Ottawa, March 09, 2022 (GLOBE NEWSWIRE) — The world AI in Pharmaceutical Market Sizing was valued at USD 905.91 million in 2021. The rapid adoption of AI technologies in the pharmaceutical industry across the globe is the major driver for AI in the pharmaceutical market. Rising investment in the biopharmaceutical industry to meet the growing demand for innovative drugs that cure various chronic diseases such as cancer, diabetes, and cardiovascular diseases is exponentially driving the adoption of AI in the pharmaceutical market .
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The use of AI technologies can improve research and development, clinical trials, and drug manufacturing processes, enabling pharmaceutical companies to significantly reduce costs and lead times. The majority of global pharmaceutical players are increasing their investments in the adoption of AI technologies. For example, in April 2021, Abbott introduced its new coronary imaging platform, powered by artificial intelligence, in Europe. It has also been CE marked by relevant authorities in Europe. Hence, the demand for AI technologies in the pharmaceutical industry is expected to increase at a significant rate.
Report scope
| Report cover | Details | |
| Market size in 2021 | $905.91 million | |
| CAGR from 2021 to 2030 | 29.4% | |
| Biggest earner | North America | |
| base year | 2021 | |
| Forecast data | 2022 to 2030 | |
Report Highlights
- Based on the technology, deep learning has dominated the market with more than 33% market share in 2020. This is attributed to the increased adoption of deep learning technology in the diagnosis and monitoring of conditions health. It is widely used in monitoring health conditions and predicting subsequent conditions in the near future. The increasing adoption of this technology to monitor conditions related to various diseases such as sepsis and cardiovascular diseases is further fueling the growth of this segment.
- According to the type of drug, the large molecule is estimated to be the most opportunistic segment. Rising demand for biologics for the treatment of various chronic diseases, coupled with growing prevalence of diseases, rising healthcare expenditures, and growing awareness of biologic drugs is expected to drive the market growth.
- Based on application, the drug discovery segment accounted for approximately 80% of the market share in 2020. AI technologies facilitate cost reduction, time savings and increased operational efficiency in the drug discovery process, which increased the growth of this segment. .
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Regional overview
North America secured a market share of around 43% in 2020. North America is characterized by higher demand for biopharmaceuticals and biologic drugs owing to the increased prevalence of various chronic diseases and the increase in health expenditure. This creates enormous pressure on biopharmaceutical companies to manufacture innovative drugs at a rapid pace. AI technologies can significantly boost the research, drug discovery and drug manufacturing process by reducing the time consumption and costs involved in drug development. Additionally, the regulatory framework in the United States is encouraging the healthcare and pharmaceutical industries to increasingly adopt AI-powered technologies to increase operational efficiency and reduce costs. These factors had resulted in the region’s dominance in AI in the pharmaceutical market.
On the other hand, Asia-Pacific is estimated to be the most opportunistic segment during the forecast period. This is attributed to the growing presence of CROs and CMOs in countries like China, India, South Korea, and Singapore. Government policies aimed at attracting FDI are fueling the adoption of AI technologies in pharmaceutical drug research and manufacturing. The presence of several prominent pharmaceutical manufacturing facilities in the Asia-Pacific region is expected to drive the demand for AI technologies in the coming years.
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Market dynamics
Driver
Growing use of AI in drug discovery
The adoption of AI and machine learning technologies is growing rapidly in drug discovery. Drug discovery has become extremely expensive and highly competitive in recent years. Hence, the growing demand for cost control and time reduction along with increasing efficiency of the drug discovery process is expected to drive the growth of AI in the pharmaceutical market during the period of forecast. Additionally, AI and ML technologies assist research and development activities by reducing errors and inefficiencies.
Constraints
High capital investments in AI technologies
A huge capital investment is involved in the implementation of AI and ML technologies in the pharmaceutical sector. Financial restraints of small and medium sized pharmaceutical companies and drug manufacturers may restrain the adoption of AI and hamper the market growth over the forecast period.
Opportunities
Growing investments in drug discovery for chronic diseases
It is estimated that by 2025, approximately 50% of global pharmaceutical companies will deploy AI technologies. This will result in increased investment in the development of drugs to cure various oncologies and chronic diseases. Hence, increased demand for innovative drugs across the globe owing to rising prevalence of various chronic diseases is expected to provide lucrative growth opportunities for market players in the near future.
Challenges
Challenges in maintaining data security and privacy
Data privacy and security is the most important factor for any large pharmaceutical organization. With the increasing adoption of digital technologies, the threat of cyberattacks is growing rapidly. Cyberattacks can negatively impact organizations and lead to financial loss. Therefore, maintaining data security and privacy are the main challenges.
Some of the Global AI in Pharmaceutical Market Key Players Include:
- IBM Corporation
- exscientia
- Deep genomics
- Cloud Pharmaceuticals, Inc.
- Microsoft Corporation
- NVIDIA Corporation
- Insilico Medicine
- Alphabet Inc.
- Atomwise, Inc.
- Biosymmetry
- Euretos
- Benevolent AI
Segments covered in the report
By technology
- natural language processing
- Context sensitive processing
- deep learning
- Query Method
By type of drug
- large molecules
- small molecules
By request
- drug discovery
- Clinical test
- research
- Others
By geography
- North America
- Europe
- Asia Pacific
- Latin America
- AEM
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