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Artificial Intelligence in Genomics Market

Global artificial intelligence in genomics market was evaluated at US$ XX billion in 2023 and is expected to attain around US$ XX billion by 2033, growing at a CAGR of XX% from 2024 to 2033. The integration of artificial intelligence (AI) has emerged as a significant growth factor in the field of drug development, drived by the exponential expansion of biomedical data facilitated by cutting-edge experimental techniques. AI technologies enable computer systems to undertake tasks traditionally requiring human intelligence, facilitating enhanced decision-making processes. Notably, advancements in AI software and hardware, particularly deep learning algorithms and graphics processing units (GPUs), have sparked a surge in medical AI applications. In clinical diagnostics, AI-driven computer vision approaches are poised to transform image-based diagnostics, while other AI subtypes demonstrate similar potential across various diagnostic modalities.

Market Overview
In the genomics, the integration of artificial intelligence (AI) has cause rapid market growth, particularly in genome sequencing processes. AI technology has revolutionized genome sequencing by enhancing the accuracy and efficiency of nucleotide order determination in DNA. With the widespread adoption of electronic medical records in healthcare facilities, AI-driven machine learning algorithms leverage accessible healthcare data to analyze patient health trajectories and anticipate future events beyond individual doctors' experiences. AI aids in identifying large-scale genomic rearrangements and structural changes, offering comprehensive insights by amalgamating clinical, environmental, lifestyle, and genomic data. Notably, AI enables the scalability of sequencing operations and management of extensive genomic datasets while ensuring compliance with regulatory requirements and data security. Although inspired by human intelligence, AI algorithms target tasks impractical for human execution, thereby optimizing clinical genomic analysis processes. These advancements hold promise for improving various stages of clinical genomic analysis, including variant calling, genome annotation, variant classification, and phenotype-to-genotype correlations, with potential future applications in genotype-to-phenotype predictions.
• In March 2024, APx Acquisition Corp. I, OmnigenicsAI Corp, and MultiplAI Health Ltd entered into a business combination agreement to create a global AI-driven genomics platform.
• In January 2022, Sema4 announced the acquisition of GeneDx, strengthening its market-leading AI-driven genomic and clinical data platform.

Regional Stance
In the global AI in genomics market, North America emerged as the dominant region, showcasing robust advancements in artificial intelligence technology. With its vast potential to enhance every facet of healthcare, AI applications offer accelerated scientific discovery, improved decision-making for healthcare professionals, enhanced medical advice for patients, and streamlined administrative processes. A recent study conducted in the United States underscores the efficacy of AI-based phenotypic risk scoring techniques, revealing previously unidentified monogenic disorders in a significant portion of the adult population studied. These findings highlight the transformative impact of AI-driven phenotype-to-genotype mapping methodologies, poised to significantly enhance genetic testing diagnostic yield and facilitate the identification of individuals with previously undetected genetic diseases.
In the forecast period, Asia Pacific is anticipated to emerge as the fastest-growing market in the field of artificial intelligence (AI), particularly within healthcare. The region holds significant potential for AI to revolutionize healthcare delivery by offering innovative solutions to tackle the complex challenges inherent in its diverse and populous jurisdictions. With a concerted effort to improve healthcare accessibility, affordability, and quality, AI applications are already reshaping the medical services landscape and are expected to continue doing so in the future. While previously, AI in healthcare may have been predominantly associated with research and development activities such as molecular analysis, its applications are now becoming more visible to the general public. AI facilitates the expedited discovery of new medicines, substantially reducing costs and increasing success rates. This transformative potential underscores the pivotal role of AI in advancing healthcare across the Asia Pacific region.

Report Highlights
By Offering
Software sector stands as the driving force in the global AI in genomics market, wielding significant influence through its integration of AI techniques into both proprietary and open-source genomics analysis tools and services. Despite advancements, the majority of AI efforts in genomics remain within the realm of research, with a surge in researchers leveraging machine-learning techniques to grapple with the immense volume of clinical data, thus optimizing costs. The transformative impact of machine learning applications extends across genetic research, clinical practices, and personalized medicine, facilitating a deeper understanding of the interplay between genetics and health outcomes. From DNA sequencing to phenotyping and variation identification, the pervasive influence of machine learning and deep learning is evident across nearly every facet of genomics study.
On the other hand, the services sector is projected to experience rapid growth in the upcoming forecast period. Within genomic analysis, AI algorithms like machine learning (ML) and deep learning (DL) play pivotal roles in processing and deciphering extensive genetic datasets. By leveraging these algorithms, patterns can be identified, predictions made, and genetic variations classified through training on expansive datasets.

By Application
In the AI in genomics market, drug discovery and development, the segment dominated, driven by the dominant application of machine learning algorithms. These algorithms adeptly sift through diverse datasets, ranging from gene expression profiles to protein-protein interaction networks, to pinpoint potential molecules—typically proteins—that could modulate disease states. Leveraging AI-based methods across various stages of the drug development process, from identifying novel targets to optimizing small molecule compounds, not only streamlines the process but also holds promise in identifying prognostic biomarkers and elucidating patterns of drug efficacy and resistance, thus shaping the future of pharmaceutical innovation.
Precision medicine emerges as the most promising segment in the forecast period, driven by its transformative impact on medical care. Departing from conventional symptom-driven treatments, precision medicine facilitates early disease risk prediction, improved diagnostics, and the customization of highly effective treatments. A meticulous examination of comprehensive patient data alongside broader factors enables the differentiation between healthy and ill individuals, paving the way for tailored precision medicine approaches. This integration of precision and genomic medicine with artificial intelligence holds immense potential in enhancing patient healthcare outcomes by providing a nuanced understanding of biological indicators and signaling critical health changes.

By End User
Pharmaceutical and biotech companies segment emerges as the dominant force in the global AI in genomics market, showcasing significant strides in leveraging AI for clinical genomics applications. Among these, the most promising avenues include AI-driven extraction of deep phenotypic information from diverse sources such as images, electronic health records (EHRs), and medical devices, which in turn inform downstream genetic analysis. Moreover, deep-learning algorithms exhibit remarkable potential across various clinical genomics tasks, including variant calling, genome annotation, and functional impact prediction..
Source: https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-019-0689-8
The healthcare providers segment is poised for rapid growth in the forecast period, driven by the integration of AI to augment the interpretability of genomic data and translate it into actionable clinical insights. The focus lies on improving disease diagnosis, optimizing medication selection, and minimizing side effects while maximizing efficacy. By harnessing AI and genomic data, healthcare providers seek to uncover new insights into prevalent diseases, paving the way for personalized treatments and innovative analytics tools. This strategic integration underscores the transformative potential of AI in revolutionizing healthcare delivery and patient outcomes.

Market Dynamics
Driver
Precision Medicine and AI Convergence
The evolution of precision medicine in healthcare is reshaping traditional symptom-driven treatment approaches, enabling early disease risk prediction through enhanced diagnostics and tailored therapeutic interventions. Integration of precision and genomic medicine with artificial intelligence (AI) holds immense potential for improving patient healthcare outcomes. Particularly, patients with unique healthcare needs or less common therapeutic responses are increasingly benefiting from genomic medicine technologies. AI's advanced computational capabilities facilitate insightful decision-making by providing nuanced analysis and inference, thereby enhancing physician decision-making processes. The emergence of genomic medicine as a burgeoning medical specialty, focusing on individual genetic information for diagnostic and therapeutic purposes, coupled with the associated health outcomes and policy implications, is driving the growth of the artificial intelligence in genomics market.
Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9198206/

Restraints
Key Risk Areas for AI Models
Several critical risk areas pose challenges for AI/ML models in genetics, notably data privacy, bias, and model drift. Data privacy concerns center around safeguarding patient confidentiality and preventing compromise, a paramount consideration given the sensitive nature of biological data. Issues such as bias, particularly selection bias resulting from datasets not accurately reflecting the population, can significantly impact the effectiveness and fairness of AI-driven analyses. Furthermore, model drift, where the target population diverges from the dataset used for training, presents ongoing challenges in maintaining model accuracy and relevance over time.

Opportunities
Accelerating Genomic Interpretation with AI
Convergence of artificial intelligence and genomics heralds a transformative era in healthcare, particularly in the realm of genome sequencing data analysis. As sequencing technologies advance, there emerges a significant opportunity for AI to revolutionize the next wave of genomics. Through advanced and rapid computation approaches, AI-powered data interpretation and variant calling can be accomplished in remarkably short timeframes, unlocking new possibilities for accelerated genomic insights. This intersection presents a promising opportunity for the artificial intelligence in genomics market to thrive and cater to the evolving needs of the healthcare industry.

Recent Developments

•    In May 2023, Google Cloud launched AI-powered solutions to safely accelerate drug discovery and precision medicine.
•    In January 2024, QIAGEN announced plans to accelerate investments into QIAGEN Digital Insights bioinformatics business.
•    In October 2022, Illumina launched a strategic research collaboration with AstraZeneca to accelerate drug target discovery.

Key Players in the Artificial Intelligence in Genomics Market
IBM
NVIDIA Corporation
Benevolent AI
Verge Genomics
MolecularMatch, Inc.
SOPHiA GENETICS
PrecisionLife Ltd.
Lifebit
FDNA, Inc.
Empiric Logic
Microsoft
Deep Genomics
Fabric Genomics Inc.
Freenome Holdings, Inc.
Cambridge Cancer Genomics
Data4Cure Inc.
Engine Biosciences Pte. Ltd.
Genoox Ltd.

Market Segmentation
By Offering
Software
Services

By Application
Drug Discovery & Development
Precision Medicine
Diagnostics
Animal Research and Agriculture
Others

By End User
Pharmaceutical & Biotech Companies
Government Organizations
Research Organizations
Others

By Technology
Machine Learning
Deep Learning
Supervised Learning
Reinforcement Learning
Unsupervised Learning
Other
Other Technologies

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

By Geography
North America
U.S.
Canada
Europe
Germany
France
United Kingdom
Rest of Europe
Asia Pacific
China
Japan
India
Southeast Asia
Rest of Asia Pacific
Latin America
Brazil
Rest of Latin America
Middle East & Africa (MEA)
GCC
North Africa
South Africa
Rest of Middle East & Africa

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Artificial Intelligence in Genomics Market

Report ID : 583

Category : Healthcare-Services

No Of Pages : 130

Published on: July 2024

Status: Published

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