Global AI in Omics Studies Market- Drivers
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.
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.
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
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)
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.
Thermo Fisher Scientific, Agilent Technologies, Illumina, BGI Genomics, Dassault Systèmes, Qiagen, Waters Corporation, GE Healthcare, Amazon Web Services, Inc., Bruker, Danaher
Global AI in Omics Studies Market - Restraints
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.
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.
Global AI in Omics Studies Market Report Snapshots
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