AI In Genomics Market

Updated on : May 11, 2023

The global AI in genomics market in terms of revenue was estimated to be worth $0.5 billion in 2023 and is poised to reach $2.0 billion by 2028, growing at a CAGR of 32.3% from 2023 to 2028. The need to control drug development and discovery costs and time, increasing public and private investments in AI in genomics, and the adoption of AI solutions in precision medicine are driving the growth of this market. The market growth is primarily driven by the need to accelerate processes and timeline and reduce drug development & discovery costs and increasing partnerships and collaborations among players and growing investments in AI in genomics. Additionally, factors such as improving computing power and declining hardware cost, rising adoption of AI in precision medicine, and explosion in bioinformatics data and genomic datasets are also contributing to the market growth.

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AI in Genomics Market Dynamics

Driver: Need to accelerate processes and timeline and reduce drug development and discovery costs

Drug discovery is an expensive and lengthy process, which creates a need for alternative tools to discover new drugs. Drug discovery and development are commonly conducted through in vivo and in vitro methods, which are costly and time-consuming. Furthermore, it takes ~10 years on average for a new drug to enter the market and costs ~USD 2.6 billion.

Only one out of 5,000–10,000 compounds is approved as a potential drug for a particular condition. Most drug candidates selected in the discovery phase fail in the late stages of development due to toxicity or other pharmacokinetic characteristics. Machine learning technology can help at this stage by predicting the outcome of a drug compound in the discovery phase and eliminating compounds without potential in the early discovery phase itself. This will significantly cut downtime and expenses in identifying potential drug candidates.

The potential for time and cost reductions in this process has drawn significant stakeholder attention and resulted in numerous investigative projects. For instance, in November 2020, Deep Genomics and BioMarin announced a collaboration to discover and develop oligonucleotide drug candidates for four rare diseases, combining BioMarin’s extensive rare disease expertise with Deep Genomics’ AI Workbench platform. With that, AI in genomics for drug discovery has the potential to significantly accelerate the drug development process, reduce costs, and improve patient outcomes by enabling the development of more targeted and effective drugs.

Restraint: Lack of skilled AI workforce and ambiguous regulatory guidelines for medical software

An AI is a complex system; to develop, manage, and implement AI systems, companies require a workforce with certain skill sets. Personnel working with AI systems, for instance, should be knowledgeable about image recognition, deep learning, cognitive computing, and ML and machine intelligence. In order to emulate human brain behavior, integrating AI technologies into current systems is a difficult operation that necessitates substantial data processing. Even a minor error can result in system failure or adversely affect the desired result. Additionally, the development of AI is being constrained by the lack of professional standards and certifications in AI/ML technologies. Because of a lack of technological understanding and a shortage of AI professionals, service providers encounter difficulties when delivering and maintaining their solutions at the locations of their clients.

Moreover, government or regulatory agencies must keep up regularly with advancements and guide AI system deployment, especially in healthcare. The accuracy, reliability, security, and clinical use of medical AI technologies are ensured by subjecting them to various standards and regulations. However, medical software regulation is still dynamic and dependent on changing guidelines and subjective interpretation by regulatory authorities. In the US, the FDA has regulatory authority over medical devices. To receive FDA approval, AI or machine learning tools that have healthcare applications must pass a series of tests to show that they can produce results at least as accurately as humans.

Similarly, there is no general exclusion for software in the European Union, and software may be regulated as a medical device if it has a medical purpose. Generally, a case-by-case assessment is required, considering the product characteristics, mode of use, and claims. However, the assessment is particularly complex because, unlike the classification of general medical devices, it is not immediately apparent how these parameters apply to software, given that software does not act on the human body to restore, correct, or modify bodily functions. As a result, the software used in healthcare settings is not necessarily a medical device. Such ambiguous regulatory guidelines sometimes create major barriers for market players.

Opportunity: Focus on developing human-aware AI systems

The aim of developing AI technologies was to make them human-aware or capable of human thinking patterns. However, creating interactive and scalable machines remains a challenge for the developers of AI machines. Additionally, increasing human interference with AI techniques has introduced new research challenges—interpretation and presentation challenges such as interaction issues with automating parts and intelligent control of crowdsourcing parts. Interpretation challenges include challenges faced by AI machines in understanding human input, such as knowledge and specific directives. Presentation challenges include issues related to delivering the AI system’s output and feedback. Thus, the development of human-aware AI systems remains the foremost opportunity for AI developers.

Challenge: Lack of curated genomic data

Data is a vital source to train and develop a complete and robust AI system. Earlier, datasets were mostly structured and entered manually. However, the growing digital footprint and technology adoption, such as IoT in healthcare and life science, has resulted in large data volumes that are unstructured (and in the form of text, voice, or images).

To train machine learning tools, developers require high-quality labeled data, along with skilled human trainers. Extracting and labeling unstructured data requires a large, skilled workforce and time. Moreover, patient information is extremely sensitive and subject to stringent privacy norms. For instance, legislations such as the HIPAA (implemented in the US in 1996) and the HITECH Act (implemented in the US in 2003) require entities responsible for sensitive health information to implement certain measures to ensure its privacy and security; these entities are also required to inform patients of instances when the privacy and security of their information have been compromised. This makes curated data hard to access due to privacy concerns, record identification concerns, and security requirements.

Thus, structured data plays a pivotal role in developing an efficient AI system. Companies are now practicing developing insights from semi-structured data (a combination of structured and unstructured data) that enables information from groupings and hierarchies. However, analytics tools and solutions for semi-structured data are still in the nascent stage.

Artificial Intelligence (AI) in Genomics Market Ecosystem

Well-known, financially secure producers of AI in genomics systems and platforms are prominent players in this market. These companies have been in operation for a while and have a broad range of products, cutting-edge technologies, and robust international sales and marketing networks. Key companies in this market include NVIDIA Corporation (US), Microsoft Corporation (US), Google, Inc. (US), Intel Corporation (US), Illumina, Inc. (US), and SOPHiA GENETICS (Switzerland).

Machine learning acquires largest size of AI in genomics market, by technology

Based on technology, the market is segmented into machine learning and other technologies. The machine learning segment dominated this market in 2022, as pharmaceutical companies, CROs, and biotechnology companies have widely adopted machine learning for drug genomics applications. This is because machine learning can extract insights from data sets, accelerating genomic research.

Based on application, diagnostics segment is anticipated to dominate the AI in genomics market

Based on application, the market is segmented into diagnostics, drug discovery & development, precision medicine, agriculture & animal research, and other applications. Diagnostics was the largest application segment in the market, in 2022. The large share of this segment can be attributed to the increasing research on diseases and the decreasing cost of sequencing.

Based on the end user, hospitals & healthcare providers accounted for the second largest share of AI in genomics market

Based on the end user, the market is broadly segmented into pharmaceutical & biotechnology companies; hospitals & healthcare providers; research centers, academic institutes, & government organizations; and other end users. Hospitals & healthcare providers accounted for the second largest share of the market in 2022. Factors such as rising demand for solutions to cut the time and costs of drug development drive the market growth.

North America is expected to account for the largest share in AI in genomics market in 2022

Based on region, the global market has been segmented into North America, Europe, Asia Pacific, and the Rest of the World. In 2022, North America accounted for the largest market share followed by Europe. The large share of North America can be attributed to the increasing research funding and government initiatives for promoting precision medicine in the US.

The AI in genomics market is dominated by a few globally established players such as NVIDIA Corporation (US), Microsoft Corporation (US), Google, Inc. (US), Intel Corporation (US), BenevolentAI (UK), SOPHiA GENETICS (Switzerland), Illumina, Inc. (US), among others.

AI in Genomics Market Report Scope

This research report categorizes the AI in genomics market to forecast revenue and analyze trends in each of the following submarkets:

By Offering

• Software
• Services

By Technology

• Machine Learning
  • Deep Learning
  • Supervised Learning
  • Reinforcement Learning
  • Unsupervised Learning
  • Other Machine Learning Technologies
• Other Technologies

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 Applications

By End User

• Pharmaceutical & Biotech Companies
• Healthcare Providers
• Research Centers, Academic Institutes, & Government Organizations
• Other End Users

By Region

• North America
  • US
  • Canada
• Europe
  • Germany
  • UK
  • France
  • Rest of Europe
• Asia Pacific
• Rest of the World

Recent Developments:

• In December 2022, Intel Labs and the Perelman School of Medicine at the University of Pennsylvania (Penn Medicine) completed of a joint research study using distributed machine learning (ML) and artificial intelligence (AI) approaches to help international healthcare and research institutions identify malignant brain tumors.
• In September 20222, NVIDIA Corporation partnered with the Broad Institute of MIT and Harvard to accelerate Genome analysis workflows and help teams to co-develop large language models for the discovery and development of targeted therapies. The collaboration connects NVIDIA’s AI expertise and healthcare computing platforms with the Broad Institute’s researchers, scientists, and open platforms with a focus on Making NVIDIA Clara Parabricks available in the Terra platform, building large language models, and providing improved deep learning to Genome Analysis Toolkit (GATK).
• In August 2021, Illumina, Inc. acquired GRAIL to provide patients with access to a potentially life-saving multi-cancer early-detection test.
• In March 2021, SOPHiA GENETICS collaborated with Hitachi. This collaboration agreement offered clinical, genomic, and real-world insights to healthcare practitioners and pharmaceutical and biotechnology firms and to further democratize Data-Driven Precision Medicine internationally for the benefit of patients.

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