Market Challenges And Opportunities

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.

Companies covered:

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

Growth Drivers:

Growing genetic/genomic data and increasing investments
Personalized medicine and precision diagnosis
Technological advancements in AI and automation

Restraints & Challenges:

Shortage of skilled workforce
High setup costs and lack of infrastructure

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.

Restraints & Challenges: