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Growth Insight - Role of AI in the Pharmaceutical Industry, Global, 2018-2022

  • ID: 4846380
  • Report
  • September 2019
  • Region: Global
  • 119 Pages
  • Frost & Sullivan
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Exploring Key Investment Trends, Companies-to-Action, and Growth Opportunities for AI in the Pharmaceutical Industry

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.

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. The analyst 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 lifecycles, 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 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?
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Executive Summary

  • 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

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

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

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

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

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

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

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

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)

Conclusion

  • 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

Appendix

  • Segment and Scope for Revenue Forecast
  • Mapping AI Applications across Pharmaceutical Value Chain Activities
  • 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
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