AI In Genomics Market to Grow at 9.72% CAGR though 2029
The growing usage of AI in rare disease research and technological advancements is expected to drive the Global AI In Genomics Market growth in the forecast period, 2025-2029.
According to
TechSci Research report, “AI In Genomics Market – Global Industry Size,
Share, Trends, Competition Forecast & Opportunities, 2029F”, the
Global AI In Genomics Market stood 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. The rapid expansion of the Global AI in Genomics Market is being fueled by its potential to expedite genomic research and drug discovery processes, ultimately leading to more tailored and efficacious medical interventions. The growing accessibility of extensive genomic datasets and advancements in machine learning methodologies are driving the uptake of AI in deciphering intricate genetic insights. AI models have the capability to simulate interactions between drugs and proteins, thereby forecasting the efficacy and potential adverse effects of drug candidates. This not only accelerates the identification of potential therapies but also heightens the probability of success in clinical trials. In the face of mounting pressure on the pharmaceutical industry to deliver innovative treatments swiftly, AI-driven genomics confers a strategic advantage.
The swift progressions in machine learning and AI techniques play a pivotal role in propelling the AI in genomics market forward. As AI technologies evolve, so too do their capacities to process and interpret intricate genomic data. Convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other deep learning architectures are increasingly being deployed in genomics, enabling the discernment of subtle patterns and correlations within genetic data. Transfer learning, a method wherein AI models trained on one task are applied to a different yet related task, represents another realm of advancement. This approach empowers AI models to leverage knowledge acquired from one genomic dataset to make predictions in another, even when data volumes are limited. These AI methodologies are broadening the horizons for uncovering meaningful insights from genomic data and fostering innovation in healthcare.
The successful integration of AI in genomics necessitates collaboration among experts from diverse domains, encompassing genetics, computer science, and healthcare. Biologists and geneticists furnish domain expertise, while AI specialists devise algorithms capable of effectively scrutinizing and interpreting complex genomic data. This trend underscores the significance of interdisciplinary partnerships to fully harness the potential of AI in advancing genomics research and applications.
AI methodologies are enhancing the precision and efficiency of genomic sequencing procedures. These algorithms have the capacity to pinpoint errors in sequencing data, refine alignment accuracy, and facilitate the detection of structural variations within genomes. By curtailing sequencing errors and optimizing data quality, AI contributes to the production of more reliable and comprehensive genomic information, critical for precise medical diagnoses and research pursuits. Furthermore, the convergence of AI and genomics empowers individuals to take charge of their health. AI-powered platforms can furnish individuals with insights into their genetic predispositions, aiding them in making informed decisions regarding lifestyle choices, disease prevention, and early intervention. This trend fosters a transition towards proactive healthcare, wherein patients actively engage with their genetic information to optimize their well-being.
While the potential of AI in genomics is immense, its seamless integration into clinical workflows poses a challenge. Healthcare providers operate within intricate systems that mandate adherence to stringent regulations, standardized practices, and established protocols. Introducing AI-driven genomics into these workflows necessitates meticulous consideration of regulatory approvals, data sharing agreements, and integration with electronic health records. Healthcare professionals may exhibit caution towards adopting AI tools without a clear comprehension of their benefits, limitations, and ramifications. Endeavors to bridge the gap between AI developers and healthcare practitioners through education, training, and collaboration can facilitate the successful integration of AI-driven genomics into clinical settings.
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The Global AI In
Genomics Market is segmented into component, technology, functionality, application,
end use, regional distribution, and company.
Based on Component, Software
have emerged as the dominating segment in the Global AI In Genomics Market in 2023. This surge is primarily attributed to the pivotal role played by advanced algorithms and bioinformatics tools in uncovering insights from genomic data. Software solutions offer a broad spectrum of functionalities, ranging from genomic sequence analysis, variant calling, pathway analysis, to predictive modeling. These sophisticated software platforms harness AI and machine learning algorithms to scrutinize intricate genomic datasets, discern patterns, and derive actionable insights that inform research, drug discovery, and clinical decision-making. By enabling seamless integration and interoperability across various genomic data sources, software solutions facilitate collaboration among researchers and healthcare providers. The scalability and adaptability of these platforms empower users to tailor analytical workflows, stay abreast of evolving research paradigms, and tackle specific research or clinical challenges with precision and efficiency.
Based on Region, Europe have emerged as the fastest growing region in the Global AI in Genomics Market in 2023. Europe's leadership in this domain is supported by various key factors. The region benefits from a robust research infrastructure, which includes globally recognized academic institutions, research centers, and biotechnology firms. These entities collaborate closely to stimulate innovation and propel genomics forward through AI-driven methodologies. Moreover, Europe boasts a sophisticated healthcare ecosystem characterized by comprehensive healthcare coverage, advanced digital health technologies, and extensive genomic data repositories. This favorable environment facilitates large-scale genomic research initiatives, population-wide biobanking endeavors, and precision medicine programs that utilize AI to decode the genetic foundations of diseases and guide personalized treatment approaches. Europe's proactive regulatory framework and ethical guidelines pertaining to genomic research and data protection cultivate public trust and confidence in AI-driven genomics applications. This regulatory clarity promotes the seamless integration of AI technologies into clinical practice and expedites the translation of research findings into actionable insights for patient care.
Major companies
operating in Global AI In Genomics Market are:
- 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.
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“The future of the AI in genomics market is bright, driven by technological advancements and increasing demand for precision medicine. Integration of AI with genomics is set to revolutionize biomedical research, clinical diagnostics, and therapeutic development. A key trend is multimodal AI, combining genomic data with other omics datasets and clinical records for deeper insights. AI-driven genomics in clinical settings will personalize treatment strategies, aided by decision support tools and predictive analytics. Advancements in AI algorithms and cloud computing will fuel innovation in genomic data analysis, leading to novel discoveries and improved patient outcomes worldwide”, said Mr. Karan Chechi, Research Director of TechSci Research, a
research-based management consulting firm.
“AI In Genomics Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented By Component (Hardware, Software, Services), By Technology (Machine Learning {Deep Learning, Supervised Learning, Unsupervised Learning, Others}, Computer Vision), By Functionality (Genome Sequencing, Gene Editing, Others), By Application (Drug Discovery & Development, Precision Medicine, Diagnostics, Others), By End Use (Pharmaceutical and Biotech Companies, Healthcare Providers, Research Centers, Others), By Region and Competition, 2019-2029F”, has evaluated the future growth
potential of Global AI In Genomics Market and provides statistics &
information on market size, structure and future market growth. The report
intends to provide cutting-edge market intelligence and help decision makers
take sound investment decisions. Besides, the report also identifies and analyzes
the emerging trends along with essential drivers, challenges, and opportunities
in Global AI In Genomics Market.
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