The Latin America, Middle East and Africa Machine Learning in Pharmaceutical Industry Market would witness market growth of 41.6% CAGR during the forecast period (2023-2029).
The possible applications of machine learning in the preliminary (early-stage) drug development process range from the initial screening of therapeutic compounds to predicting success rates based on biological parameters. Next-generation sequencing is one example of an R&D discovery technology. The cutting edge in this field appears to be precision medicine, which entails discovering the mechanisms underlying "multifactorial" disorders and, in turn, different therapeutic avenues.
Important players in this field are creating precision medicine research which is centered on creating algorithms to understand better disease processes and design for successful treatment of diseases like Type 2 diabetes. With a present focus on developing a strategy to tailor drug combinations for Acute Myeloid Leukemia, they are leveraging ML technologies in numerous endeavors to build AI technology for cancer precision treatment (AML). The time for optimizing ML in bio-manufacturing for pharmaceuticals has come. Pharmaceutical companies can cut costs and enhance replication by using data from experiments or manufacturing processes to speed up the production of medications.
The Saudi Arabian government will continue increasing its health IT spending. This is due to the government's desire to complete programs like the common e-Health card, digital patient records, digital doctor prescriptions, and digital insurance claims. According to industry sources, updating and modernizing the e-Health infrastructure costs $500 million annually, and if funding is available, that amount will likely rise in the following few years. One objective of the NTP is to ensure that 70% of patients have a digital health record by 2020, up from 0%. The growing adoption of technologies like machine learning and AI in the healthcare sector will aid in the growth of machine learning in pharmaceutical industry market to reduce operational costs and provide prescriptions etc., in LAMEA.
The Brazil market dominated the LAMEA Machine Learning in Pharmaceutical Industry Market by Country in 2022, and would continue to be a dominant market till 2029; thereby, achieving a market value of $180.7 million by 2029. The Argentina market is experiencing a CAGR of 42.3% during (2023 - 2029). Additionally, The UAE market would showcase a CAGR of 41.2% during (2023 - 2029).
Based on Component, the market is segmented into Solution and Services. Based on Deployment Mode, the market is segmented into Cloud and On-premise. Based on Organization size, the market is segmented into Large Enterprises and SMEs. Based on countries, the market is segmented into Brazil, Argentina, UAE, Saudi Arabia, South Africa, Nigeria, and Rest of LAMEA.
Free Valuable Insights: The Worldwide Machine Learning in Pharmaceutical Industry Market is Projected to reach USD 11.4 Billion by 2029, at a CAGR of 34.4%
The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include Google LLC (Alphabet, Inc.), NVIDIA Corporation, IBM Corporation, Microsoft Corporation, Cyclica, Inc., BioSymetrics Inc., Cloud Pharmaceuticals, Inc., Deep Genomics Incorporated and Atomwise, Inc.
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