Artificial Intelligence in Genomics Market Size US$ 16,431.16 Million by 2033

May 15, 2024

19 8 minutes read

The global artificial intelligence in genomics market size was evaluated at US$ 560 million in 2023 and is expected to attain around US$ 16,431.16 million by 2033, growing at a CAGR of 40.2% from 2024 to 2033.

Artificial Intelligence in Genomics Market Size 2024 to 2033

The integration of artificial intelligence (AI) has emerged as a significant growth factor in the field of drug development, driven by the exponential expansion of biomedical data facilitated by cutting-edge experimental techniques.

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Market Overview

In genomics, the integration of artificial intelligence (AI) has caused 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. AI technologies enable computer systems to undertake tasks traditionally requiring human intelligence, facilitating enhanced decision-making processes.

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.

Report Coverage

Details

AI in Genomics Market Size in 2033

USD 16.43 Billion

AI in Genomics Market CAGR

40.2%

U.S. AI in Genomics Market Size in 2033

USD 3.58 Billion

Europe AI in Genomics Market Size in 2033

USD 4.43 Billion

Key Insights

North America dominated the artificial intelligence in genomics market in 2023.

Asia Pacific is estimated to be the fastest-growing region during the forecast period of 2024-2033.

By offering insights, the software segment dominated the market in 2023.

By offering insights, the services segment is estimated to be the fastest-growing region during the forecast period.

By application insights, the drug discovery & development segment dominated the market in 2023.

By application insights, the precision medicine segment is expected to be the most opportunistic segment during the forecast period.

By end-user insights, the pharmaceutical and biotech companies segment dominated the market in 2023.

By end-user insights, the healthcare providers segment is estimated to be the fastest growing during the forecast period.

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U.S. Artificial Intelligence in Genomics Market Size 2024 to 2033

The U.S. AI in genomics market size was valued at USD 114.88 million in 2023 and is expected to hit around USD 3,583.23 million by 2033 with a CAGR of 41.1% from 2024 to 2033.

U.S. Artificial Intelligence in Genomics Market Size 2024 to 2033

Regional Insights

North America dominated the artificial intelligence in genomics market in 2023, 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 region 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 public. AI facilitates the expedited discovery of new medicines, substantially reducing costs and increasing success rates. This transformative potential underscore the pivotal role of AI in advancing healthcare across the Asia Pacific region.

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Report Highlights

By Offering

The software segment stands as the driving force in the global artificial intelligence 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, most 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 segment is projected to experience rapid growth in the 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 artificial intelligence in genomics market, the drug discovery and development segment dominated in 2023, 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.

The precision medicine segment 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 signalling critical health changes.

By End User

The pharmaceutical and biotech companies segment dominated the global artificial intelligence in genomics market in 2023, 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.

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 artificial intelligence in genomics market.

In March 2024, NVIDIA Healthcare launched Generative AI Microservices to advance drug discovery, MedTech, and digital health.

Restraint

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 centre 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.

Opportunity

Accelerating genomic interpretation with AI

The 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, a significant opportunity emerges 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.