AI In Genomics Market projected to reach USD 5.6 Billion by 2030, growing at a CAGR of 35.3% during the forecast period of 2023-2030

The Global AI In Genomics Market was valued at USD 0.5 Billion in 2022 and is projected to reach USD 5.6 Billion by 2030, growing at a CAGR of 35.3% during the forecast period of 2023-2030.

𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐭𝐡𝐞 𝐒𝐚𝐦𝐩𝐥𝐞 -
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AI, an intelligent system, applies human-like reasoning, learning, and problem-solving in fields like healthcare, computer science, math, and engineering. Its integration in healthcare automates tasks, analyzes patient data, and enhances care efficiency and cost-effectiveness, aiding disease diagnosis and gene variation identification. In genomics, AI encompasses on-premises, cloud, and web-based software and services. It's pivotal in genome sequencing, editing, pharmacogenomics, and gene testing, speeding drug discovery, precision medicine, diagnostics, and more. Growth drivers include cost control, increased AI investments, and precision medicine adoption. Collaboration, investments, and improved computing power also contribute to market expansion, alongside the burgeoning bioinformatics and genomic data.

Major vendors in the global AI In Genomics Market - IBM, Microsoft, NVIDIA, Deep Genomics, DATA4CURE, Freenome holdings, Inc, Thermo fisher scientific, Illumina Inc, Sophia Genetics, BenevolentA, verge genomics, Molecular match Inc, Tempus, Fabric Genomics, Congenica Ltd, BGI Genomics, QIAGEN, Seven Bridges Genomics, PierianDx, DNAnexus Inc and Others.

𝐓𝐫𝐢𝐦 𝐃𝐫𝐮𝐠 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐀𝐧𝐝 𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲 𝐄𝐱𝐩𝐞𝐧𝐬𝐞𝐬, 𝐀𝐧𝐝 𝐌𝐞𝐞𝐭 𝐓𝐢𝐠𝐡𝐭𝐞𝐫 𝐓𝐢𝐦𝐞𝐥𝐢𝐧𝐞𝐬.

Drug discovery is an expensive and time-consuming process, necessitating the search for alternative tools. Current methods, both in vivo and in vitro, are costly, and it typically takes about a decade and around $2.6 billion for a new drug to reach the market. Only a small fraction of compounds, about 1 in 5,000-10,000, become potential drugs for specific conditions. Many drug candidates identified in the discovery phase fail in later stages due to issues like toxicity. Machine learning can predict drug compound outcomes early in the discovery phase, reducing time and costs significantly.

This potential for time and cost savings has garnered considerable attention, leading to various investigative projects. For example, in November 2020, Deep Genomics and BioMarin collaborated to discover oligonucleotide drug candidates for rare diseases, using Deep Genomics' AI Workbench platform alongside BioMarin's rare disease expertise. AI in genomics for drug discovery holds the promise of expediting the drug development process, cutting costs, and improving patient outcomes through the development of more targeted and effective drugs.

𝐌𝐚𝐫𝐤𝐞𝐭 𝐃𝐲𝐧𝐚𝐦𝐢𝐜𝐬

𝐃𝐫𝐢𝐯𝐞𝐫𝐬:
The Adoption Of AI In Precision Medicine Is On The Rise.
AI Is Increasingly Applied In Genomics, Accompanied By A Surge In Genomics Investment.
AI In Genomics Are Reduce The Cost Of Sequencing

𝐎𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐢𝐞𝐬:
Increased Funding & Investment.
Increasing Advancements In Drug Discovery.

AI was being increasingly utilized to tailor treatments to an individual's genetic makeup.

A significant trend in the AI in genomics field involves the increasing application of artificial intelligence to customize medical treatments based on an individual's genetic information. This personalized medicine approach harnesses AI's power to analyze and interpret genetic data, enabling healthcare providers to deliver highly tailored treatments that are precisely suited to a patient's genetic makeup. By doing so, it holds the potential to enhance treatment effectiveness, minimize adverse reactions, and ultimately improve patient care and outcomes.

𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐒𝐞𝐠𝐦𝐞𝐧𝐭 𝐢𝐬 𝐚𝐧𝐭𝐢𝐜𝐢𝐩𝐚𝐭𝐞𝐝 𝐭𝐨 𝐡𝐨𝐥𝐝 𝐭𝐡𝐞 𝐋𝐚𝐫𝐠𝐞𝐬𝐭 𝐌𝐚𝐫𝐤𝐞𝐭 𝐒𝐡𝐚𝐫𝐞 𝐝𝐮𝐫𝐢𝐧𝐠 𝐭𝐡𝐞 𝐅𝐨𝐫𝐞𝐜𝐚𝐬𝐭 𝐏𝐞𝐫𝐢𝐨𝐝

Machine learning is playing an increasingly significant role in the IoT (Internet of Things) genomics market, primarily due to its capacity to analyze and derive valuable insights from the vast amounts of genomic data generated by connected devices and sensors. By harnessing machine learning algorithms, this sector can enhance personalized medicine, disease prediction, and treatment strategies, ultimately leading to more effective and precise healthcare solutions, driving growth and innovation in the IoT genomics market. ML is being used to develop new drugs and therapies more quickly and efficiently. For example, in 2023, the company DeepMind developed an AI algorithm that can predict the 3D structure of proteins, which is essential for drug discovery.

ML is also being used to develop new and improved diagnostic tools for diseases. For example, in 2023, the company Verily developed an AI-powered blood test that can detect over 50 types of cancer at an early stage.

𝐆𝐫𝐚𝐬𝐩 𝐚 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐨𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐲 𝐢𝐧 𝐀𝐈 𝐈𝐧 𝐆𝐞𝐧𝐨𝐦𝐢𝐜𝐬 𝐌𝐚𝐫𝐤𝐞𝐭. 𝐓𝐨 𝐛𝐮𝐲 𝐭𝐡𝐢𝐬 𝐫𝐞𝐩𝐨𝐫𝐭 @
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𝐑𝐞𝐜𝐞𝐧𝐭 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭𝐬
In June 2022, Ultima Genomics Collaborates with NVIDIA To Deliver $100 Genome Sequencing with AI and Accelerated Computing. Ultima Genomics, Inc. today unveiled a broad collaboration with NVIDIA that leverages AI-accelerated analysis across multiple stages of the DNA sequencing and analysis process. The collaboration includes the use of NVIDIA GPUs for deep learning in photometry and base calling and will soon include acceleration of variant calling.
In October 2022, GRAIL has unveiled the Galleri® test, a groundbreaking MCED (Multi-Cancer Early Detection) test that is commercially accessible. This innovative test can identify a common signal present in numerous cancer types, even those that aren't typically screened for, all from a single blood sample. MCED testing enables individuals to be screened for this shared signal across over 50 different cancer types, significantly improving the likelihood of early cancer detection.

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