Pune, Jan. 31, 2024 (GLOBE NEWSWIRE) -- “The Artificial Intelligence in Genomics Market, as per the SNS Insider report, achieved a valuation of USD 464.50 million in 2022. It is projected to attain a market size of USD 9855.68 million by 2030, exhibiting a remarkable compound annual growth rate (CAGR) of 46.5% during the forecast period from 2023 to 2030.”
Market Overview
Artificial Intelligence (AI) has emerged as a transformative force in various domains, and its impact on genomics is no exception. Genomics, the study of an organism's complete set of genes, has witnessed a revolutionary paradigm shift with the integration of AI technologies. Traditional drug discovery is a time-consuming and resource-intensive process. AI expedites this by analyzing vast genomic datasets to identify potential drug targets, predict drug responses, and optimize drug design. The integration of AI in drug discovery holds the promise of accelerating the development of novel therapies and personalized treatment plans.
Market Analysis
The continuous evolution of machine learning techniques, including deep learning, reinforcement learning, and transfer learning, has significantly enhanced the capabilities of AI in genomics. These sophisticated algorithms enable more accurate predictions and classifications, thereby fueling the artificial intelligence in genomics market. The exponential growth in genomic data has ushered in a new era of challenges and opportunities. With the plummeting cost of genome sequencing, vast datasets are being generated, necessitating advanced computational tools for analysis. Artificial intelligence, with its prowess in handling complex datasets, is becoming the cornerstone in deciphering the intricate genetic codes efficiently. Increasing recognition of the potential impact of AI in genomics has led to the establishment of regulatory frameworks and guidelines. Regulatory support provides a conducive environment for market growth, ensuring that AI applications in genomics adhere to ethical standards and safety protocols.
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Major Players Listed in this Report are:
- IBM
- Cambridge Cancer Genomics
- NVIDIA Corporation
- Thermo Fisher Scientific
- MolecularMatch Inc.
- Deep Genomics
- Microsoft
- Freenome Holdings, Inc.
- BenevolentAI
- Fabric Genomics Inc.
Artificial Intelligence in Genomics Market Report Scope:
| Report Attributes | Details |
| Market Size in 2022 | US$ 464.50 Million |
| Market Size by 2030 | US$ 9855.68 Million |
| CAGR | CAGR of 46.5% From 2023 to 2030 |
| Base Year | 2022 |
| Forecast Period | 2023-2030 |
| Historical Data | 2019-2021 |
| Key Regional Coverage | North America (US, Canada, Mexico), Europe (Eastern Europe [Poland, Romania, Hungary, Turkey, Rest of Eastern Europe] Western Europe] Germany, France, UK, Italy, Spain, Netherlands, Switzerland, Austria, Rest of Western Europe]), Asia Pacific (China, India, Japan, South Korea, Vietnam, Singapore, Australia, Rest of Asia Pacific), Middle East & Africa (Middle East [UAE, Egypt, Saudi Arabia, Qatar, Rest of Middle East], Africa [Nigeria, South Africa, Rest of Africa], Latin America (Brazil, Argentina, Colombia Rest of Latin America) |
| Key Growth Drivers | • Controlling the time and expense of medication development and discovery is essential. • Increasing the amount of money spent on AI in genomics |
Artificial Intelligence in Genomics Market Trends & Industry Insights
The intersection of artificial intelligence (AI) and genomics has ignited a transformative wave in the realm of healthcare, giving rise to a dynamic and evolving market landscape. As we delve into the trends and insights of the Artificial Intelligence in Genomics Market, it becomes apparent that the synergy between advanced computational algorithms and genomic data is propelling precision medicine to unprecedented heights. AI-powered tools are redefining how we decipher the intricacies of the human genome, enabling researchers to unravel complex genetic patterns, identify potential disease markers, and accelerate the pace of drug discovery. Machine learning algorithms, fine-tuned for genomics applications, are not only enhancing the accuracy of genomic analyses but also paving the way for personalized therapeutic interventions. The market is witnessing a shift towards decentralized genomics, where AI algorithms are integrated into portable devices, empowering individuals to have greater control over their genetic information.
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Key Takeaway from Artificial Intelligence in Genomics Market Study
- Machine Learning, a subset of artificial intelligence, has proven to be a game-changer in the analysis of genomic data. Its ability to discern patterns, identify correlations, and predict outcomes based on vast datasets has unlocked new dimensions in genomics research.
- Genome sequencing, a revolutionary technology, has witnessed remarkable advancements in recent years. The ability to decode an individual's entire DNA sequence provides an unparalleled depth of information. This wealth of genomic data is pivotal in tailoring medical interventions to the unique genetic makeup of each patient, a paradigm known as precision medicine.
Recent Developments
- In a move towards advancing artificial intelligence (AI)-driven drug discovery and the evolution of personalized medicine, Accenture has strategically invested in Ocean Genomics. By integrating AI into genomics research, the partnership seeks to unravel complex biological information efficiently and identify potential drug candidates with unprecedented speed and accuracy.
- Verge Genomics has announced a pioneering collaboration in AI-enabled drug discovery, forging a strategic partnership with Alexion to tackle the challenges associated with rare diseases. This joint venture represents a concerted effort to leverage artificial intelligence in unraveling the complexities of rare diseases and developing targeted therapeutic solutions.
Market Dynamics Analysis
The dynamics of the artificial intelligence in genomics market are multifaceted, driven by a confluence of factors that shape its trajectory. At the forefront of the market's drivers is the increasing demand for precision medicine, wherein AI plays a pivotal role in analyzing vast genomic datasets to tailor treatment plans based on an individual's genetic makeup. This personalized approach not only enhances patient outcomes but also contributes to the optimization of healthcare resources. Furthermore, the escalating adoption of next-generation sequencing technologies has exponentially expanded the volume of genomic data generated, necessitating sophisticated AI solutions for efficient processing and interpretation.
However, amidst the promising advancements, the artificial intelligence in genomics market faces its set of challenges and restraints. One notable challenge lies in the complex nature of genomic data, characterized by its sheer volume, intricacy, and dynamic nature. The integration of AI into genomics demands robust algorithms capable of deciphering this complexity, posing a challenge to researchers and developers alike. Additionally, issues related to data privacy and security are significant concerns, given the sensitive nature of genomic information. The need for stringent regulations and ethical frameworks becomes paramount to address these concerns and foster trust among stakeholders.
Artificial Intelligence in Genomics Market Segmentation:
By Offering Type
- Software
- Services
By Technology
- Machine Learning
- Deep Learning
- Supervised Learning
- Reinforcement Learning
- Unsupervised Learning
- Computer Vision
By Functionality
- Genome Sequencing
- Gene Editing
- Clinical Workflows
- Predictive Genetic Testing & Preventive Medicine
By Application
- Drug Discovery & Development
- Precision Medicine
- Diagnostics
- Others
By End User
- Pharmaceutical & Biotech Companies
- Healthcare Providers
- Research Centers, Academic Institutes, &
- Government Organizations
Key Regional Developments
North America stands at the forefront of artificial intelligence in genomics market, owing to the presence of leading biotechnology and pharmaceutical companies, as well as a robust healthcare infrastructure. The region has witnessed a surge in collaborative efforts between AI developers and genomics researchers, leading to the creation of advanced tools for genetic analysis. The European region has demonstrated a strong commitment to advancing genomics research through the integration of AI. Collaborations between academic institutions, research organizations, and private enterprises have paved the way for innovative solutions in genomic analysis. The Asia-Pacific region is experiencing a rapid rise in the adoption of AI in genomics, driven by increasing investments in healthcare infrastructure and rising awareness of personalized medicine.
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Impact of Recession
The ongoing recession has prompted organizations to reassess their priorities, often resulting in a shift away from long-term research and development initiatives. This shift can have a cascading effect on the artificial intelligence in the genomics market, where continuous innovation is critical for staying competitive. Reduced funding and resources may lead to delays in the development of new AI applications and technologies that could potentially revolutionize genomics research and personalized medicine. In times of economic uncertainty, collaboration and partnerships become pivotal for the survival and growth of businesses. The market may witness increased collaboration between industry players, research institutions, and healthcare providers as they pool resources to weather the recession.
Table of Contents – Major Key Points
1. Introduction
2. Research Methodology
3. Market Dynamics
4. Impact Analysis
5. Value Chain Analysis
6. Porter’s 5 forces model
7. PEST Analysis
8. Artificial Intelligence In Genomics Market Segmentation, By Offering Type
9. Artificial Intelligence In Genomics Market Segmentation, By Technology
10. Artificial Intelligence In Genomics Market Segmentation, By Functionality
11. Artificial Intelligence In Genomics Market Segmentation, By Application
12. Artificial Intelligence In Genomics Market Segmentation, By End User
13. Regional Analysis
14. Company Profiles
15. Competitive Landscape
16. Conclusion
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