DUBLIN–(BUSINESS WIRE)–The “AI In Pharma Global Market Report 2022, By Technology, Drug Type, Application” report has been added to ResearchAndMarkets.com’s offering.
This report provides strategists, marketers and senior management with the critical information they need to assess the global ai in pharma market.
This report focuses on ai in pharma market which is experiencing strong growth. The report gives a guide to the ai in pharma market which will be shaping and changing our lives over the next ten years and beyond, including the markets response to the challenge of the global pandemic.
The global AI in pharma market is expected to grow from $934.56 million in 2021 to $1,236.09 million in 2022 at a compound annual growth rate (CAGR) of 32.3%. The change in growth trend is mainly due to the companies stabilizing their output after catering to the demand that grew exponentially during the COVID-19 pandemic in 2021. The market is expected to reach $3626.24 million in 2026 at a CAGR of 30.9%.
Companies Mentioned
- Novartis
- IBM Watson
- Microsoft Corporation
- Merck
- GNS Healthcare
- AstraZeneca
- Atomwise Inc.
- Exscientia
- Cyclica
- NVIDIA Corporation
- XtalPi Inc.
- BenevolentAI
- Cloud Pharmaceuticals Inc.
- BERG LLC
- Bayer
- Pfizer
- Recursion Pharmaceuticals Inc.
- Bristol-Myers Squibb
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The 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 useful in handling data and presenting results that encourage better decision-making and save human efforts, cost, and time. The market consists of revenue generated by artificial intelligence (AI) companies, which are applying in the pharmaceutical industry. Only goods and services traded between entities or sold to end consumers are included.
The main technology types of artificial intelligence (AI) in pharma are context-aware processing, natural language processing, querying method, deep learning. Natural language processing is a a branch of artificial intelligence (AI), concerned with giving computers the ability to understand text and spoken words in the same way that humans do. The various drugs produced are small molecule and large molecules that are used for diagnosis, clinical trial research, drug discovery, research and development and epidemic prediction.
North America was the largest region in the AI in pharma market in 2021. The regions covered in this report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East and Africa.
The growing adoption of artificial intelligence (AI) for clinical trials research is driving its demand for AI in the pharmaceutical market. To bring a new drug to the market takes on an average 10-15 years approximately and half of this time is consumed during the clinical trial phases of the drug development cycle. Hence, using AI models and analytics tools can accelerate the clinical trial phases, perception of diseases, identify suitable cases and key investigators to inform site selection, and support novel clinical study designs.
Lack of skilled professionals may hamper the growth of artificial intelligence in the pharma market. In 2019, Indeed.com revealed that AI job postings increased 29.1% over the last year and yet, with no uncertainty due to the AI skills gap, searches for AI-related roles decreased by 14.5% over the same period. The shortage of AI skills is seen as a major barrier to the pace of technology’s adoption. In fact, 56% of senior AI professionals thought that a lack of additional, qualified AI workers was the single most important hurdle to be overcome in terms of obtaining the necessary level of AI implementation beyond business operations. Therefore, the lack of skilled professionals may hamper the growth of AI in the pharma market.
Companies operating in the AI for the pharma market are introducing new AI tools that will be beneficial for the pharma companies and this is a key trend in AI in the pharma market. AI helps in quality control, predictive maintenance, waste reduction, design optimization and process automation.
For more information about this report visit https://www.researchandmarkets.com/r/smt05o
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