ARTIFICIAL INTELLIGENCE (AI) IN GENOMICS MARKET ANALYSIS

Market Challenges And Opportunities

Global Artificial Intelligence (AI) in Genomics Market Drivers:

• Increased Biomedical and Genomic Datasets to propel market growth: As biomedical research projects and large-scale collaborations are increasing rapidly, the volume of genomic data being generated is also growing, with roughly 2 to 40 billion gigabytes of data now being produced every year. Researchers are working to obtain useful information from such complex and big datasets so they can better understand human health and disease. AI tools are progressively helping researchers to process huge quantities of genome-sequence data to look for unseen patterns in DNA. Nevertheless, because AI algorithms habitually lack transparency, biases can slink in unobserved when such algorithms are employed in DNA data.
• Growing Adoption of Ai-Based Solutions to drive market growth: AI is also used for the discovery of genetic mutations in tumors with 3D imaging. For instance, an upcoming technology could detect glioma-type tumors that start in the glial cells of the brain or the spine using brain scans of a patient with high accuracy. Using technologies that are based on deep learning and neural networks, the treatment process can become highly enhanced, wherein doctors will not require tissue samples to be collected from a biopsy and can rule out the risks associated with surgery. AI & ML provide numerous possibilities with diagnosis automation. The digitization of health-related data and the speedy technology uptake are encouraging transformation and advances in the development and application of AI in healthcare.

Global Artificial Intelligence (AI) in Genomics Market Opportunities:

• Ever since the Human Genome Project began in 1990, genomics has demonstrated immense potential to  enhance and personalize healthcare. More recently,  the formation  of  DNA  biobanks,  which  are  collaborative  repositories  of  genome sequences, and the growth of direct-to-consumer genetics testing companies such as 23andMe have increased the explosion of genomic data. Top healthcare investors, such as Sequoia Capital and Deerfield Management, acknowledge that data has unlocked considerable commercial opportunities across healthcare verticals. In 2017, liquid biopsy company GRAIL raised US$ 914 million in its Series B round led by Smart Money VC ARCH Venture Partners and including Johnson & Johnson to continue product development and validation for its early-stage cancer detection blood tests.
• Sophia Genetics from Switzerland is another company making a mark in the data- driven medicine space. It already works with a half-dozen UK hospitals to combine data and bring AI-driven insights to cancer diagnostics, claiming to already diagnose hundreds of patients a day. Sophia Genetics has its AI firmly focused on cancer. EpiCombi.AI is another start-up that is a genomics signature-driven therapeutics spinout from Oxford University with its prime focus on overcoming epigenetic barriers in cancer complexity through the creation of AI-derived, network-acting multi-targeted drugs.

Global Artificial Intelligence (AI) in Genomics Market Restraints:

• Lack of Skilled Workforce & Infrastructure to hinder market growth: The healthcare system is expanding swiftly with technological advances, and with this expansion, there is a massive demand for its service. A skilled workforce and soaring value are still the unmet needs. Unstoppable forces such as changing treatment solutions,  lifestyle  choice,  aging  population,  and  overcoming  this management in the long-term healthcare system are still struggling. The adoption of AI in the field of healthcare is driving demand for skilled labor for healthcare services such as diagnostics, precision medicine, genomics, and patient engagement. As the population grows, there is a shortage of skilled labor, and gaps in services are key points for investments in the future.
• Ambiguous Regulatory Guidelines for Genomics Software to hamper the market growth: One of the main regulatory issues hampering the adoption of AI in healthcare is the archaic regulatory infrastructure. Although technological advances in healthcare have grown by leaps and bounds, the regulatory infrastructure has not kept pace. For example, AI-based software learns with increased use and becomes smarter. Most regulatory approvals are based on repeatability, but when software is self- learning, the results may and mostly will differ. While that is the strength of an AI system, regulations have to change. Another aspect is that AI, especially neural networks, is a black box. While it can be programmed, the user does really know how it works inside. This raises the problem of explicability. More than the regulatory challenge holding back AI, regulators are trying to keep up.