologies

By Functionality

• Genome Sequencing
• Gene Editing
• Others

By Region

• North America
  • U.S.
  • Canada
• Europe
  • U.K.
  • Germany
  • France
• Asia-Pacific
  • China
  • India
  • Japan
  • South Korea
  • Malaysia
  • Philippines
• Latin America
  • Brazil
  • Rest of Latin America
• Middle East & Africa (MEA)
  
  • GCC
  • North Africa
  • South Africa
  • Rest of the Middle East & Africa