Artificial Intelligence in Genomics Market Report

Market Insights

The global artificial intelligence in genomics market is expected to grow from USD 0.29 billion in 2022 to USD 4.24 billion by 2029 at a CAGR of 46.7% during the forecast period.

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Artificial Intelligence (AI) in genomics involves the use of machine learning algorithms to analyze and interpret vast amounts of genetic data. With the rapid advancements in genomic sequencing technologies, AI can help researchers identify patterns, mutations, and variations in genetic data that can help in the diagnosis, treatment, and prevention of diseases.

AI can also predict the likelihood of certain genetic diseases and help in personalized medicine, tailoring treatments to an individual's specific genetic makeup. Additionally, AI can help in drug discovery and development, by identifying potential drug targets and predicting the efficacy of drug candidates. AI in genomics is revolutionizing the field, accelerating scientific discoveries, and improving patient outcomes.

Key Players

The major players operating in the global artificial intelligence in genomics market are:

• Microsoft Corporation
• IBM
• Deep Genomics
• NVIDIA
• Benevolentai
• Fabric Genomics
• Verge Genomics
• Freenome Holdings
• Molecularmatch
• Cambridge Cancer Genomics
• Sophia Genetics
• Datacure
• PrecisionLife
• Genoox
• Lifebit
• Diploid
• FDNA
• Dnanexus
• Empiric Logic
• Engine Biosciences

Market Dynamics

The market growth is being propelled by several factors such as the desire to regulate drug development and discovery costs and time, rising public and private investments in AI technology for genomics, and the adoption of AI solutions in precision medicine. Nevertheless, the lack of a competent workforce specialized in AI and the ambiguous regulatory framework for medical software are predicted to hinder the market's growth in the projected period.

Driver: Need to make drug discovery and development process time- and cost-efficient

The drug discovery and development process is a long and costly process that involves multiple stages of research and development, including target identification, drug design, preclinical testing, and clinical trials. The use of AI in genomics has the potential to make this process more time- and cost-efficient by accelerating the pace of research and improving the accuracy of drug discovery.

One way that AI can speed up the drug discovery process is by predicting the potential efficacy and safety of new drugs. AI models can analyze large amounts of genomic data to identify potential drug targets and predict how different compounds might interact with these targets. This can help to prioritize compounds for further testing, reducing the time and cost associated with preclinical and clinical trials.

Another way that AI can make drug discovery more efficient is by helping to identify patient populations that are most likely to benefit from a particular treatment. By analyzing genomic data from large patient cohorts, AI models can identify genetic markers that are associated with a specific disease or condition, and use this information to predict which patients are most likely to respond to a particular drug.

This can help to streamline clinical trials by identifying patients who are most likely to benefit from a particular treatment, reducing the time and cost associated with recruiting and enrolling patients.

Finally, AI can help to optimize the drug development process by predicting potential side effects and toxicity of new drugs. By analyzing genomic data and using machine learning algorithms, AI models can predict the potential toxicity of new drugs and help to identify potential safety issues before clinical trials begin. This can help to reduce the risk of adverse events during clinical trials and improve patient safety.

Opportunity: Development of human-aware AI systems

The development of human-aware AI systems in genomics involves the creation of artificial intelligence (AI) tools that can understand and work collaboratively with human experts to analyze genomic data. The goal is to create AI systems that can assist researchers in making sense of the vast amounts of genomic data generated by modern sequencing technologies.

One area where human-aware AI systems are particularly important is in the interpretation of genomic variants. With the increasing use of whole-genome sequencing and other genomic technologies, there is a growing need for tools that can help clinicians and researchers interpret the meaning of genetic variants.

Human-aware AI systems can provide valuable assistance in this task by analyzing large amounts of genomic data and identifying potentially relevant variants. They can also help to prioritize variants based on their predicted functional impact and their relevance to specific diseases or conditions.

Another area where human-aware AI systems are being developed is in the analysis of gene expression data. This involves the use of machine learning algorithms to identify patterns in gene expression data that are associated with specific diseases or conditions. Human-aware AI systems can help to refine these analyses by taking into account the expertise and insights of human researchers.

For example, an AI system might identify a set of genes that are upregulated in a particular disease, but a human expert might be able to recognize that these genes are also associated with a particular cellular process or pathway.

Restraint: Dearth of AI workforce and ambiguous regulatory guidelines

To develop, manage, and implement AI systems, companies need a workforce with specific skill sets due to the complexity of these systems. For example, personnel involved in AI systems must have knowledge of cognitive computing, machine learning and intelligence, deep learning, and image recognition. Integrating AI solutions into existing systems is also challenging and requires extensive data processing to mimic human brain behavior. Any minor mistake in this process can result in system failure or negatively impact the intended outcome.

Challenge: Lack of curated genomics data

Data plays a crucial role in training and developing a complete and robust AI system. In the past, datasets were primarily structured and manually entered. However, the increasing digital presence and technology usage, such as the Internet of Things (IoT) in healthcare and life science, have led to the accumulation of large volumes of unstructured data in the form of text, voice, or images.

Segment Analysis

By End User Type

Pharmaceutical and biotechnology companies to account for the largest share of the AI in genomics market during the forecast period. The AI in genomics market is divided into several segments based on the end user, including pharmaceutical and biotechnology companies, healthcare providers, research centers, academic institutes, government organizations, and other end users.

In 2022, pharmaceutical and biotechnology companies held the largest share of the market as end users. The growth of this market segment can be attributed to the increasing need for solutions that can reduce the time and cost associated with drug development.

Regional Analysis

North America to account for the largest share of the global AI in genomics market over the forecast period. The North American region held the largest share of the AI in genomics market in 2022, followed by Europe. The significant market share in North America can be attributed to the increased research funding and government initiatives in the US aimed at promoting precision medicine.

The region also houses some of the world's largest and well-resourced research institutions and biotechnology companies. These entities are heavily investing in the advancement of AI-based solutions for genomics, leading to the creation of new software and tools for analyzing genomic data. As a result, the AI in genomics market in North America is experiencing significant growth.

Research Methodology

This report offers high-quality insights and is the outcome of a detailed research methodology comprising extensive secondary research, rigorous primary interviews with industry stakeholders, and validation and triangulation with Stratview Research’s internal database and statistical tools.

More than 1,000 authenticated secondary sources, such as company annual reports, fact books, press releases, journals, investor presentations, white papers, patents, and articles, have been leveraged to gather the data.

We conducted more than 15 detailed primary interviews with the market players across the value chain in all four regions and industry experts to obtain both qualitative and quantitative insights.

Report Features

This report provides market intelligence in the most comprehensive way. The report structure has been kept such that it offers maximum business value. It provides critical insights into the market dynamics and will enable strategic decision making for the existing market players as well as those willing to enter the market. The following are the key features of the report:

• Market structure: Overview, industry life cycle analysis, supply chain analysis.
• Market Environment analysis: Growth drivers and constraints, Porter’s five forces analysis, SWOT analysis.
• Market trend and forecast analysis.
• Market segment trend and forecast.
• Competitive landscape and dynamics: Market share, Product Portfolio, New Product Launches, etc.
• Attractive market segments and associated growth opportunities.
• Emerging trends.
• Strategic growth opportunities for the existing and new players.
• Key success factors.

Customization Options

With this detailed report, Stratview Research offers one of the following free customization options to our respectable clients:

Company Profiling

• Detailed profiling of additional market players (up to three players)
• SWOT analysis of key players (up to three players)

Competitive Benchmarking

• Benchmarking of key players on the following parameters: product portfolio, geographical reach, regional presence, and strategic alliances

Custom Research: Stratview research offers custom research services across the sectors. In case of any custom research requirement related to market assessment, competitive benchmarking, sourcing and procurement, target screening, and others, please send your inquiry to sales@Stratviewresearch.com

Recent Developments

• In June 2022, Ultima Genomics, Inc. unveiled a broad collaboration with NVIDIA that leverages AI-accelerated analysis across various stages of the DNA sequencing and analysis process.
• In March 2022, IBM and Genomics England entered into a new partnership to transform patient care. IBM is selected as a strategic technology partner for Genomics England over 18 months to support the rapid expansion, and enable consumption of cloud services and AI capabilities.