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Upcoming Allied Market Research
2021
Ai In Pharma Market

AI in Pharma Market by Component Type (Software and Services), Technology Type (Machine Learning, Others Technologies), and Application (Diagnosis, Clinical Trial Research, Drug Discovery, Research and Development, Epidemic Prediction), and End User (Pharmaceutical & Biotechnology Companies, Contract Research Organizations, and Research Centers and Academic & Government Institutes): Global Opportunity Analysis and Industry Forecast, 2021–2030

A12694
Pages: NA
Oct 2021 | 127 Views
   
Author(s) : Vineet Kumar
Tables: NA
Charts: NA
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COVID-19

Pandemic disrupted the entire world and affected many industries.

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AI in Pharma Market Statistics: 2030

Artificial intelligence (AI) in pharma market consists of sales of AI in the pharma and related services that use an automated algorithm to perform tasks that traditionally rely on human intelligence. It can be used to handle data and present results that encourage better decision-making and save human efforts, cost, and time. The market consists of revenue generated by AI companies, which are applied in the pharmaceutical industry.

Report Matric

Details

 Market size available for the years

2020-2030

 Base year considered

2020

 Forecast period

2021-2030

 Forecast unit

Value (USD)

 Segments covered

  • Component Type
  • Technology
  • Application
  • End User
  • Region

 Regions covered

  • North America (U.S. and Canada),
  • Europe (Germany, UK, France, and the Rest of Europe),
  • Asia-Pacific (China, Japan, India, and the Rest of Asia-Pacific).
  • LAMEA (Latin America, Middle East, and Africa)

 Companies covered

 

  • IBM Watson
  • Microsoft Corporation
  • Merck
  • GNS Healthcare
  • NVIDIA Corporation
  • XtalPi Inc.
  • Benevolent AI
  • Cloud Pharmaceuticals Inc.
  • BERG LLC
  • Google
  • AstraZeneca
  • Atom wise Inc.
  • Exscientia
  • Cyclica
  • Others

 


COVID-19 scenario Analysis:

  • The emergence of COVID-19 pandemic has affected every industry except pharma industry across the world. Further, it is expected that growth of Pharma is mainly due to the companies resuming their operations and adapting to the new norm while recovering from the COVID-19 impact, which had earlier led to restrictive containment measures involving social distancing, remote working, and the closure of commercial activities that resulted in operational challenges.
  • The outbreak of COVID-19 pandemic has moderately inhibited the growth of pharma industries owing to financial impact, production and demand gap, or supply chain and market disruption.
  • In addition, the artificial intelligence for drug discovery (pharma) and healthcare market have grown significantly.
  • Furthermore, there are strong indications that the current pandemic has pushed the demand for technology and digital solutions. In the medium-to-long run the COVID-19 technology impact may be extremely positive. Everyone is forced to embrace new technologies and discover their benefits. Additional investments in digital infrastructure can be expected.

Top impacting factors: Market Scenario Analysis, Trends, Drivers, and Impact Analysis

The increase in number of cross-industry collaborations and partnerships, along with the rise in requirement to control drug discovery & development costs, are the two significant factors that are responsible for the growth of the global artificial intelligence in the drug discovery market. Moreover, the surge in adoption of cloud-based applications and services and the impending patent expiry of blockbuster drugs further drive the growth of the AI in pharma market.

Adoption of artificial intelligence (AI) in the drug discovery market

Artificial intelligence (AI) technology integration in the drug discovery is steadily increasing, and many pharmaceutical companies are highly focusing on drug development using artificial intelligence. The pharmaceutical companies are taking advantage of artificial intelligence technology to discover the drugs for the most complex diseases such as precision’s disease, Alzheimer’s disease, and pancreatic cancer. Artificial intelligence helps the pharmaceutical companies to streamline the research and development activities for the personalized medicines and the complex drug discovery.

Rise in demand to reduce drug discovery time

The drug discovery has become increasingly competitive and expensive, which has driven pharmaceutical companies to look into AI as a new method to reduce research and development costs, while avoiding costly errors. AI has a great potential to transform drug discovery by accelerating the research and development timeline, in an effort to make drugs affordable and improve the probability of FDA approval. This technology can also help with the repurposing new drugs, especially during the COVID-19 pandemic. AI and machine learning algorithms are able to identify molecules that may have failed in clinical trials and predict how the same compounds could be applied to target other diseases.

Key Segments covered:

Segments

Sub-segments

 Component Type

  • Software
  • Services

 Technology

  • Machine Learning,
  • Others Technologies

 Application

  • Diagnosis
  • Clinical Trial Research
  • Drug Discovery
  • Research and Development
  • Epidemic Prediction

 End User

  • Pharmaceutical & Biotechnology Companies
  • Contract Research Organizations
  • Research Centers and Academic & Government Institutes


Key benefits of the report:

  • This study presents the analytical depiction of the global AI in pharma market along with the current trends and future estimations to determine the imminent investment pockets.
  • The report presents information related to key drivers, restraints, and opportunities along with detailed analysis of global AI in pharma market share.
  • The current market is quantitatively analyzed from 2020 to 2030 to highlight the global AI in pharma market growth scenario.
  • Porter’s five forces analysis illustrates the potency of buyers & suppliers in the AI in pharma industry.
  • The report provides detailed global AI in pharma market analysis based on competitive intensity and how the competition will take shape in coming years.
 
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