Global Growth Insight - Role of AI in the Pharmaceutical Industry 2018-2022: Exploring Key Investment Trends, Companies-to-Action, and Growth Opportunities for AI in the Pharmaceutical Industry

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Oct 08, 2019, 14:00 ET

DUBLIN, Oct. 8, 2019 /PRNewswire/ -- The "Growth Insight - Role of AI in the Pharmaceutical Industry, Global, 2018-2022" report has been added to ResearchAndMarkets.com's offering.

The revenue generated through AI-based solutions in the pharmaceutical industry is projected to rise at a CAGR of 21.94% and reach $2.199 billion by 2022.

Pharmaceutical drug discovery and development processes suffer from declining success rates and a stagnant pipeline. Artificial Intelligence (AI) supported by Big Data could be a key element that can provide an effectual solution. There has been a rapid growth in the data generated within the life sciences and pharmaceutical industries. This stems from several sources, including the R&D process itself, academia, patients, caregivers, and commercial activity.

The analyst projects that effective utilization of data and application of AI technologies for gaining insights and decision support will impact the complete value chain for drug discovery and development within the pharmaceutical industry. Major application areas such as drug discovery, clinical trials, real-world evidence (RWE), and commercial cover more than 80% of the current use cases within the industry. The analyst estimates operationalizing AI platforms across drug discovery & development workflows would result in improved productivity and cost-efficiency, saving more than 3-5% of the current spend. Pharmaceutical and biotech companies will continue to bet big on AI applications across compound discovery, drug repurposing, real-time analytics for patient-centric trial design and recruitment, as well as RWE. Identification of the right partners and developing the technical know-how will be essential. Over the short term (the next two to three years), the impact will be seen through improved drug pipeline, faster clinical trials, and approval of new and well as repurposed molecules. In the long run (over the next five years), the industry can expect to cumulatively save as much as $50 billion at a medium level of investment on AI-based products and solutions.

With total investment exceeding $7.20 billion across 300+ deals between 2013 and 2018, the pharmaceutical industry continues to lead the healthcare sector in terms of attracting AI-related venture funding. Major pharmaceutical companies have embraced AI as part of their digitization efforts. These programs are currently run through value-based partnerships and collaborations, while certain elements are outsourced. The infrastructure cost and the intellectual workforce that is required to run such deep technical programs within the industry have been a concern, but this has provided opportunities for niche start-ups to enter the space. At present, the United States is leading the market with a 78.2% revenue share and the maximum number of vendors, followed by Europe. With a focused approach, China is poised to outrun the competition and become the market leader in the coming decade.

Key Issues Addressed

What are the key trends and growth opportunities that can be derived from the application of AI in the pharmaceutical industry?
What are the top 10 areas within the pharmaceutical value chain which are ripe for innovation and can be transformed using AI?
What are the unique companies that are introducing innovative AI solutions for focused pharmaceutical industry applications? What is the select Companies-to-Action by major AI application areas?
What are the immediate lucrative growth opportunities and future cost savings potential for AI applications across the drug development value chain?
How does the current vendor ecosystem for AI applications in the pharmaceutical industry look like? How pharmaceutical companies are engaging with AI vendors?
What are the critical success factors, challenges, and strategic imperatives for considering AI applications in the pharmaceutical industry space?

Key Topics Covered:

1. Executive Summary

2. Market Definition and Overview

Definitions-AI and Available Techniques
Market Definition-AI for the Pharmaceutical Industry Solutions Market
AI in the Pharmaceuticals Industry-Major Application Segments and Technologies
Six Big Themes Driving AI Adoption in Pharmaceutical Industry
Continuing Challenges with Drug Discovery
Advancing the Clinical Trial Expedition
Streamlining Personalized Genomics and Decision Support Services
Evolving AI Solutions & Service-based Business Model

3. Market Forecast-AI in the Pharmaceutical Industry

Adoption Curve for AI across the Pharmaceutical Value Chain
Revenue Forecast for AI in the Pharmaceutical Industry
Forecast for AI in the Pharmaceutical Industry
Sizing the Market Opportunity for AI in the Pharmaceutical Industry
Therapeutic Focus-Which Focus Areas Demonstrate High Potential for AI Applications
Key Geographic Regions Adopting AI in the Pharmaceutical Industry
AI in the Pharmaceutical Industry-Types of Revenue Generators

4. AI Applications in Drug Discovery-Growth Opportunities

Category Definition-AI in Drug Discovery
Growth Opportunity for AI in Drug Discovery
Revenue Forecast-AI in Drug Discovery
Forecast for AI Solutions in Drug Discovery
Competitive Intelligence-Drug Discovery Pharmaceutical and AI Vendor Collaborations
Major AI Applications within Drug Discovery-Use Cases
Example of AI Business Framework-Insilico Medicine: DL Platform Solutions for Drug Repurposing and Biomarker Development

5. AI Applications in Clinical Trials-Growth Opportunities

Category Definition-AI in Clinical Trials
Growth Opportunity for AI in Clinical Trials
Revenue Forecast-AI in Clinical Trials
Forecast for AI Solutions in Clinical Trials
Competitive Intelligence-Clinical Trials, Pharmaceutical, and AI Vendor Collaborations
Major AI Applications in Clinical Trials-Use Cases
Example of AI Business Framework-Evidation Health: Mapping the Behaviorome
Example of AI Business Framework-Antidote: Democratizing the Clinical Trial Recruitment Process

6. AI Applications in Real-World Evidence-Growth Opportunities

Category Definition-AI in RWE
Growth Opportunity for AI within RWE
Revenue Forecast-AI in RWE
Forecast for AI Solutions in RWE
Major AI Applications within RWE-Use Cases
Example of AI Business Framework: GNS Healthcare-The Power of Causal ML

7. AI Applications in Commercial and Operational Areas-Growth Opportunities

Category Definition-AI Applications in the Commercial and Operational Domain
Growth Opportunity for AI Applications in the Commercial and Operational Domain
Revenue Forecast-AI Applications in the Commercial and Operational Domain
Forecast for AI Applications in the Commercial and Operational Domain
Major AI Applications in Commercial and Operational Domain-Use Cases
Example of AI Business Framework-Lexalytics: Translating Thoughts & Feelings into Profitable Decisions

8. Investment and Opportunity Analysis-AI Applications in the Pharmaceutical Industry

AI Applications in the Pharmaceutical Industry-Opportunity Assessment
AI in the Pharmaceutical Industry-Spend vs. Saving Analysis
Savings Generated by AI Solutions in the Pharmaceutical Industry
AI in the Pharmaceutical Industry-Funding Analysis (2013-2018)
AI in the Pharmaceutical Industry-Funding Analysis (2013-2018) by Geographic Region
AI in the Pharmaceutical Industry-Investment vs. Revenue Analysis (Breakeven Analysis)
AI in the Pharmaceutical Industry-Funding Analysis: Analyst Perspective

9. Competitive Landscape

AI in the Pharmaceutical Industry-Vendor Ecosystem
AI in the Pharmaceutical Industry-Major Vendors by Geography
AI in the Pharmaceutical Industry-Role of Non-traditional Players (GAFAM/BAT)

10. Conclusion

For more information about this report visit https://www.researchandmarkets.com/r/lqcpvo

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