Visiongain has published a new report entitled Artificial Intelligence (AI) in Genomics Market Report 2023-2033: Forecasts by Offering (Software, Services), by Technology (Machine Learning, Computer Vision), by Delivery Mode (On-premises, Cloud & Web-Based Mode), by Functionality (Genome Sequencing, Gene Editing, Clinical Workflows, Predictive Genetic Testing & Preventive Medicine), by Application (Diagnostics, Drug Discovery & Development, Precision Medicine, Agriculture & Animal Research, Other), by End-user (Pharmaceutical & Biotech Companies, Healthcare Providers, Research Centres, Academic Institutes & Government Organisations, Others) AND Regional and Leading National Market Analysis PLUS Analysis of Leading Companies AND COVID-19 Impact and Recovery Pattern Analysis.

The AI in Genomics market was valued at US$608.9 million in 2022 and is projected to grow at a CAGR of 38.4% during the forecast period 2023-2033.

Growing demand for precision medicine
The integration of AI in genomics offers several advantages. It enables researchers and clinicians to process and interpret large-scale genomic data more efficiently, leading to improved diagnostics, drug discovery, and treatment decision-making. AI algorithms can identify disease-associated genetic markers, predict disease risk, and develop personalized treatment plans based on a patient's genetic profile. This strategy enables more targeted and efficient therapies, minimising negative effects and improving patient outcomes. Further, this reinforces the medical companies to invest in AI drug discovery technologies heavily. For instance, in July 2021, Deep Genomics obtained $180m in a Series C funding round to advance its artificial intelligence (AI)-discovered drug programs for genetic diseases into the clinic.

Additionally, growing awareness among healthcare professionals and the general public about the potential benefits of precision medicine has increased the adoption of their unique genetic makeup and other beneficial properties. Additionally, the growing prevalence of chronic illnesses, including diabetes, cardiovascular disease, and cancer, encourages using AI in genomics. Precision medicine offers the potential to improve disease prevention, early detection, and targeted treatments, which can significantly impact patient outcomes and reduce the burden of chronic diseases. As a result, there has been a sharp increase in demand for AI in genomics. Both research institutions and healthcare providers are increasingly adopting AI technologies to leverage the vast amounts of genomic data generated by next-generation sequencing and other high-throughput techniques. The market for AI in genomics is expected to continue expanding as precision medicine becomes more widely adopted and advancements in AI and genomics technologies continue to enhance.

How has COVID-19 had a positive impact on AI in the genomics Market?
The industry for AI in genomics has benefited from the COVID-19 pandemic. Pharmaceutical companies raised their investments in COVID-19 vaccine R&D due to the pandemic. In the research of COVID-19, the use of AI in genomics was crucial for determining the origin, structure, and behaviour of SARS-CoV-2. This information was used to create new vaccines or pharmaceutical treatments. Market participants created AI technologies to quicken research on COVID-19 to suit the market's expanding needs in light of the pandemic. For instance, to assist the medical community in better tracking, treating, and testing COVID-19, the NVIDIA Corporation (U.S.) enhanced the NVIDIA Clara Healthcare Platform capabilities in May 2020.

During its early stages, the COVID-19 pandemic considerably influenced the adoption of artificial intelligence (AI) in the pharmaceutical industry. The use of AI in the pharmaceutical business was impacted by strict lockdowns, government regulations, and postponed or interrupted research and development (R&D) efforts with a greater focus on COVID-19 trials. However, as AI technology has become a crucial non-medical intervention, it has a rich market for developing next-generation epidemic preparedness and creating novel medications. For instance, according to a March 2022 IJMS (International Journal of Molecular Sciences) article, AI was a key factor in COVID-19 De Novo medication design. The identification and development of novel molecules during the pandemic was aided by a variety of AI techniques, including structure-based AI methods for small compounds, ligand-based AI methods for small molecules, and AI approaches for vaccine formulation. During the pandemic, this tendency is anticipated to affect the market favourably and will persist in the years to come.

How will this Report Benefit you?
Visiongain’s 316-page report provides 128 tables and 191 charts/graphs. Our new study is suitable for anyone requiring commercial, in-depth analyses for the AI in Genomics market, along with detailed segment analysis in the market. Our new study will help you evaluate the overall global and regional market for AI in Genomics. Get financial analysis of the overall market and different segments including type, process, upstream, downstream, and company size and capture higher market share. We believe that there are strong opportunities in this fast-growing AI in Genomics market. See how to use the existing and upcoming opportunities in this market to gain revenue benefits in the near future. Moreover, the report will help you to improve your strategic decision-making, allowing you to frame growth strategies, reinforce the analysis of other market players, and maximise the productivity of the company.

What are the Current Market Drivers?

Growing Attention on Speeding up Drug Discovery and Diagnostics
New medication discovery and diagnostic innovations are time-consuming, expensive, high-risk operations have slim odds of success. The discovery of a new medication takes 10 to 15 years and costs an average between US$1 billion to US$2 billion. The likelihood of failure and turnaround time are, however, reduced by elements including a lack of clinical effectiveness, a lack of commercial needs, and a lack of medicinal characteristics. As a result, pharmaceutical companies are using AI to find new drugs. Finding new medications is crucial, given the COVID-19 pandemic. Many biotech and pharmaceutical businesses are using AI to boost accuracy and expedite drug discovery. The method of finding new drugs for difficult conditions like cancer and Alzheimer's has also been transformed through use of AI. As a result, applying AI helps with medication discovery. The primary motivator for pharmaceutical businesses to engage in AI is the potential time savings from integrating AI with genomics. In addition to being used for patient matching, target identification, and forecasting, it can handle a sizable amount of genetic data. As a result, by facilitating data-driven decision-making, the application of AI in drug research can result in the development of safe and effective medications.

Genomics Researchers Are Increasingly Using Cloud-Based AI Solutions
Compared to the on-premises industry, the cloud-based AI in the genomics market is anticipated to grow more quickly. The ability of cloud-based solutions to access, analyze, and store data and combine it all in one place is the main driver of this rise. Furthermore, as genomics databases grow in size, on-premises solutions become impractical. As more people become aware of genome sequencing, there will be a rise in human genome sequencing research carried out during the projected period. The raw data produced from a single human sequence is roughly 200 Gigabytes. This would increase the difficulty of using on-premises AI technologies, thereby increasing the demand for cloud deployment.

Where are the Market Opportunities?

The Emergence of human-aware AI Systems
Human-aware AI systems are designed to understand, interact with, and adapt to human behaviour, preferences, and emotions. These systems aim to enhance the collaboration and co-operation between humans and AI technologies, leading to more effective and intuitive interactions. The genomic field focuses on analysing and interpreting large-scale genomic data to understand the genetic basis of diseases, develop personalized treatments, and advance genomic research. Integrating human-aware AI systems in genomics can greatly enhance the capabilities and outcomes in this field. These systems can enhance contextual understanding, enable personalized medicine, address ethical considerations, promote collaboration, and engage patients in the genomics field. Human-aware AI systems can adapt to individual patient's preferences, needs, and genetic variations. These systems can help identify personalized treatment options, predict disease risks, and select targeted therapies by considering patient-specific factors. In addition, developing human-aware AI systems can help address ethical considerations in genomics. It ensures transparent and explainable AI models, which is important for dealing with sensitive genetic information. They can also help maintain compliance with data protection laws and privacy standards. This can increase the trust of patients and researchers.

Furthermore, human-aware AI systems can improve patient engagement and education by providing personalized and understandable information about genomic data, genetic conditions, and potential risks. These systems can empower patients to make informed decisions about their health, participate in research initiatives, and contribute to advancing genomics. However, developing and deploying human-aware AI systems raises ethical considerations and challenges. Ensuring privacy, consent, and fairness in collecting and using human data is essential. Thus, the development of human-aware AI systems provides the opportunity for the growth of AI in the genomics market.

Competitive Landscape
The major players operating in the AI in Genomics market are IBM, Microsoft Corporation, NVIDIA Corporation, Deep Genomics Incorporated, Fabric Genomics, Inc., Data4cure, Inc., Predictive Oncology Inc., Emedgene Technologies Ltd, Congenica Ltd., Sophia Genetics SA, Illumina Inc., BenevolentAI Limited., Freenome Holdings, Inc., and Verge Genomics, These major players operating in this market have adopted various strategies comprising M&A, investment in R&D, collaborations, partnerships, regional business expansion, and new product launches.

Recent Developments
• On March 2023, SOPHiA GENETICS partnered with QIAGEN Forge to combine strengths in next-generation sequencing. This will pair QIAseq reagent technology with the SOPHiA DDM™ platform to enhance tumor analysis through next-generation sequencing (NGS). In addition, QIAGEN's Partnership Program enables more customers to benefit from the high quality of its NGS preparation kits by utilizing a broader range of analytics solutions to meet their unique analysis and interpretation requirements.
• On March 2023, to advance the creation of the cancer medication platform PEDAL, Predictive Oncology teamed up with Cancer Research Horizons. This will speed up the development of cancer medicines using substances developed by Cancer Research Horizons in collaboration with the CRUK network and originating from their global network.

Notes for Editors
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