GLOBAL AI IN OMICS STUDIES MARKET SIZE AND SHARE ANALYSIS - GROWTH TRENDS AND FORECASTS (2023 - 2030)

The global AI in omics studies market size is expected to reach US$ 4,515.4 Mn by 2030, from US$ 639.8 Mn in 2023, exhibiting a compound annual growth rate (CAGR) of 32.2% during the forecast period.

Artificial intelligence (AI) is being leveraged across various fields of science to revolutionize research and discovery. In genomics and molecular research, AI is playing a pivotal role by assisting researchers in the analysis of large and complex omics datasets. There are various AI-based products that are used for omics data analysis.

One of the most commonly used products are gene expression analysis tools that use machine learning algorithms to identify patterns in transcriptomics data and deduce biological insights. These tools allow researchers to perform functional analysis, biomarker detection, and gene network modeling much more efficiently compared to traditional statistical methods. Other useful products include genomic and proteomic sequencing tools that employ deep learning for base calling, variant calling, and peptide identification from omics datasets. This has significantly boosted sequencing throughput and data accuracy.

While AI omics tools have clear advantages like speed, automation, and the ability to discover subtle patterns, there are still some challenges. The models used by these tools function as 'black-boxes' and do not provide explanations for their results. This can reduce the reliability and reproducibility of findings. Also, the performance of AI models depends on the quantity and quality of training data, limiting their usage for rare diseases. The standardization of datasets and models across platforms is another issue. 

Global AI in Omics Studies Market- Regional Insights

• North America is expected to be the largest market for AI in omics studies during the forecast period, accounting for over 40% of the market share in 2023. The region is home to leading AI and healthcare companies that are at the forefront of developing and applying AI technologies for omics studies. Countries like the U.S. have a highly advanced healthcare research infrastructure along with strong government funding for biomedical research. Several major universities and research institutions in the U.S. are actively exploring the potential of AI and machine learning methods to analyze genomics, proteomics, and other types of omics data. These initiatives are driving the adoption of AI solutions across clinical and research applications in the region.

Moreover, North America has a large pool of AI and data science experts who are working on collaborative projects between academia and industry. The region also has a receptive market environment and favorable regulations to support commercialization of AI-based diagnostic and research tools. Leading pharmaceutical and life sciences companies with significant research and development investments are using AI to accelerate drug discovery from omics data. These factors have made North America the dominant early adopter of AI-powered solutions and services for omics studies.

• Asia Pacific market is expected to be the second-largest market for AI in omics studies market, accounting for over 25% of the market share in 2023. Countries like China, India, Japan, and South Korea are rapidly investing in healthcare technology and promoting initiatives aimed at precision and personalized medicine. With a growing middle-class population seeking advanced healthcare options, the Asia Pacific region presents substantial opportunities. The governments are also introducing supportive policies and funding for collaborative research involving AI and other cutting-edge technologies. This is attracting major international players to establish R&D centers and partnerships in the region. Furthermore, Asia Pacific countries have a large talent pool of AI and computational experts, which helps address skills shortages and brings down costs for companies. These favorable conditions are strengthening the Asia Pacific position as the fastest-growing regional market for AI in omics studies.
• Europe market is expected to be the fastest-growing market for AI in omics studies market, with a share of 19% during the forecast period. The growth of the market in Europe is due to the increasing genomic research and development in the region.

Figure 1. Global AI in Omics Studies Market Share (%), By Region, 2023

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Analyst View: The AI in omics studies market is growing steadily and is expected to witness significant growth over the forecast period. The primary driver for the AI adoption in omics studies is its ability to analyze large and complex omics datasets. AI tools help researchers identify patterns, predictive biomarkers and gain novel biological insights from omics data more efficiently. North America dominated the market in 2021 due to heavy investments by pharmaceutical companies and presence of leading AI players in the region. Asia Pacific is projected to be the fastest-growing market during the forecast period owing to increasing R&D investments by China and India in omics and AI technologies.

However, the lack of skilled workforce to develop and deploy AI solutions remains a major restraint for wider adoption. Data integration and extracting meaningful insights from multi-omics datasets also pose challenges. Nevertheless, the growing partnership between AI and omics companies presents opportunities for the development of advanced analytics platforms. New startups are also offering cloud-based AI solutions to researchers, which is expanding the addressable market. The future outlook remains positive, with increasing acceptance of AI as an indispensable tool for accelerating omics research.

Global AI in Omics Studies Market- Drivers

• Growing genetic and genomic data and increasing investments: The rapid growth of genetic and genomic data available through large-scale sequencing projects is fueling the increased adoption of artificial intelligence in omics studies. Due to plummeting DNA sequencing costs over the past decade, ability to analyze the building blocks of life has accelerated exponentially. Several governments and non-profit organizations worldwide have launched ambitious initiatives to collect genomic data from millions of volunteers to advance biomedical research. For example, the U.K. Biobank, a large long-term biobank study in the U.K, has genetic data from over 500,000 individuals, which is freely available to approved researchers globally.

As petabytes of genetic information pour in from these public efforts, there is an urgent need to analyze this deluge of complex data. This is driving significant investments in AI and machine learning to derive meaningful insights from omics datasets. Pharmaceutical companies and academic research centers are increasingly utilizing deep learning models to speed up drug discovery by better understanding genotype-phenotype correlations. Startups are also emerging that focus on developing AI tools tailored for precision medicine and disease prediction applications using genomic data.

• Personalized medicine and precision diagnosis: Personalized medicine and precision diagnosis are significantly driving the adoption of artificial intelligence in omics studies. With the advancement of technologies like genomics, epigenomics and proteomics, a huge amount of multidimensional omics data is being generated from individual patients. Analyzing this complex omics data manually to understand each patient's disease condition and find customized treatment plans is an almost impossible task. This is where artificial intelligence is playing a pivotal role by helping researchers leverage large healthcare datasets and clinical information to develop predictive models for accurate diagnosis and personalized therapies.

AI techniques like machine learning and deep learning are being extensively used for applications such as gene sequencing, pharmacogenomics, biomarker development, and clinical decision support systems. For example, AI algorithms are analyzing genomic variations, RNA transcripts and protein expressions in a patient's biological sample to predict disease predisposition, diagnose conditions, track disease progression, and identify potential drug targets or therapies that may work best for that individual. Some AI systems can even monitor treatment responses and flag adverse events in near real-time by integrating omics profiles with electronic health records. This is allowing healthcare providers to deliver more effective precision care tailored to the unique biological characteristics of each patient.

• Technological advancements in AI and automation: Advances in artificial intelligence and machine learning are revolutionizing genomics and enabling more extensive analysis of large and complex omics datasets. By leveraging vast amounts of genomic and molecular data, AI technologies can uncover patterns and insights that would be nearly impossible for researchers to discover on their own. For example, algorithms developed by the National Institutes of Health can now analyze a person's full genome in under a second, identifying potentially disease-causing mutations over 200 times faster than traditional methods. As datasets in fields like proteomics and transcriptomics continue to grow exponentially due to advancements in high-throughput sequencing and data collection tools, AI will become increasingly critical to help researchers make sense of this flood of data.

The application of AI is also helping to automate many routine genomic workflows and tasks. Deep learning models have been developed to automatically interpret genomic variant calls with 99% accuracy, saving researchers immense time previously spent on manual validation and evaluation. Other AI tools can now automate complex processes like CRISPR genome editing design in a matter of hours versus months for human experts. As genomics studies generate petabytes of new data each year, automated systems powered by AI will be necessary to help analyze this deluge of information in a timely, cost-effective manner. This rise of AI-driven automation is reducing the workload on researchers, freeing them to focus on more innovative scientific questions.

Global AI in Omics Studies Market- Opportunities

• Scope for AI in drug discovery and vaccine development: AI has tremendous scope for accelerating drug discovery and vaccine development processes in the AI in omics studies market. With AI and machine learning algorithms, researchers can now analyze huge amounts of omics data like genomics, proteomics, and metabolomics at an unprecedented scale and speed. This big data analysis helps identify disease subtypes and discover new drug targets and biomarkers. It also aids in clinical trial recruitment and monitoring.

For instance, AI is being used to sift through millions of chemical compounds to predict those most likely to effectively target proteins associated with a disease. This saves precious time compared to traditional trial and error methods. Pharmaceutical companies are also leveraging AI to improve strategies for repurposing existing drugs for new therapies. By revealing similarities between diseases or conditions at the molecular level, AI can uncover unexpected ways to deploy approved treatments for other illnesses.

As the COVID-19 pandemic showed, developing safe and effective vaccines typically takes years through conventional research. However, AI algorithms can now analyze coronavirus genomes sequenced from various geographic locations and predict how it may mutate over time. This helps vaccine designers stay ahead of new variants. Several AI tools are also expediting vaccine candidate screening and selection processes. For example, over 50 potential SARS-CoV-2 vaccine candidates were tested and two were selected for clinical trials just two months after the virus genomic sequence was disclosed, according to the World Health Organization (WHO)

• Rising healthcare expenditure: One of the significant factors influencing the growth rate of the global AI in omics studies market is the growing healthcare expenditure, which helps in improving its infrastructure. For instance, according to the International Health Care System of the U.S., in June 2020, U.S. government organizations aim to improve the healthcare infrastructure by increasing funding, setting legislation, and national strategies, and cofounding and setting basic requirements and regulations for the Medicaid program. Similarly, in November 2022, the Canadian Institute for Health Information reported that the total healthcare expenditure in Canada was US$ 331 billion in 2022, or US$ 8,563 per Canadian, while health expenditure represented 12.2% of Canada's gross domestic product (GDP) in 2022, following a high of 13.8% in 2020.
• Growth in emerging markets: The emerging markets in developing countries present a huge potential for growth in the AI in omics studies market. These nations are experiencing rapid economic development and witnessing increased investments in healthcare and life sciences research. With rising incomes, people in these regions now have greater access to sophisticated diagnostic technologies and are more open to novel applications of AI in medicine.

Several factors make the emerging market conditions conducive to the wide adoption of AI tools in omics research. Firstly, in emerging nations, the population is often younger and has a greater prevalence of illness. This emphasizes the need for precision diagnostics and therapeutics. Secondly, governments are investing heavily in building biotech infrastructure to promote national priorities around bioprospecting and drug discovery. For instance, India's National Biopharma Mission aims to foster R&D collaborations between academia and industry. Thirdly, reducing the costs of genomic sequencing and data storage is making AI-driven multi-omics analysis feasible even for low-resource public health programs and hospitals in remote areas.

Global AI in Omics Studies Market- Trends

• Adoption of cloud-based solutions and services: The adoption of cloud-based solutions and services has rapidly increased over the past few years. More businesses are migrating their infrastructure and applications to the cloud as it provides tremendous flexibility, scalability, and reduced costs compared to maintaining on-premise servers. The cloud allows companies to avoid large upfront capital investments in hardware and data centers while paying only for the resources they use. This pay-as-you-go model has proved very appealing, especially for cash-strapped startups and small business According to data from the U.K. Government Department of Digital, Culture, Media, and Sport, over 90% of U.K. businesses now use some form of cloud computing, up from around 75% in 2020.

As businesses embrace cloud-based tools and remote work enabled by technologies like cloud-hosted virtual meeting solutions, the demand for reliable and secure cloud infrastructure has also increased tremendously. To meet this demand, major cloud service providers like Amazon Web Services, Microsoft Azure and Google Cloud have been significantly expanding their data center presence globally. For example, Amazon Web ServicesCloud computing company,announced plans in late 2021 to invest US$5 billion in building 15 new data center regions worldwide by 2026. This rapid data center expansion allows cloud providers to reduce latency and better support customers across the world, attracting even more businesses to their platforms.

The growing adoption of cloud-based solutions by enterprises is creating a huge market opportunity for independent software vendors and cloud technology startups. More companies are developing cloud-native applications and workflows that are easy to deploy, manage and update in the cloud. This has driven strong investment and innovation in areas like serverless computing, containers, cloud storage, collaboration tools, cybersecurity, AI/ML, and more. The pandemic has accelerated this shift towards cloud-enabled digital transformation across all industries.

• Integration of AI with IoT and blockchain: The convergence of emerging technologies like AI, and blockchain is driving greater adoption of cloud-based solutions and services across industries. As more physical assets get connected to the internet and generate vast amounts of data, there is an increased need for computing power and data storage. Cloud infrastructure allows organizations to harness real-time insights from IoT data through AI and analytics tools hosted on the cloud. For example, predictive maintenance of industrial equipment using IoT sensor data and AI models in the cloud helps manufacturing companies improve uptime and reduce downtime costs considerably.

The integration of AI and IoT is also opening new opportunities through hyper-automation. Real-time data from connected devices can power automated decision making and workflows. Blockchain's ability to securely share data across organizational silos further enhances the potential of AI and IoT collaborations. When devices, systems, and trading partners can reliably transact, interact, and validate transactions in an automated manner, it drives efficiencies. For instance, blockchain and AI-powered smart contracts are streamlining supply chain processes for automakers like Ford by digitally tracking parts from suppliers. This is reducing paperwork and improving visibility into inventory levels.

Global AI in Omics Studies Market - Restraints

• Shortage of skilled workforce: The shortage of skilled workforce is significantly restraining the growth of adoption of cloud-based solutions and services across various sectors. With more and more businesses recognizing the strategic and operational benefits of cloud computing, the demand for cloud skills and capabilities is surging exponentially. However, the supply of trained and experienced cloud professionals is struggling to keep up with this high demand.

Several factors are contributing to the growing skills gap in cloud technologies. Traditional IT training programs are still catching up with the pace of innovation in the cloud domain. Cloud models require new skills around distributed systems, networking, serverless architecture, containerization, machine learning, etc. Re-skilling the existing workforce with these new age technologies is also a challenge. Many educational institutes have yet to design courses that can equip students with the relevant cloud skills. This is hampering the talent pipeline for cloud jobs.

At the same time, fast growing cloud players themselves are facing difficulties in recruiting sufficiently trained staff. According to a 2022 report by the World Economic Forum, over half of business leaders surveyed said they are facing significant talent shortfalls in areas like data science, cloud computing, and cybersecurity. This skills shortage acts as a constraint for companies to fully leverage cloud capabilities and scale their digital transformation. It reduces their agility and speed of innovation. Ultimately, it has a dampening effect on the pace at which organizations are willing to adopt cloud models and migrate their IT infrastructure and workloads to the cloud.

• High setup costs and lack of infrastructure: The adoption of cloud-based solutions requires significant investment in upgrading existing infrastructure and networks to support cloud technologies. For many organizations, especially small and medium-sized businesses, the upfront capital expenditure required to implement cloud migration or to build new cloud-enabled infrastructure can be prohibitively high. Setting up cloud capabilities such as virtual servers, storage, networking equipment, and security features entails non-trivial expenditures. This high barrier to entry prevents many prospective customers from transitioning to the cloud in the first place. Given their limited budgets, such organizations are deterred by the high setup and migration costs associated with cloud adoption.

Moreover, in developing countries and remote areas, lack of access to high-speed internet continues to pose challenges. Reliable and speedy network connectivity is essential for businesses and individuals to fully leverage the advantages of cloud services. However, inadequate broadband penetration in parts of Africa and Asia is a hindrance. For example, according to the latest data from the International Telecommunication Union, approximately 31% of households in India still lack internet access as of 2021. The inability to ensure seamless data transfer poses difficulties for organizations in these regions to move their workloads and processes fully onto the cloud. Infrastructure deficits negatively impact user experience and undermine confidence in cloud solutions.

Figure 2. Global AI in Omics Studies Market Share (%), By Offering, 2023

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Global AI in Omics Studies Market- Recent Developments

Product and Technology Launch

• On September 20, 2023, DNAstack, a software company, launched Omics AI, a revolutionary new software suite for omics and health research. Omics AI makes scientific discoveries faster and more powerful by enabling insights across federated networks of data in compliance with open standards provided by the Global Alliance for Genomics & Health (GA4GH). The system is being used by world leading pharmaceutical companies, hospitals, universities, patient advocacy groups, funders, sequencing facilities, government agencies, and consortiums to grow collaborative networks in diverse areas of research.
• On April 14, 2023, Bimodal, a biotechnology company, launched its new duet multiomics solution, which it says reveals the combinatorial power of genetic and epigenetic information from a single low-volume sample. The duet multiomics solution is the world’s first single-base-resolution sequencing technology that enables the simultaneous phased reading of genetic and epigenetic information in a single sample with one workflow using any sequencer.
• In November 2022, Amazon Web Services, Inc., a cloud computing company, launched Amazon Omics for precision medicine. Amazon omics is a cloud-based platform that provides security, scale, and the processing power needed for genomic data storage and analysis, eliminating the need for specialized infrastructure and workflows.

Acquisition and Collaboration

• On November 6, 2023, OWKIN, a biotechnology company and 10x Genomics, Inc., a biotechnology company, announced they have entered into an agreement to add 10x Genomics spatial omics and single-cell technologies to work in tumor analysis for therapeutic discovery.
• On September 4, 2023, Intelligent OMICS Ltd, a biotechnology company, has agreed an AI-driven research collaboration with Janssen Global Services, LLC, pharmaceutical company, The collaboration with Janssen Global Services, LLC, is evaluate novel biological targets for the treatment of haematological cancers.

Top Companies in Global AI in Omics Studies Market

• Thermo Fisher Scientific
• Agilent Technologies
• Illumina
• BGI Genomics
• Dassault Systèmes
• Qiagen
• Waters Corporation
• GE Healthcare
• Amazon Web Services, Inc
• Bruker
• Danaher

Definition: Artificial intelligence (AI) is a powerful approach for solving complex problems in the processing, analysis, and interpretation of omics data, as well as the integration of multi-omics and clinical data. In recent years, AI has enabled remarkable breakthroughs across diverse biomedical fields, such as genomic variant interpretation, protein structure prediction, disease diagnosis, and drug discovery.