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Growth Insight—Role of AI in the Pharmaceutical Industry, Global, 2018–2022
Growth Insight—Role of AI in the Pharmaceutical Industry, Global, 2018–2022
Exploring Key Investment Trends, Companies-to-Action, and Growth Opportunities for AI in the Pharmaceutical Industry
26-Sep-2019
Global
Description
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 the commercial activity. Frost & Sullivan 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. Frost & Sullivan 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.
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. 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. Frost & Sullivan concurs that over the next five years, the utilization of AI will become a significant source of competitive advantage and differentiation for pharmaceutical companies; more successful use cases will emerge and significant efficiency and cost-saving opportunities will be addressed.
Research Scope
This research service analyzes the growth opportunities for AI applications in the pharmaceutical industry with a specific focus on value chain activities of drug discovery, clinical trials, RWE, and commercial applications. It also evaluates and discusses market projections, key trends, technology lifecycle, and key implementation challenges of AI across the top 10 applications. Finally, it provides industry best practices, case studies, and strategic imperatives for key industry stakeholders such as pharmaceutical sponsors, CROs, sites, and life science IT providers.
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 are 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?
Author: Amol Dilip Jadhav
Table of Contents
Key Findings
Scope and Definition
Key Questions this Study will Answer
AI Applications in the Pharmaceutical Industry—Opportunity Assessment
Six Big Themes Driving AI Adoption in the Pharmaceutical Industry
Sizing the Market Opportunity for AI Solutions in the Pharmaceutical Industry
AI in the Pharmaceutical Industry—Growth Opportunities by Use Case
Major AI Applications in the Pharmaceutical Industry—Performance Maturity Mapping across Key Performance Indicators (KPIs)
AI in the Pharmaceutical Industry—Investment vs. Revenue Analysis (Breakeven Analysis)
Savings Generated by AI Solutions in the Pharmaceutical Industry
AI in the Pharmaceutical Industry—Funding Analysis: 2013–2018
AI in the Pharmaceutical Industry—Vendor Ecosystem
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
Adoption Curve for AI across the Pharmaceutical Value Chain
Revenue Forecast for AI in the Pharmaceutical Industry
Forecast for AI in the Pharmaceutical Industry
Forecast for AI in the Pharmaceutical Industry (continued)
Forecast for AI in the Pharmaceutical Industry (continued)
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
Key Geographic Regions Adopting AI in the Pharmaceutical Industry (Continued)
AI in the Pharmaceutical Industry—Types of Revenue Generators
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
Major AI Applications within Drug Discovery—Use Cases (continued)
Example of AI Business Framework—Insilico Medicine: DL Platform Solutions for Drug Repurposing and Biomarker Development
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
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
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
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—Investment vs. Revenue Analysis (Breakeven Analysis) (Continued)
AI in the Pharmaceutical Industry—Funding Analysis: Analyst Perspective
AI in the Pharmaceutical Industry—Vendor Ecosystem
AI in the Pharmaceutical Industry—Vendor Ecosystem (Continued)
AI in the Pharmaceutical Industry—Major Vendors by Geography
AI in the Pharmaceutical Industry—Role of Non-traditional Players (GAFAM/BAT)
Strategic Imperatives for Major Stakeholders
Critical Challenges for AI Initiatives in the Pharmaceutical Industry
Convergence Potential of AI with Emerging Technologies
Key Conclusions—Five Industry Needs Critical for Future Strategies
Three Big Predictions
Legal Disclaimer
Segment and Scope for Revenue Forecast
Mapping AI Applications across Pharmaceutical Value Chain Activities
Mapping AI Applications across Pharmaceutical Value Chain Activities (continued)
Forecast—Key Trends for AI Applications in Drug Discovery
Major AI Applications within Drug Discovery—Vendor Ecosystem
Forecast—Key Trends for AI in Clinical Trials Applications
Major AI Applications within Clinical Trials—Vendor Ecosystem
Forecast—Key Trends for AI Applications in RWE
Major AI Applications in RWE—Vendor Ecosystem
Forecast—Key Trends for AI in Commercial and Operational Applications
AI Applications in Commercial and Operational Domain—Vendor Ecosystem
AI in the Pharmaceutical Industry: Participation of Non-traditional Players (GAFAM/BAT)
List of Exhibits
List of Exhibits (continued)
List of Exhibits (continued)
The Frost & Sullivan Story
Value Proposition—Future of Your Company & Career
Global Perspective
Industry Convergence
360º Research Perspective
Implementation Excellence
Our Blue Ocean Strategy
Popular Topics
Research Scope
This research service analyzes the growth opportunities for AI applications in the pharmaceutical industry with a specific focus on value chain activities of drug discovery, clinical trials, RWE, and commercial applications. It also evaluates and discusses market projections, key trends, technology lifecycle, and key implementation challenges of AI across the top 10 applications. Finally, it provides industry best practices, case studies, and strategic imperatives for key industry stakeholders such as pharmaceutical sponsors, CROs, sites, and life science IT providers.
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 are 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?
Author: Amol Dilip Jadhav
| No Index | No |
|---|---|
| Podcast | No |
| Author | Amol Dilip Jadhav |
| Industries | Healthcare |
| WIP Number | K3CE-01-00-00-00 |
| Is Prebook | No |
| GPS Codes | 9600-B1,9612-B1,9B07-C1,9611-B1,9627-B1 |