The Business Research Company’s Machine Learning (ML) in Pharmaceutical Industry market research report offers global market size, growth rate, regional shares, competitive analysis, detailed segments, trends and opportunities.
What is the size of the global machine learning (ML) market in the pharmaceutical industry?
The machine learning market in the pharmaceutical industry is expected to grow exponentially in the coming years. It will reach $10.2 billion by 2028, at a compound annual growth rate (CAGR) of 35.7%. The growth during the forecast period can be attributed to the increasing complexity of biological data, increasing computing power, growing industry awareness and education, and patient-centric healthcare solutions. Key trends during the forecast period include AI-pharma partnerships and collaborations, drug-agnostic therapies, interoperability of ML systems, decentralized clinical trials, and AI-driven biomarker discovery.
Top Companies in the Machine Learning (ML) Market in the Pharmaceutical Industry
The major companies operating in the field of machine learning (ML) in the pharmaceutical industry market report are Amazon.com Inc., Alphabet Inc., Microsoft Corporation, Dell Technologies Inc., Hitachi Ltd., International Business Machines Corporation, Cisco Systems Inc., Oracle Corporation, Honeywell International Inc., Hewlett Packard Enterprise, NVIDIA Corporation, Thales SA, Atos SE, Hexagon AB, Palantir Technologies Inc., Verient Systems Inc., Alteryx Inc., Comet ML Inc., GAVS Technologies, NEC Corporation, Veritone Inc., H2O.ai Inc., Sparkcognition Inc., Akira AI, Deep Genomics Inc., Cloud Pharmaceuticals Inc., Atomwise Inc., Cyclica Inc., BioSymetrics Inc., Neptune Labs
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Machine Learning (ML) in the Pharmaceutical Industry: Market Drivers and Trends with Examples
The growing adoption of artificial intelligence (AI) is driving the integration of machine learning (ML) in the pharmaceutical industry. AI and ML technologies are transforming drug discovery and delivery processes by providing rapid solutions to complex challenges and enabling more effective decision-making. The widespread adoption of AI in UK businesses in 2023 underscores its growing importance across a range of industries, including pharmaceuticals. To enhance drug discovery capabilities, companies are developing user-friendly software platforms, such as Merck & Co.’s AIDDISON, which integrates AI, ML and computer-aided drug design to more efficiently identify and evaluate potential drug candidates.
Machine Learning (ML) Market Segmentation in the Pharmaceutical Industry
The Machine Learning (ML) Market in Pharmaceutical Industry covered in this report is segmented –
1) By Component: Solution, Services
2) By component: Cloud, on-premises
3) By company size: small and medium-sized enterprises (SMEs), large companies
Table of Contents
1. Introduction
• Machine Learning (ML) Market Overview in the Pharmaceutical Industry
• Comprehensive analysis of machine learning (ML) in the pharmaceutical industry market
2.
Machine Learning (ML) in the Pharmaceutical Industry: Market Drivers and Restraints
• Driving factors and constraints for the historical and forecast period
3. Machine Learning (ML) in Pharmaceutical Industry Market Segmentation
• By component
• By component
• By company size
4. Competitive landscape and company profiles in the machine learning (ML) market in the pharmaceutical industry
• Preview
• Products and services
• Strategy
5. Regional and Country Analysis of Machine Learning (ML) Market in Pharmaceutical Industry
• By region
• By country
6. Machine Learning (ML) Market Trends and Opportunities in the Pharmaceutical Industry
• Technological progress
• Emerging trends
7. Machine Learning (ML) in Pharmaceutical Industry Future Outlook and Market Potential Analysis
• Growth strategies
• New opportunities
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Machine learning (ML) in the pharmaceutical industry market refers to the use of artificial intelligence (AI) algorithms and techniques. They are used to improve various aspects of drug discovery, development, and manufacturing processes. Implementing ML solutions in the pharmaceutical industry requires rigorous regulatory compliance and consideration of potential challenges related to data quality, research gaps, and regulatory uncertainty.
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