Artificial Intelligence in Pharmaceutical Market SWOT
The latest study released on the Global Artificial Intelligence in Pharmaceutical Market by HTF MI Research evaluates market size, trend, and forecast to 2029. The Artificial Intelligence in Pharmaceutical market study covers significant research data and proofs to be a handy resource document for managers, analysts, industry experts and other key people to have ready-to-access and self-analysed study to help understand market trends, growth drivers, opportunities and upcoming challenges and about the competitors.
Key Players in This Report Include:
IBM Watson Health (United States), Google Health (United States), Microsoft (United States), NVIDIA Corporation (United States), Deep Genomics (Canada), Insilico Medicine (United States), BenevolentAI (United Kingdom), Recursion Pharmaceuticals (United States), Atomwise (United States), Berg Health (United States), Tempus (United States)
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“According to HTF Market Intelligence, the Global Artificial Intelligence in Pharmaceutical market is expected to see a growth rate of 29.5% and may see market size of USD 3.6 Billion by 2029, currently pegged at USD 1.2 Billion.”
Definition:
Artificial intelligence (AI) in the pharmaceutical industry refers to the application of advanced computational algorithms and machine learning techniques to enhance various aspects of drug discovery, development, manufacturing, and healthcare delivery. AI technologies analyse vast amounts of data, extract patterns, make predictions, and generate insights that can aid in decision-making processes and improve overall efficiency and effectiveness in the pharmaceutical sector. The integration of AI in pharmaceuticals has the potential to revolutionize the industry by accelerating the drug discovery and development process, optimizing clinical trials, improving patient outcomes, and reducing costs. AI algorithms can analyse large databases of chemical and biological information to identify potential drug candidates. Machine learning models can predict the efficacy, safety, and potential side effects of drug compounds, thereby narrowing down the selection process. AI techniques can identify and prioritize potential drug targets by analysing genetic and molecular data. This helps researchers understand disease mechanisms and develop targeted therapies.
Major Highlights of the Artificial Intelligence in Pharmaceutical Market report released by HTF MI
Global Artificial Intelligence in Pharmaceutical Market Breakdown by Application (Drug Discovery and Development, Precision Medicine and Personalized Treatment, Clinical Trials Optimization, Regulatory Compliance and Drug Safety, Supply Chain Optimization, Others) by Type (Cloud-Based, On-Premise) by End-User (Biotechnology Companies, Contract Research Organizations (CROs), Academic and Research Institutions, Others) and by Geography (North America, South America, Europe, Asia Pacific, MEA)
Global Artificial Intelligence in Pharmaceutical market report highlights information regarding the current and future industry trends, growth patterns, as well as it offers business strategies to help the stakeholders in making sound decisions that may help to ensure the profit trajectory over the forecast years.
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Geographically, the detailed analysis of consumption, revenue, market share, and growth rate of the following regions:
• The Middle East and Africa (South Africa, Saudi Arabia, UAE, Israel, Egypt, etc.)
• North America (United States, Mexico & Canada)
• South America (Brazil, Venezuela, Argentina, Ecuador, Peru, Colombia, etc.)
• Europe (Turkey, Spain, Turkey, Netherlands Denmark, Belgium, Switzerland, Germany, Russia UK, Italy, France, etc.)
• Asia-Pacific (Taiwan, Hong Kong, Singapore, Vietnam, China, Malaysia, Japan, Philippines, Korea, Thailand, India, Indonesia, and Australia).
Objectives of the Report
• -To carefully analyse and forecast the size of the Artificial Intelligence in Pharmaceutical market by value and volume.
• -To estimate the market shares of major segments of the Artificial Intelligence in Pharmaceutical
• -To showcase the development of the Artificial Intelligence in Pharmaceutical market in different parts of the world.
• -To analyse and study micro-markets in terms of their contributions to the Artificial Intelligence in Pharmaceutical market, their prospects, and individual growth trends.
• -To offer precise and useful details about factors affecting the growth of the Artificial Intelligence in Pharmaceutical
• -To provide a meticulous assessment of crucial business strategies used by leading companies operating in the Artificial Intelligence in Pharmaceutical market, which include research and development, collaborations, agreements, partnerships, acquisitions, mergers, new developments, and product launches.
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Points Covered in Table of Content of Global Artificial Intelligence in Pharmaceutical Market:
Chapter 01 – Artificial Intelligence in Pharmaceutical Executive Summary
Chapter 02 – Market Overview
Chapter 03 – Key Success Factors
Chapter 04 – Global Artificial Intelligence in Pharmaceutical Market – Pricing Analysis
Chapter 05 – Global Artificial Intelligence in Pharmaceutical Market Background
Chapter 06 – Global Artificial Intelligence in Pharmaceutical Market Segmentation
Chapter 07 – Key and Emerging Countries Analysis in Global Artificial Intelligence in Pharmaceutical Market
Chapter 08 – Global Artificial Intelligence in Pharmaceutical Market Structure Analysis
Chapter 09 – Global Artificial Intelligence in Pharmaceutical Market Competitive Analysis
Chapter 10 – Assumptions and Acronyms
Chapter 11 – Artificial Intelligence in Pharmaceutical Market Research Methodology
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Key questions answered
• How feasible is Artificial Intelligence in Pharmaceutical market for long-term investment?
• What are influencing factors driving the demand for Artificial Intelligence in Pharmaceutical near future?
• What is the impact analysis of various factors in the Global Artificial Intelligence in Pharmaceutical market growth?
• What are the recent trends in the regional market and how successful they are?
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This release was published on openPR.