基因组学市场中的人工智慧 - 全球行业规模、份额、趋势、机会和预测，按组件、技术、功能、应用、最终用途、地区和竞争细分，2019-2029F

2023 年，全球基因组学人工智慧市场估值为4.7921 亿美元，预计到2029 年，预测期内将稳定成长，复合年增长率为9.72%。了市场的广泛关注。这种协同作用为理解、诊断和治疗复杂的遗传性疾病以及推动个人化医疗开闢了新途径。基因组学市场中的人工智慧正在快速成长，有望重塑医疗保健格局。基因组学是对个体遗传物质的研究，是深入了解疾病和疾病遗传基础的关键。然而，基因组资料的复杂性和庞大性为分析和解释带来了巨大的挑战。这就是人工智慧介入的地方，它提供了一种解决方案，可以有效地处理这些复杂的资讯并从中提取有意义的见解。人工智慧演算法擅长处理大型数据集，使其成为基因组领域的完美匹配。透过应用机器学习技术，人工智慧可以识别基因组内可能被忽视的模式、相关性和异常。这使得研究人员和临床医生能够找出与疾病相关的遗传变异，为更准确的诊断和有针对性的治疗铺路。

市场概况
预测期	2025-2029
2023 年市场规模	4.7921亿美元
2029 年市场规模	83252万美元
2024-2029 年复合年增长率	9.72%
成长最快的细分市场	机器学习
最大的市场	北美洲

主要市场驱动因素

基因组数据的指数成长

加速药物发现与开发

个人化医疗和标靶治疗

主要市场挑战

数据品质和数量

可解释和可解释的人工智慧

道德考虑和隐私问题

主要市场趋势

基因组数据分析与解释

人工智慧驱动的诊断

细分市场洞察

技术洞察

应用洞察

区域洞察

目录

第 1 章：产品概述

第 2 章：研究方法

第 3 章：执行摘要

第 4 章：客户之声

第 5 章：基因组学领域的全球人工智慧市场展望

• 市场规模及预测
  • 按价值
• 市占率及预测
  • 按组件（硬体、软体、服务）
  • 按技术（机器学习{深度学习、监督学习、无监督学习、其他}、电脑视觉）
  • 按功能（基因组定序、基因编辑、其他）
  • 按应用（药物发现与开发、精准医学、诊断、其他）
  • 依最终用途（製药和生物技术公司、医疗保健提供者、研究中心等）
  • 按公司划分 (2023)
  • 按地区
• 市场地图

第 6 章：北美基因组学人工智慧市场展望

• 市场规模及预测
  • 按价值
• 市占率及预测
  • 按组件
  • 依技术
  • 按功能分类
  • 按申请
  • 按最终用途
  • 按国家/地区
• 北美：国家分析
  • 美国
  • 墨西哥
  • 加拿大

第 7 章：欧洲人工智慧在基因组学市场前景

• 市场规模及预测
  • 按价值
• 市占率及预测
  • 按组件
  • 依技术
  • 按功能分类
  • 按申请
  • 按最终用途
  • 按国家/地区
• 欧洲：国家分析
  • 法国
  • 德国
  • 英国
  • 义大利
  • 西班牙

第 8 章：亚太地区人工智慧在基因组学市场前景

• 市场规模及预测
  • 按价值
• 市占率及预测
  • 按组件
  • 依技术
  • 按功能分类
  • 按申请
  • 按最终用途
  • 按国家/地区
• 亚太地区：国家分析
  • 中国
  • 印度
  • 韩国
  • 日本
  • 澳洲

第 9 章：南美洲基因体学中的人工智慧市场展望

• 市场规模及预测
  • 按价值
• 市占率及预测
  • 按组件
  • 依技术
  • 按功能分类
  • 按申请
  • 按最终用途
  • 按国家/地区
• 南美洲：国家分析
  • 巴西
  • 阿根廷
  • 哥伦比亚

第 10 章：中东和非洲基因组学中的人工智慧市场展望

• 市场规模及预测
  • 按价值
• 市占率及预测
  • 按组件
  • 依技术
  • 按功能分类
  • 按申请
  • 按最终用途
  • 按国家/地区
• MEA：国家分析
  • 南非
  • 沙乌地阿拉伯
  • 阿联酋

第 11 章：市场动态

• 司机
• 挑战

第 12 章：市场趋势与发展

• 最近的发展
• 产品发布
• 併购

第 13 章：大环境分析

第 14 章：波特的五力分析

• 产业竞争
• 新进入者的潜力
• 供应商的力量
• 客户的力量
• 替代产品的威胁

第15章：竞争格局

• IBM Corp.
• Deep Genomics Inc.
• Nvidia Corporation
• Data4Cure, Inc.
• Illumina, Inc.
• Thermo Fisher Scientific Inc.
• Sophia Genetics SA
• Freenome Holdings, Inc.
• BenevolentAI Ltd.
• Genentech, Inc.

第 16 章：策略建议

第17章调查会社について・免责事项

Global AI In Genomics Market was valued at USD 479.21 Million in 2023 and is anticipated to project steady growth in the forecast period with a CAGR of 9.72% through 2029. In recent years, the convergence of artificial intelligence (AI) and genomics has ignited a transformative revolution in the healthcare industry. This synergy has opened up new avenues for understanding, diagnosing, and treating complex genetic disorders, as well as advancing personalized medicine. The AI in genomics market is witnessing rapid growth, poised to reshape the landscape of healthcare. Genomics, the study of an individual's genetic material, holds the key to unlocking a deeper understanding of the genetic basis of diseases and conditions. However, the complexity and vastness of genomic data pose significant challenges for analysis and interpretation. This is where artificial intelligence steps in, offering a solution to efficiently process and extract meaningful insights from this intricate information. AI algorithms excel in handling large datasets, making them a perfect match for the genomic field. By applying machine learning techniques, AI can identify patterns, correlations, and anomalies within genomes that might otherwise go unnoticed. This enables researchers and clinicians to pinpoint genetic variations associated with diseases, paving the way for more accurate diagnostics and targeted treatments.

Market Overview
Forecast Period	2025-2029
Market Size 2023	USD 479.21 Million
Market Size 2029	USD 832.52 Million
CAGR 2024-2029	9.72%
Fastest Growing Segment	Machine Learning
Largest Market	North America

Key Market Drivers

Exponential Growth of Genomic Data

One of the primary drivers behind the surge in AI applications in genomics is the exponential growth of genomic data. The advent of next-generation sequencing (NGS) technologies has enabled the rapid generation of vast amounts of genetic information. This deluge of data, ranging from whole genome sequences to transcriptomic profiles, presents both a challenge and an opportunity. The burgeoning field of genomics has unveiled intricate genetic variations, mutations, and regulatory elements that underlie various diseases and biological processes. However, the sheer volume of data generated presents a unique challenge i.e, making sense of the information hidden within the vast genomic landscapes. Artificial Intelligence, particularly machine learning (ML) and deep learning, has demonstrated its prowess in handling complex and high-dimensional data. In the realm of genomics, AI algorithms can analyze, interpret, and predict biological phenomena with remarkable accuracy. The combination of AI and genomics is a natural progression, driven by the need to extract meaningful insights from the vast genomic datasets. A key area where AI has made significant strides is in the identification of disease-causing genetic mutations. In the past, identifying the genetic basis of a disease was a laborious process, often requiring years of painstaking research.

With the advent of AI, this process has been accelerated dramatically. AI algorithms can rapidly sift through vast genomic datasets to pinpoint mutations associated with specific diseases. This has not only accelerated the understanding of genetic disorders but has also paved the way for more targeted and personalized treatments. AI's ability to identify patterns, detect anomalies, and uncover correlations within genomic data is invaluable for both research and clinical applications. ML models can identify potential disease-causing mutations, predict patient outcomes, and optimize drug discovery pipelines. AI-driven tools can unravel the complexities of gene expression, protein interactions, and regulatory networks, shedding light on the underlying mechanisms of diseases. AI excels at handling large datasets, and genomics is no exception. The sheer volume and complexity of genomic data require advanced computational methods to extract meaningful insights. AI algorithms can sift through massive datasets to identify patterns, correlations, and genetic variations associated with diseases. As the volume of genomic data continues to expand, the demand for AI-driven solutions becomes increasingly crucial for making sense of this wealth of information.

Accelerating Drug Discovery and Development

The integration of AI in genomics has revolutionized the drug discovery and development process. Traditional drug development can take years and incur exorbitant costs. AI algorithms have the potential to drastically reduce both the time and financial investments required.AI-driven genomics assists researchers in identifying potential drug targets with precision. By analyzing the interactions between genes, proteins, and pathways, AI algorithms can predict the impact of genetic variations on protein structures and functions. This predictive capability allows researchers to focus their efforts on developing drugs that are more likely to succeed. Traditional drug discovery pipelines involve extensive trial-and-error experimentation, often resulting in costly failures. The vastness of chemical and biological space, coupled with the complexity of disease mechanisms, has made the process incredibly challenging. This inefficiency has driven the pharmaceutical industry to seek innovative approaches that can expedite the process without compromising on safety and efficacy. Enter artificial intelligence, a technology that is transforming industries across the board. In drug discovery and development, AI is proving to be a game-changer.

Genomics, which provides insights into the genetic underpinnings of diseases, has become a crucial ally in this AI-driven revolution. AI algorithms can analyze extensive genomic datasets to identify potential drug targets, predict compound interactions, and optimize molecular structures. AI's impact begins at the very start of the drug discovery pipeline - target identification. Through the analysis of genomic data, AI can identify genes or proteins that play a critical role in disease pathways. These insights are invaluable in pinpointing potential drug targets. The traditional hit-or-miss approach is replaced by AI's ability to predict which molecules will interact with these targets and modulate disease mechanisms. This predictive power not only expedites the process but also increases the likelihood of success. AI doesn't stop at target identification; it extends to compound design as well. By harnessing AI algorithms, researchers can predict the properties of potential drug candidates. This enables the identification of molecules that are likely to have optimal binding affinity, bioavailability, and minimal toxicity. This predictive modeling accelerates the compound optimization phase, making it more efficient and cost-effective.

Personalized Medicine and Targeted Treatments

The concept of personalized medicine, tailoring medical treatments to an individual's genetic makeup and health history, has gained significant traction with the emergence of AI in genomics. Traditional medical approaches often adopt a one-size-fits-all strategy, which can result in suboptimal outcomes and adverse reactions for some patients. AI-driven genomics is transforming this paradigm by enabling personalized treatment strategies. AI has been playing a significant role in advancing the field of precision medicine. By analyzing large-scale genomic and clinical data, AI algorithms can identify patterns and correlations that help predict disease risk, diagnose conditions earlier, and personalize treatment plans based on individual genetic profiles. Personalized medicine, once confined to the realm of science fiction, has now become a reality due to the convergence of artificial intelligence (AI) and genomics. The integration of these two cutting-edge fields is revolutionizing healthcare by tailoring medical treatments to an individual's unique genetic makeup and health history. In contrast to traditional medical approaches that often employ a one-size-fits-all strategy, personalized medicine fueled by AI in genomics holds the promise of more effective treatments, fewer adverse reactions, and improved patient outcomes. At the core of personalized medicine lies genomics - the study of an individual's unique genetic information.

Each person's DNA holds a treasure trove of insights into their susceptibility to diseases, their response to medications, and their overall health trajectory. Genomic data has the power to reveal genetic mutations that increase disease risk, genetic variations that impact drug metabolism, and genetic markers that determine treatment efficacy. However, the complexity and vastness of genomic data make its analysis a formidable challenge. This is where AI steps in as an indispensable tool, capable of processing and interpreting the intricate patterns within genetic information. AI algorithms can analyze an individual's genetic profile, along with clinical and lifestyle data, to predict disease risks and treatment responses. This information empowers healthcare providers to develop customized treatment plans that optimize patient outcomes. From selecting the most effective medications to anticipating disease progression, AI in genomics enhances the precision and effectiveness of medical interventions. AI algorithms possess the ability to sift through vast genomic datasets, identifying meaningful associations that may otherwise remain hidden. These algorithms can identify genetic mutations associated with specific diseases, predict an individual's likelihood of developing certain conditions, and even anticipate how an individual will respond to a particular treatment.

Key Market Challenges

Data Quality and Quantity

The primary focus of AI in genomics lies data - specifically, high-quality and diverse genomic datasets. The accuracy and representativeness of these datasets directly impact the performance of AI algorithms. Unfortunately, challenges related to data quality and quantity persist. Genomic data is generated through various technologies and platforms, each with its own biases and limitations. Integrating data from multiple sources and technologies can introduce noise and inconsistencies that can affect the reliability of AI predictions. The availability of diverse and well-annotated datasets is essential for training AI models that can generalize across different populations and genetic backgrounds. Addressing these challenges requires concerted efforts to curate, standardize, and share high-quality genomic datasets. Collaboration between research institutions, data repositories, and AI developers is critical to ensure that the data used to train AI models accurately reflects the complexity of the human genome.

Interpretable and Explainable AI

The "black-box" nature of some AI algorithms poses a significant challenge in the genomics field, where transparency and interpretability are paramount. In medical applications, understanding the rationale behind AI-generated insights is crucial for gaining trust from healthcare professionals, researchers, and patients. The inability to explain AI predictions can hinder the adoption of AI-driven solutions in clinical practice. Researchers and developers are actively working on developing AI models that provide interpretable outputs. Techniques such as attention mechanisms, feature visualization, and model explanations aim to shed light on how AI arrives at its conclusions. Balancing the need for interpretability with the complexity of genomic data is a delicate endeavor that requires collaboration between AI experts and domain-specific researchers.

Ethical Considerations and Privacy Concerns

AI in genomics raises important ethical and privacy considerations. Genomic data is inherently sensitive, containing information about an individual's health, ancestry, and potential predisposition to diseases. Ensuring the responsible use, storage, and sharing of this data is essential to maintain patient trust and uphold ethical standards. Data breaches, unauthorized access, and potential misuse of genomic data are real concerns. Regulatory frameworks that dictate how genomic data can be collected, shared, and used must be established to protect patient privacy and ensure ethical AI practices. Collaboration between AI developers, legal experts, and policymakers is vital to strike a balance between innovation and ethical safeguards.

Key Market Trends

Genomic Data Analysis and Interpretation

The proliferation of next-generation sequencing technologies has generated an unprecedented amount of genomic data. AI plays a critical role in analyzing and interpreting this data, extracting meaningful insights from the vast genetic information. By employing deep learning algorithms and natural language processing, AI systems can identify genetic variations, detect disease-causing mutations, and predict potential genetic disorders. This trend is pivotal in unraveling the complexities of the human genome and its implications for health. Deep learning, a subset of AI, has gained prominence for its ability to automatically learn and represent intricate patterns within data. In genomics, deep learning algorithms can identify genetic variations and mutations that might be linked to various diseases. By learning from vast datasets, these algorithms can recognize subtle relationships between genetic markers and disease susceptibility that might be elusive to conventional analytical methods.

AI-Powered Diagnostics

AI technologies are transforming the diagnostic landscape by enabling accurate and rapid identification of genetic disorders. One of the most promising applications of AI in genomics is the detection of disease-causing mutations. AI algorithms can sift through large genomic datasets to pinpoint mutations that are associated with specific diseases. This capability holds immense potential for early disease diagnosis, enabling healthcare providers to intervene before symptoms manifest. Machine learning algorithms can analyze genomic data to diagnose diseases, predict disease progression, and suggest appropriate interventions. This trend has the potential to revolutionize clinical diagnostics, providing early and precise diagnoses that empower healthcare providers to take proactive measures in managing diseases.

Segmental Insights

Technology Insights

Based on Technology, Machine Learning have emerged as the fastest growing segment in the Global AI In Genomics Market in 2023. The dominance of the machine learning segment in the AI in genomics market can be attributed to several key factors that have propelled its growth and prominence in the industry. Machine learning techniques have demonstrated exceptional capabilities in analyzing vast amounts of genomic data, extracting meaningful insights, and driving advancements in genomics research and applications. One of the primary reasons for the dominance of machine learning is its ability to handle complex datasets with high dimensionality and variability, which are inherent characteristics of genomic data. Machine learning algorithms excel at identifying patterns, correlations, and associations within genomic datasets, enabling researchers to uncover hidden biological insights and make clinically relevant discoveries.

Machine learning algorithms exhibit a high degree of adaptability and scalability, allowing them to continuously improve and refine their predictive performance as they are exposed to new data. This iterative learning process enables machine learning models to evolve and become increasingly accurate in predicting various genomic outcomes, such as disease risk, treatment response, and patient prognosis. The widespread availability of advanced computational resources and cloud computing infrastructure has facilitated the implementation and deployment of machine learning algorithms in genomics research and clinical practice. These resources enable researchers and healthcare professionals to efficiently process and analyze large-scale genomic datasets, accelerating the pace of genomic discoveries and translating them into actionable insights for patient care. The integration of machine learning techniques with other advanced technologies, such as next-generation sequencing and precision medicine, has further bolstered the dominance of the machine learning segment in the AI in genomics market. By leveraging machine learning algorithms to interpret genomic data in conjunction with clinical and phenotypic information, researchers and healthcare providers can achieve a more comprehensive understanding of the genetic basis of diseases and tailor personalized treatment strategies for patients.

Application Insights

Based on Application, Diagnostics have emerged as the dominating segment in the Global AI In Genomics Market in 2023. AI-powered algorithms can rapidly analyse intricate genomic patterns, enabling more accurate and efficient identification of genetic markers associated with various conditions. This capability not only expedites early disease diagnosis but also facilitates personalized treatment strategies, ultimately driving the prominence of the diagnostics segment within the AI in genomics domain. The dominance of the diagnostics segment in the AI in genomics market can further be attributed to several key factors that underscore its significance in driving market growth and adoption. Diagnostics play a pivotal role in leveraging AI technologies to analyze genomic data for disease detection, risk assessment, and treatment selection. AI-powered diagnostic tools offer unprecedented capabilities to process vast amounts of genomic information quickly and accurately, enabling healthcare providers to make informed decisions about patient care.

The rising prevalence of genetic disorders, chronic diseases, and cancer has fueled the demand for advanced diagnostic solutions that can accurately interpret genomic data. AI algorithms can sift through complex genetic variations and identify patterns associated with specific diseases, facilitating early detection and personalized treatment strategies. The integration of AI into diagnostic workflows has streamlined genomic testing processes, making them more efficient, cost-effective, and accessible to a broader patient population. Automated AI algorithms can analyze genomic sequences with greater speed and precision than traditional methods, reducing turnaround times and enhancing diagnostic accuracy. Advancements in AI-driven diagnostic technologies have spurred collaborations between healthcare institutions, research organizations, and technology companies to develop innovative solutions for genomics-based diagnostics. These partnerships have accelerated the translation of AI research into practical applications, driving market growth and expanding the utility of genomic data in clinical practice.

Regional Insights

Based on Region, North America have emerged as a dominating region in the Global AI In Genomics Market in 2023. The region's preeminence in AI-enabled genomics is a testament to its unparalleled blend of technological prowess, robust research infrastructure, and vibrant biotechnology ecosystem. At the core of North America's leadership lies its unparalleled concentration of cutting-edge technology and innovation hubs. The region boasts a rich tapestry of world-renowned research institutions, biopharmaceutical companies, and tech giants, collectively serving as engines of innovation in AI and genomics. These institutions form a dynamic ecosystem characterized by collaboration and knowledge exchange, fueling the rapid advancement of AI technologies in genomics research and application. North America's robust research infrastructure provides a solid foundation for pioneering AI-driven genomics initiatives. The region is home to state-of-the-art genomic sequencing facilities, high-performance computing centers, and data analytics platforms, facilitating the processing and analysis of vast genomic datasets with unprecedented speed and precision. This infrastructure enables researchers and healthcare professionals to harness the power of AI algorithms to unlock valuable insights from genomic data, driving scientific discovery and clinical innovation.

Key Market Players

IBM Corp.

Deep Genomics Inc.

Nvidia Corporation

Data4Cure, Inc.

Illumina, Inc.

Thermo Fisher Scientific Inc.

Sophia Genetics S.A.

Freenome Holdings, Inc.

BenevolentAI Ltd.

Genentech, Inc.

Report Scope:

In this report, the Global AI In Genomics Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

AI In Genomics Market, By Component:

Hardware Software Services

AI In Genomics Market, By Technology:

Machine Learning Computer Vision

AI In Genomics Market, By Functionality:

Genome Sequencing Gene Editing Others

AI In Genomics Market, By Application:

Drug Discovery & Development Precision Medicine Diagnostics Others

AI In Genomics Market, By End Use:

Pharmaceutical and Biotech Companies Healthcare Providers Research Centres Others

AI In Genomics Market, By Region:

North America
• United States
• Canada
• Mexico
Asia Pacific
• China
• India
• South Korea
• Australia
• Japan
Europe
• Germany
• France
• United Kingdom
• Spain
• Italy
South America
• Brazil
• Argentina
• Colombia
Middle East & Africa
• South Africa
• Saudi Arabia
• UAE

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the Global AI In Genomics Market.

Available Customizations:

Global AI In Genomics Market report with the given market data, TechSci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

Detailed analysis and profiling of additional market players (up to five).

Table of Contents

1. Product Overview

• 1.1. Market Definition
• 1.2. Scope of the Market
  • 1.2.1. Markets Covered
  • 1.2.2. Years Considered for Study
  • 1.2.3. Key Market Segmentations

2. Research Methodology

• 2.1. Objective of the Study
• 2.2. Baseline Methodology
• 2.3. Key Industry Partners
• 2.4. Major Association and Secondary Sources
• 2.5. Forecasting Methodology
• 2.6. Data Triangulation & Validation
• 2.7. Assumptions and Limitations

3. Executive Summary

• 3.1. Overview of the Market
• 3.2. Overview of Key Market Segmentations
• 3.3. Overview of Key Market Players
• 3.4. Overview of Key Regions/Countries
• 3.5. Overview of Market Drivers, Challenges, Trends

4. Voice of Customer

5. Global AI In Genomics Market Outlook

• 5.1. Market Size & Forecast
  • 5.1.1. By Value
• 5.2. Market Share & Forecast
  • 5.2.1. By Component (Hardware, Software, Services)
  • 5.2.2. By Technology (Machine Learning {Deep Learning, Supervised Learning, Unsupervised Learning, Others}, Computer Vision)
  • 5.2.3. By Functionality (Genome Sequencing, Gene Editing, Others)
  • 5.2.4. By Application (Drug Discovery & Development, Precision Medicine, Diagnostics, Others)
  • 5.2.5. By End Use (Pharmaceutical and Biotech Companies, Healthcare Providers, Research Centres, Others)
  • 5.2.6. By Company (2023)
  • 5.2.7. By Region
• 5.3. Market Map

6. North America AI in Genomics Market Outlook

• 6.1. Market Size & Forecast
  • 6.1.1. By Value
• 6.2. Market Share & Forecast
  • 6.2.1. By Component
  • 6.2.2. By Technology
  • 6.2.3. By Functionality
  • 6.2.4. By Application
  • 6.2.5. By End Use
  • 6.2.6. By Country
• 6.3. North America: Country Analysis
  • 6.3.1. United States AI In Genomics Market Outlook
    • 6.3.1.1. Market Size & Forecast
      • 6.3.1.1.1. By Value
    • 6.3.1.2. Market Share & Forecast
      • 6.3.1.2.1. By Component
      • 6.3.1.2.2. By Technology
      • 6.3.1.2.3. By Functionality
      • 6.3.1.2.4. By Application
      • 6.3.1.2.5. By End Use
  • 6.3.2. Mexico AI In Genomics Market Outlook
    • 6.3.2.1. Market Size & Forecast
      • 6.3.2.1.1. By Value
    • 6.3.2.2. Market Share & Forecast
      • 6.3.2.2.1. By Component
      • 6.3.2.2.2. By Technology
      • 6.3.2.2.3. By Functionality
      • 6.3.2.2.4. By Application
      • 6.3.2.2.5. By End Use
  • 6.3.3. Canada AI In Genomics Market Outlook
    • 6.3.3.1. Market Size & Forecast
      • 6.3.3.1.1. By Value
    • 6.3.3.2. Market Share & Forecast
      • 6.3.3.2.1. By Component
      • 6.3.3.2.2. By Technology
      • 6.3.3.2.3. By Functionality
      • 6.3.3.2.4. By Application
      • 6.3.3.2.5. By End Use

7. Europe AI In Genomics Market Outlook

• 7.1. Market Size & Forecast
  • 7.1.1. By Value
• 7.2. Market Share & Forecast
  • 7.2.1. By Component
  • 7.2.2. By Technology
  • 7.2.3. By Functionality
  • 7.2.4. By Application
  • 7.2.5. By End Use
  • 7.2.6. By Country
• 7.3. Europe: Country Analysis
  • 7.3.1. France AI In Genomics Market Outlook
    • 7.3.1.1. Market Size & Forecast
      • 7.3.1.1.1. By Value
    • 7.3.1.2. Market Share & Forecast
      • 7.3.1.2.1. By Component
      • 7.3.1.2.2. By Technology
      • 7.3.1.2.3. By Functionality
      • 7.3.1.2.4. By Application
      • 7.3.1.2.5. By End Use
  • 7.3.2. Germany AI In Genomics Market Outlook
    • 7.3.2.1. Market Size & Forecast
      • 7.3.2.1.1. By Value
    • 7.3.2.2. Market Share & Forecast
      • 7.3.2.2.1. By Component
      • 7.3.2.2.2. By Technology
      • 7.3.2.2.3. By Functionality
      • 7.3.2.2.4. By Application
      • 7.3.2.2.5. By End Use
  • 7.3.3. United Kingdom AI in Genomics Market Outlook
    • 7.3.3.1. Market Size & Forecast
      • 7.3.3.1.1. By Value
    • 7.3.3.2. Market Share & Forecast
      • 7.3.3.2.1. By Component
      • 7.3.3.2.2. By Technology
      • 7.3.3.2.3. By Functionality
      • 7.3.3.2.4. By Application
      • 7.3.3.2.5. By End Use
  • 7.3.4. Italy AI In Genomics Market Outlook
    • 7.3.4.1. Market Size & Forecast
      • 7.3.4.1.1. By Value
    • 7.3.4.2. Market Share & Forecast
      • 7.3.4.2.1. By Component
      • 7.3.4.2.2. By Technology
      • 7.3.4.2.3. By Functionality
      • 7.3.4.2.4. By Application
      • 7.3.4.2.5. By End Use
  • 7.3.5. Spain AI In Genomics Market Outlook
    • 7.3.5.1. Market Size & Forecast
      • 7.3.5.1.1. By Value
    • 7.3.5.2. Market Share & Forecast
      • 7.3.5.2.1. By Component
      • 7.3.5.2.2. By Technology
      • 7.3.5.2.3. By Functionality
      • 7.3.5.2.4. By Application
      • 7.3.5.2.5. By End Use

8. Asia Pacific AI In Genomics Market Outlook

• 8.1. Market Size & Forecast
  • 8.1.1. By Value
• 8.2. Market Share & Forecast
  • 8.2.1. By Component
  • 8.2.2. By Technology
  • 8.2.3. By Functionality
  • 8.2.4. By Application
  • 8.2.5. By End Use
  • 8.2.6. By Country
• 8.3. Asia Pacific: Country Analysis
  • 8.3.1. China AI In Genomics Market Outlook
    • 8.3.1.1. Market Size & Forecast
      • 8.3.1.1.1. By Value
    • 8.3.1.2. Market Share & Forecast
      • 8.3.1.2.1. By Component
      • 8.3.1.2.2. By Technology
      • 8.3.1.2.3. By Functionality
      • 8.3.1.2.4. By Application
      • 8.3.1.2.5. By End Use
  • 8.3.2. India AI In Genomics Market Outlook
    • 8.3.2.1. Market Size & Forecast
      • 8.3.2.1.1. By Value
    • 8.3.2.2. Market Share & Forecast
      • 8.3.2.2.1. By Component
      • 8.3.2.2.2. By Technology
      • 8.3.2.2.3. By Functionality
      • 8.3.2.2.4. By Application
      • 8.3.2.2.5. By End Use
  • 8.3.3. South Korea AI In Genomics Market Outlook
    • 8.3.3.1. Market Size & Forecast
      • 8.3.3.1.1. By Value
    • 8.3.3.2. Market Share & Forecast
      • 8.3.3.2.1. By Component
      • 8.3.3.2.2. By Technology
      • 8.3.3.2.3. By Functionality
      • 8.3.3.2.4. By Application
      • 8.3.3.2.5. By End Use
  • 8.3.4. Japan AI In Genomics Market Outlook
    • 8.3.4.1. Market Size & Forecast
      • 8.3.4.1.1. By Value
    • 8.3.4.2. Market Share & Forecast
      • 8.3.4.2.1. By Component
      • 8.3.4.2.2. By Technology
      • 8.3.4.2.3. By Functionality
      • 8.3.4.2.4. By Application
      • 8.3.4.2.5. By End Use
  • 8.3.5. Australia AI In Genomics Market Outlook
    • 8.3.5.1. Market Size & Forecast
      • 8.3.5.1.1. By Value
    • 8.3.5.2. Market Share & Forecast
      • 8.3.5.2.1. By Component
      • 8.3.5.2.2. By Technology
      • 8.3.5.2.3. By Functionality
      • 8.3.5.2.4. By Application
      • 8.3.5.2.5. By End Use

9. South America AI In Genomics Market Outlook

• 9.1. Market Size & Forecast
  • 9.1.1. By Value
• 9.2. Market Share & Forecast
  • 9.2.1. By Component
  • 9.2.2. By Technology
  • 9.2.3. By Functionality
  • 9.2.4. By Application
  • 9.2.5. By End Use
  • 9.2.6. By Country
• 9.3. South America: Country Analysis
  • 9.3.1. Brazil AI In Genomics Market Outlook
    • 9.3.1.1. Market Size & Forecast
      • 9.3.1.1.1. By Value
    • 9.3.1.2. Market Share & Forecast
      • 9.3.1.2.1. By Component
      • 9.3.1.2.2. By Technology
      • 9.3.1.2.3. By Functionality
      • 9.3.1.2.4. By Application
      • 9.3.1.2.5. By End Use
  • 9.3.2. Argentina AI In Genomics Market Outlook
    • 9.3.2.1. Market Size & Forecast
      • 9.3.2.1.1. By Value
    • 9.3.2.2. Market Share & Forecast
      • 9.3.2.2.1. By Component
      • 9.3.2.2.2. By Technology
      • 9.3.2.2.3. By Functionality
      • 9.3.2.2.4. By Application
      • 9.3.2.2.5. By End Use
  • 9.3.3. Colombia AI In Genomics Market Outlook
    • 9.3.3.1. Market Size & Forecast
      • 9.3.3.1.1. By Value
    • 9.3.3.2. Market Share & Forecast
      • 9.3.3.2.1. By Component
      • 9.3.3.2.2. By Technology
      • 9.3.3.2.3. By Functionality
      • 9.3.3.2.4. By Application
      • 9.3.3.2.5. By End Use

10. Middle East and Africa AI In Genomics Market Outlook

• 10.1. Market Size & Forecast
  • 10.1.1. By Value
• 10.2. Market Share & Forecast
  • 10.2.1. By Component
  • 10.2.2. By Technology
  • 10.2.3. By Functionality
  • 10.2.4. By Application
  • 10.2.5. By End Use
  • 10.2.6. By Country
• 10.3. MEA: Country Analysis
  • 10.3.1. South Africa AI In Genomics Market Outlook
    • 10.3.1.1. Market Size & Forecast
      • 10.3.1.1.1. By Value
    • 10.3.1.2. Market Share & Forecast
      • 10.3.1.2.1. By Component
      • 10.3.1.2.2. By Technology
      • 10.3.1.2.3. By Functionality
      • 10.3.1.2.4. By Application
      • 10.3.1.2.5. By End Use
  • 10.3.2. Saudi Arabia AI In Genomics Market Outlook
    • 10.3.2.1. Market Size & Forecast
      • 10.3.2.1.1. By Value
    • 10.3.2.2. Market Share & Forecast
      • 10.3.2.2.1. By Component
      • 10.3.2.2.2. By Technology
      • 10.3.2.2.3. By Functionality
      • 10.3.2.2.4. By Application
      • 10.3.2.2.5. By End Use
  • 10.3.3. UAE AI In Genomics Market Outlook
    • 10.3.3.1. Market Size & Forecast
      • 10.3.3.1.1. By Value
    • 10.3.3.2. Market Share & Forecast
      • 10.3.3.2.1. By Component
      • 10.3.3.2.2. By Technology
      • 10.3.3.2.3. By Functionality
      • 10.3.3.2.4. By Application
      • 10.3.3.2.5. By End Use

11. Market Dynamics

• 11.1. Drivers
• 11.2. Challenges

12. Market Trends & Developments

• 12.1. Recent Developments
• 12.2. Product Launches
• 12.3. Mergers & Acquisitions

13. PESTLE Analysis

14. Porter's Five Forces Analysis

• 14.1. Competition in the Industry
• 14.2. Potential of New Entrants
• 14.3. Power of Suppliers
• 14.4. Power of Customers
• 14.5. Threat of Substitute Product

15. Competitive Landscape

• 15.1. IBM Corp.
  • 15.1.1. Business Overview
  • 15.1.2. Company Snapshot
  • 15.1.3. Products & Services
  • 15.1.4. Financials (As Reported)
  • 15.1.5. Recent Developments
  • 15.1.6. Key Personnel Details
  • 15.1.7. SWOT Analysis
• 15.2. Deep Genomics Inc.
• 15.3. Nvidia Corporation
• 15.4. Data4Cure, Inc.
• 15.5. Illumina, Inc.
• 15.6. Thermo Fisher Scientific Inc.
• 15.7. Sophia Genetics S.A.
• 15.8. Freenome Holdings, Inc.
• 15.9. BenevolentAI Ltd.
• 15.10. Genentech, Inc.

16. Strategic Recommendations

17. About Us & Disclaimer