AI In Genomics Market 2030 By Component (Software, Hardware, Services), Technology (Machine Learning {Deep Learning, Supervised Learning, Reinforcement Learning, Unsupervised Learning}, Others), Functionality (Genome Sequencing, Gene Editing, Clinical Workflows, Predictive Genetic Testing & Preventive Medicine), Application, End-Use and Region - Partner & Customer Ecosystem (Product Services, Proposition & Key Features) Competitive Index & Regional Footprints by MarketDigits

Industry : Healthcare | Pages : 184 Pages | Published On : Nov 2023

The AI In Genomics Market was valued USD 0.5 Billion in 2022 and projected to reach USD 5.6 Billion by 2030, growing at a CAGR of 35.3% during the forecast period of 2023-2030.

Major Players In AI In Genomics Market Include: IBM, Microsoft, NVIDIA, Deep Genomics, DATA4CURE, Freenome holdings, Inc, Thermo fisher scientific, Illumina Inc, Sophia Genetics, BenevolentA, verge genomics, Molecular match Inc, Tempus, Fabric Genomics, Congenica Ltd, BGI Genomics, QIAGEN, Seven Bridges Genomics,  PierianDx, DNAnexus Inc and Others.

Market Overview

AI, an intelligent system, applies human-like reasoning, learning, and problem-solving in fields like healthcare, computer science, math, and engineering. Its integration in healthcare automates tasks, analyzes patient data, and enhances care efficiency and cost-effectiveness, aiding disease diagnosis and gene variation identification. In genomics, AI encompasses on-premises, cloud, and web-based software and services. It's pivotal in genome sequencing, editing, pharmacogenomics, and gene testing, speeding drug discovery, precision medicine, diagnostics, and more. Growth drivers include cost control, increased AI investments, and precision medicine adoption. Collaboration, investments, and improved computing power also contribute to market expansion, alongside the burgeoning bioinformatics and genomic data.

Trim Drug Development And Discovery Expenses, And Meet Tighter Timelines.

Drug discovery is an expensive and time-consuming process, necessitating the search for alternative tools. Current methods, both in vivo and in vitro, are costly, and it typically takes about a decade and around $2.6 billion for a new drug to reach the market. Only a small fraction of compounds, about 1 in 5,000-10,000, become potential drugs for specific conditions. Many drug candidates identified in the discovery phase fail in later stages due to issues like toxicity. Machine learning can predict drug compound outcomes early in the discovery phase, reducing time and costs significantly.

This potential for time and cost savings has garnered considerable attention, leading to various investigative projects. For example, in November 2020, Deep Genomics and BioMarin collaborated to discover oligonucleotide drug candidates for rare diseases, using Deep Genomics' AI Workbench platform alongside BioMarin's rare disease expertise. AI in genomics for drug discovery holds the promise of expediting the drug development process, cutting costs, and improving patient outcomes through the development of more targeted and effective drugs.

Market Dynamics


  • The Adoption Of AI In Precision Medicine Is On The Rise.
  • AI Is Increasingly Applied In Genomics, Accompanied By A Surge In Genomics Investment.
  • AI In Genomics Are Reduce The Cost Of Sequencing


  • Increased Funding And Investment.
  • Increasing Advancements In Drug Discovery.

AI was being increasingly utilized to tailor treatments to an individual's genetic makeup.

A significant trend in the AI in genomics field involves the increasing application of artificial intelligence to customize medical treatments based on an individual's genetic information. This personalized medicine approach harnesses AI's power to analyze and interpret genetic data, enabling healthcare providers to deliver highly tailored treatments that are precisely suited to a patient's genetic makeup. By doing so, it holds the potential to enhance treatment effectiveness, minimize adverse reactions, and ultimately improve patient care and outcomes.

The market for AI In Genomics Market is dominated by North America.

The AI in genomics market is predominantly dominated by North America due to a combination of factors. The region boasts a robust infrastructure for genomics research and development, a high level of investment in AI technologies, and a well-established healthcare system. Additionally, North America houses a concentration of leading AI and genomics companies, research institutions, and academic centers. The presence of a well-defined regulatory framework and supportive policies for AI and genomics applications further strengthens its market dominance. The region's leadership in genomics research, combined with significant public and private investments, positions North America at the forefront of the AI in genomics market, driving innovation and adoption of these technologies.

Machine Learing Segment is anticipated to hold the Largest Market Share during the Forecast Period

Machine learning is playing an increasingly significant role in the IoT (Internet of Things) genomics market, primarily due to its capacity to analyze and derive valuable insights from the vast amounts of genomic data generated by connected devices and sensors. By harnessing machine learning algorithms, this sector can enhance personalized medicine, disease prediction, and treatment strategies, ultimately leading to more effective and precise healthcare solutions, driving growth and innovation in the IoT genomics market. ML is being used to develop new drugs and therapies more quickly and efficiently. For example, in 2023, the company DeepMind developed an AI algorithm that can predict the 3D structure of proteins, which is essential for drug discovery.

ML is also being used to develop new and improved diagnostic tools for diseases. For example, in 2023, the company Verily developed an AI-powered blood test that can detect over 50 types of cancer at an early stage.

Major Segmentations Are Distributed as follows:

By Component

  • Software
  • Hardware
  • Services

By Technology

  • Machine Learning
    • Deep Learning
    • Supervised Learning
    • Reinforcement Learning
    • Unsupervised Learning
  • Others

By Functionality

  • Genome Sequencing
  • Gene Editing
  • Clinical Workflows
  • Predictive Genetic Testing & Preventive Medicine

By Application

  • Diagnostics
  • Drug Discovery & Development
  • Precision Medicine
  • Agriculture & animal Research
  • Others

By End-Use

  • Pharmaceutical & Biotech Companies
  • Government Organizations
  • Healthcare Providers
  • Others

By Region

  • North America
    • US
    • Canada
  • Latin America
    • Brazil
    • Mexico
    • Argentina
    • Colombia
    • Chile
    • Peru
    • Rest of Latin America
  • Europe
    • Germany
    • France
    • Italy
    • Spain
    • U.K.
    • CIS & Russia
    • Nordics
    • Austria
    • Poland
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • South Korea
    • India
    • Thailand
    • Indonesia
    • Malaysia
    • Vietnam
    • Australia & New Zealand
    • Rest of Asia Pacific
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • South Africa
    • Nigeria
    • Egypt
    • Israel
    • Turkey
    • Rest of MEA

​​​​​​​Recent Developments

  • In June 2022, Ultima Genomics Collaborates with NVIDIA To Deliver $100 Genome Sequencing with AI and Accelerated Computing. Ultima Genomics, Inc. today unveiled a broad collaboration with NVIDIA that leverages AI-accelerated analysis across multiple stages of the DNA sequencing and analysis process. The collaboration includes the use of NVIDIA GPUs for deep learning in photometry and base calling and will soon include acceleration of variant calling.
  • In October 2022, GRAIL has unveiled the Galleri® test, a groundbreaking MCED (Multi-Cancer Early Detection) test that is commercially accessible. This innovative test can identify a common signal present in numerous cancer types, even those that aren't typically screened for, all from a single blood sample. MCED testing enables individuals to be screened for this shared signal across over 50 different cancer types, significantly improving the likelihood of early cancer detection.

Answers to Following Key Questions:

  • What will be the AI In Genomics Market’s Trends & growth rate? What analysis has been done of the prices, sales, and volume of the top producers of Artificial Intelligence in the Agriculture Market?
  • What are the main forces behind worldwide AI In Genomics Market? Which companies dominate AI In Genomics Market?
  • Which companies dominate AI In Genomics Market? Which business possibilities, dangers, and tactics did they embrace in the market?
  • What are the global Insight Engines industry's suppliers' opportunities and dangers in AI In Genomics Market?
  • What is the Insight Engines industry's regional sales, income, and pricing analysis? In the AI In Genomics Market, who are the distributors, traders, and resellers?
  • What are the main geographic areas for various trades that are anticipated to have astounding expansion over the AI In Genomics Market?
  • What are the main geographical areas for various industries that are anticipated to observe astounding expansion for AI In Genomics Market?
  • What are the dominant revenue-generating regions for AI In Genomics Market, as well as regional growth trends?
  • By the end of the forecast period, what will the market size and growth rate be?
  • What are the main AI In Genomics Market trends that are influencing the market's expansion?
  • Which key product categories dominate AI In Genomics Market?
  • What is AI In Genomics Market’s main applications?
  • In the coming years, which AI In Genomics Market technology will dominate the market?

Reason to purchase this AI In Genomics Market Report:

  • Determine prospective investment areas based on a detailed trend analysis of the global AI In Genomics Market over the next years.
  • Gain an in-depth understanding of the underlying factors driving demand for different AI In Genomics Market segments in the top spending countries across the world and identify the opportunities each offers.
  • Strengthen your understanding of the market in terms of demand drivers, industry trends, and the latest technological developments, among others.
  • Identify the major channels that are driving the global AI In Genomics Market, providing a clear picture of future opportunities that can be tapped, resulting in revenue expansion.
  • Channelize resources by focusing on the ongoing programs that are being undertaken by the different countries within the global AI In Genomics Market.
  • Make correct business decisions based on a thorough analysis of the total competitive landscape of the sector with detailed profiles of the top AI In Genomics Market providers worldwide, including information about their products, alliances, recent contract wins, and financial analysis wherever available.


Table and Figures


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