Global AI in GenomicsMarket
The global AI in genomicsmarket is estimated to be worth over USD8Bnin 2033 and is expected to grow at CAGR of32.0% during the forecast period (2024-2033).
The global AI in genomics market is rapidly expanding, fuelled by the growing need for advanced tools to process and assess massive amounts of genomic data. Key market drivers include rapid advancements in AI technologies, such as machine learning (ML) and deep learning, which substantiallyimprove the speed and accuracy of genomic data analysis. In addition, the increasing demand for precision medicine and the acceleration of the drug discovery process are propelling the adoption of AI in genomics, as AI can identify genetic mutations and potential drug targets more efficiently than conventional methods.
A significant market opportunity lies in the development of human-aware AI systems, which prioritize ethical considerations, data privacy, and transparency. These systems are designed to improve trust in AI technologies by providing more intuitive, transparent, and secure genomic analysis solutions. As concerns over data privacy and security continue to grow, human-aware AI systems are becoming crucial for broader adoption in clinical and research settings. Furthermore, the growing focus on personalized genomics offers opportunities for AI-driven innovations in areas such as oncology, rare disease treatment, and preventive healthcare.
Latest developments comprise the incorporation of AI with cloud-based genomics platforms, allowing for more scalable, cost-effective, and accessible genomic data processing. This trend is facilitating global collaboration and making advanced AI tools available to a wider audience. Additionally, AI's role in personalized medicine continues to grow, with AI algorithms helping to tailor treatment plans based on individual genetic profiles, particularly in oncology and pharmacogenomics.
In summary, the global AI in genomics market is poised for robust growth, with advancements in AI technologies, personalized medicine, and human-aware AI systems leading the way. These innovations are transforming how genetic data is analysed and applied in healthcare, offering more precise and efficient solutions for disease diagnosis, treatment, and prevention.
The market report presents an in-depth analysis, highlighting the capabilities of various stakeholders engaged in this industry, across different geographies. Amongst other elements, the market report includes:
A preface providing an introduction to the full report, AI in Genomics market, 2023-2033.
An outline of the systematic research methodology adopted to conduct the study on AI in Genomics market, providing insights on the various assumptions, methodologies, and quality control measures employed to ensure accuracy and reliability of our findings.
An overview of economic factors that impact the overall AI in Genomics market, including historical trends, currency fluctuation, foreign exchange impact, recession, and inflation measurement.
An executive summary of the insights captured during our research, offering a high-level view of the current state of the AI in Genomics market and its likely evolution in the mid-to-long term.
A brief introduction to the AI in Genomics, highlighting their historical background, as well as information on their types, key aspects, key challenges and the advantages of using AI in Genomics.
A detailed assessment of the market landscape of AI in Genomics that are either approved or being evaluated in different stages of development, based on several relevant parameters, such as 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). Further, the chapter features analysis on key niche market segments. In addition, the chapter features analysis of various AI in Genomics developers, based on their year of establishment, company size, location of headquarters and most active players.
An in-depth analysis of partnerships and collaborations that have been inked between various stakeholders, since 2019, based on several relevant parameters, such as the year of partnership, type of partnership, focus of partnership, purpose of partnership, therapeutic applications and most active players (in terms of number of partnerships). It also highlights the regional distribution of partnership activity in this market.
A detailed analysis of various investments made by companies engaged in this industry, since 2019, based on several relevant parameters, such as year of funding, type of funding (grants, seed, venture capital, initial public offering, secondary offerings, private equity and debt financing), type of HPAPIs, amount invested, geography, purpose of funding, stage of development, therapeutic area, most active players (in terms of number and amount of funding instances) and leading investors (in terms of number of funding instances).
An in-depth analysis of the various AI in Genomics focused initiatives undertaken by big market players, based on several relevant parameters, such as number of initiatives, year of initiative, type of initiative, purpose of initiative, focus of initiative and location of headquarters of the big pharma players.
One of the key objectives of this market report was to estimate the current market size and the future growth potential of the AI in Genomics over the forecast period. Based on several parameters, such as regional analysis as well as segmental analysis rates, we have developed informed estimates of the likely evolution of the AI in Genomics market over the forecast period 2023-2033. Our year-wise projections of the current and future opportunity have further been segmented based on relevant parameters, such as 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 key geographical regions (North America, Europe, Asia-Pacific, Middle East and Africa, and South America) and leading players. In order to account for future uncertainties associated with some of the key parameters and to add robustness to our model, we have provided three market forecast scenarios, namely conservative, base, and optimistic scenarios, representing different tracks of the industry’s evolution.