세계의 의약품 인공지능 시장 : 제공, 기술, 전개 모드, 용도, 최종 사용자별 예측(2025-2030년)

The Artificial Intelligence in Pharmaceutical Market was valued at USD 12.58 billion in 2023, expected to reach USD 15.79 billion in 2024, and is projected to grow at a CAGR of 26.68%, to USD 65.90 billion by 2030.

Artificial Intelligence (AI) in the pharmaceutical industry encompasses the use of advanced algorithms, machine learning, and data analytics to accelerate drug discovery, streamline clinical trials, personalize medicine, and enhance manufacturing processes. The necessity for AI arises from the enormous data and complexity in pharmaceuticals, requiring innovative approaches to improve efficiency, reduce time-to-market, and address unmet medical needs. Applications range from predictive modeling of disease outcomes and optimizing patient recruitment for trials to automating repetitive tasks in drug manufacturing. End-use scope includes pharmaceutical companies, biotech firms, research institutions, and healthcare providers seeking to harness AI for competitive advantage. Key growth factors include the increasing availability of big data and advancements in computational power, driving demand for AI to analyze vast datasets for meaningful insights. Moreover, the rising need for cost-effective drug development in the face of looming patent expirations and healthcare costs further fuels AI adoption. Opportunities lie in expanding AI capabilities in precision medicine, real-time data analytics, and improving patient engagement platforms. Companies can capitalize on these by investing in talent acquisition, forming strategic alliances, and maintaining a focus on ethical AI deployment. However, challenges include regulatory hurdles, data privacy concerns, and high costs of integrating AI systems within existing frameworks. There's also a talent gap for skilled professionals adept in both AI and pharmaceuticals. Innovations in AI-driven biomarker discovery and digital twins for virtual simulations hold promise for breakthroughs. Research on ethical AI usage can safeguard patient data while unlocking market opportunity. The AI in pharmaceutical market is dynamic, often influenced by regulatory policies, technological advancements, and collaborative R&D efforts. Companies must remain agile, embracing both open innovation models and proprietary developments to stay ahead in a rapidly evolving landscape.

Market Dynamics: Unveiling Key Market Insights in the Rapidly Evolving Artificial Intelligence in Pharmaceutical Market

The Artificial Intelligence in Pharmaceutical Market is undergoing transformative changes driven by a dynamic interplay of supply and demand factors. Understanding these evolving market dynamics prepares business organizations to make informed investment decisions, refine strategic decisions, and seize new opportunities. By gaining a comprehensive view of these trends, business organizations can mitigate various risks across political, geographic, technical, social, and economic domains while also gaining a clearer understanding of consumer behavior and its impact on manufacturing costs and purchasing trends.

• Market Drivers
  • Growing demand for personalized medicine to improve treatment outcomes
  • Utilization of AI-driven approaches to enhance existing drug repurposing strategies
  • Increasing need to enhance the processing of biomedical and clinical data
• Market Restraints
  • High initial investment for developing and implementing AI solutions
• Market Opportunities
  • Introduction of innovative AI-based solutions for pharmaceutical industry
  • Significant investments for AI drug discovery research
• Market Challenges
  • Concerns regarding data privacy and security

Porter's Five Forces: A Strategic Tool for Navigating the Artificial Intelligence in Pharmaceutical Market

Porter's five forces framework is a critical tool for understanding the competitive landscape of the Artificial Intelligence in Pharmaceutical Market. It offers business organizations with a clear methodology for evaluating their competitive positioning and exploring strategic opportunities. This framework helps businesses assess the power dynamics within the market and determine the profitability of new ventures. With these insights, business organizations can leverage their strengths, address weaknesses, and avoid potential challenges, ensuring a more resilient market positioning.

PESTLE Analysis: Navigating External Influences in the Artificial Intelligence in Pharmaceutical Market

External macro-environmental factors play a pivotal role in shaping the performance dynamics of the Artificial Intelligence in Pharmaceutical Market. Political, Economic, Social, Technological, Legal, and Environmental factors analysis provides the necessary information to navigate these influences. By examining PESTLE factors, businesses can better understand potential risks and opportunities. This analysis enables business organizations to anticipate changes in regulations, consumer preferences, and economic trends, ensuring they are prepared to make proactive, forward-thinking decisions.

Market Share Analysis: Understanding the Competitive Landscape in the Artificial Intelligence in Pharmaceutical Market

A detailed market share analysis in the Artificial Intelligence in Pharmaceutical Market provides a comprehensive assessment of vendors' performance. Companies can identify their competitive positioning by comparing key metrics, including revenue, customer base, and growth rates. This analysis highlights market concentration, fragmentation, and trends in consolidation, offering vendors the insights required to make strategic decisions that enhance their position in an increasingly competitive landscape.

FPNV Positioning Matrix: Evaluating Vendors' Performance in the Artificial Intelligence in Pharmaceutical Market

The Forefront, Pathfinder, Niche, Vital (FPNV) Positioning Matrix is a critical tool for evaluating vendors within the Artificial Intelligence in Pharmaceutical Market. This matrix enables business organizations to make well-informed decisions that align with their goals by assessing vendors based on their business strategy and product satisfaction. The four quadrants provide a clear and precise segmentation of vendors, helping users identify the right partners and solutions that best fit their strategic objectives.

Strategy Analysis & Recommendation: Charting a Path to Success in the Artificial Intelligence in Pharmaceutical Market

A strategic analysis of the Artificial Intelligence in Pharmaceutical Market is essential for businesses looking to strengthen their global market presence. By reviewing key resources, capabilities, and performance indicators, business organizations can identify growth opportunities and work toward improvement. This approach helps businesses navigate challenges in the competitive landscape and ensures they are well-positioned to capitalize on newer opportunities and drive long-term success.

Key Company Profiles

The report delves into recent significant developments in the Artificial Intelligence in Pharmaceutical Market, highlighting leading vendors and their innovative profiles. These include AiCure, LLC, Aspen Technology Inc., Atomwise Inc., BenevolentAI SA, BioSymetrics Inc., BPGbio Inc., Butterfly Network, Inc., Cloud Pharmaceuticals, Inc., Cyclica by Recursion Pharmaceuticals, Inc., Deargen Inc., Deep Genomics Incorporated, Deloitte Touche Tohmatsu Limited, Euretos Services BV, Exscientia PLC, Google LLC, Insilico Medicine, Intel Corporation, International Business Machines Corporation, InveniAI LLC, Isomorphic Labs Limited, Microsoft Corporation, Novo Nordisk A/S, NVIDIA Corporation, Oracle Corporation, SANOFI WINTHROP INDUSTRIE, Turbine Ltd., Viseven Europe OU, and XtalPi Inc..

Market Segmentation & Coverage

This research report categorizes the Artificial Intelligence in Pharmaceutical Market to forecast the revenues and analyze trends in each of the following sub-markets:

• Based on Offering, market is studied across Hardware, Services, and Software. The Hardware is further studied across Memory, Network, and Processor. The Services is further studied across Deployment & Integration and Support & Maintenance.
• Based on Technology, market is studied across Computer Vision, Machine Learning, Natural Language Processing, and Robotic Process Automation.
• Based on Deployment Mode, market is studied across Cloud-Based Solutions and On-Premise Solutions. The Cloud-Based Solutions is further studied across Hybrid Cloud, Private Cloud, and Public Cloud.
• Based on Application, market is studied across Clinical Trials, Drug Development, Drug Discovery, and Personalized Medicine. The Clinical Trials is further studied across Clinical Data Management, Patient Recruitment, and Safety Monitoring. The Drug Development is further studied across Formulation Development, Preclinical Studies, and Prototype Design. The Drug Discovery is further studied across Drug Screening and Lead Identification & Optimization. The Personalized Medicine is further studied across Biomarker Discovery, Genomic Analysis, and Patient Disease Journey.
• Based on End User, market is studied across Contract Research Organizations, Healthcare Providers, Pharmaceutical Companies, and Research Laboratories.
• Based on Region, market is studied across Americas, Asia-Pacific, and Europe, Middle East & Africa. The Americas is further studied across Argentina, Brazil, Canada, Mexico, and United States. The United States is further studied across California, Florida, Illinois, New York, Ohio, Pennsylvania, and Texas. The Asia-Pacific is further studied across Australia, China, India, Indonesia, Japan, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand, and Vietnam. The Europe, Middle East & Africa is further studied across Denmark, Egypt, Finland, France, Germany, Israel, Italy, Netherlands, Nigeria, Norway, Poland, Qatar, Russia, Saudi Arabia, South Africa, Spain, Sweden, Switzerland, Turkey, United Arab Emirates, and United Kingdom.

The report offers a comprehensive analysis of the market, covering key focus areas:

1. Market Penetration: A detailed review of the current market environment, including extensive data from top industry players, evaluating their market reach and overall influence.

2. Market Development: Identifies growth opportunities in emerging markets and assesses expansion potential in established sectors, providing a strategic roadmap for future growth.

3. Market Diversification: Analyzes recent product launches, untapped geographic regions, major industry advancements, and strategic investments reshaping the market.

4. Competitive Assessment & Intelligence: Provides a thorough analysis of the competitive landscape, examining market share, business strategies, product portfolios, certifications, regulatory approvals, patent trends, and technological advancements of key players.

5. Product Development & Innovation: Highlights cutting-edge technologies, R&D activities, and product innovations expected to drive future market growth.

The report also answers critical questions to aid stakeholders in making informed decisions:

1. What is the current market size, and what is the forecasted growth?

2. Which products, segments, and regions offer the best investment opportunities?

3. What are the key technology trends and regulatory influences shaping the market?

4. How do leading vendors rank in terms of market share and competitive positioning?

5. What revenue sources and strategic opportunities drive vendors' market entry or exit strategies?

Table of Contents

1. Preface

• 1.1. Objectives of the Study
• 1.2. Market Segmentation & Coverage
• 1.3. Years Considered for the Study
• 1.4. Currency & Pricing
• 1.5. Language
• 1.6. Stakeholders

2. Research Methodology

• 2.1. Define: Research Objective
• 2.2. Determine: Research Design
• 2.3. Prepare: Research Instrument
• 2.4. Collect: Data Source
• 2.5. Analyze: Data Interpretation
• 2.6. Formulate: Data Verification
• 2.7. Publish: Research Report
• 2.8. Repeat: Report Update

3. Executive Summary

4. Market Overview

5. Market Insights

• 5.1. Market Dynamics
  • 5.1.1. Drivers
    • 5.1.1.1. Growing demand for personalized medicine to improve treatment outcomes
    • 5.1.1.2. Utilization of AI-driven approaches to enhance existing drug repurposing strategies
    • 5.1.1.3. Increasing need to enhance the processing of biomedical and clinical data
  • 5.1.2. Restraints
    • 5.1.2.1. High initial investment for developing and implementing AI solutions
  • 5.1.3. Opportunities
    • 5.1.3.1. Introduction of innovative AI-based solutions for pharmaceutical industry
    • 5.1.3.2. Significant investments for AI drug discovery research
  • 5.1.4. Challenges
    • 5.1.4.1. Concerns regarding data privacy and security
• 5.2. Market Segmentation Analysis
  • 5.2.1. Offerings: Growing AI-based software adoption owing to its improved functionality & features
  • 5.2.2. Technology: Increasing utilization of machine learning technology in the pharmaceutical sector
  • 5.2.3. Applications: Improving the efficiency of clinical trial process through AI solutions
  • 5.2.4. End-users: Significant implementation of AI technologies by pharma & biotech companies for drug discovery & development
• 5.3. Porter's Five Forces Analysis
  • 5.3.1. Threat of New Entrants
  • 5.3.2. Threat of Substitutes
  • 5.3.3. Bargaining Power of Customers
  • 5.3.4. Bargaining Power of Suppliers
  • 5.3.5. Industry Rivalry
• 5.4. PESTLE Analysis
  • 5.4.1. Political
  • 5.4.2. Economic
  • 5.4.3. Social
  • 5.4.4. Technological
  • 5.4.5. Legal
  • 5.4.6. Environmental

6. Artificial Intelligence in Pharmaceutical Market, by Offering

• 6.1. Introduction
• 6.2. Hardware
  • 6.2.1. Memory
  • 6.2.2. Network
  • 6.2.3. Processor
• 6.3. Services
  • 6.3.1. Deployment & Integration
  • 6.3.2. Support & Maintenance
• 6.4. Software

7. Artificial Intelligence in Pharmaceutical Market, by Technology

• 7.1. Introduction
• 7.2. Computer Vision
• 7.3. Machine Learning
• 7.4. Natural Language Processing
• 7.5. Robotic Process Automation

8. Artificial Intelligence in Pharmaceutical Market, by Deployment Mode

• 8.1. Introduction
• 8.2. Cloud-Based Solutions
  • 8.2.1. Hybrid Cloud
  • 8.2.2. Private Cloud
  • 8.2.3. Public Cloud
• 8.3. On-Premise Solutions

9. Artificial Intelligence in Pharmaceutical Market, by Application

• 9.1. Introduction
• 9.2. Clinical Trials
  • 9.2.1. Clinical Data Management
  • 9.2.2. Patient Recruitment
  • 9.2.3. Safety Monitoring
• 9.3. Drug Development
  • 9.3.1. Formulation Development
  • 9.3.2. Preclinical Studies
  • 9.3.3. Prototype Design
• 9.4. Drug Discovery
  • 9.4.1. Drug Screening
  • 9.4.2. Lead Identification & Optimization
• 9.5. Personalized Medicine
  • 9.5.1. Biomarker Discovery
  • 9.5.2. Genomic Analysis
  • 9.5.3. Patient Disease Journey

10. Artificial Intelligence in Pharmaceutical Market, by End User

• 10.1. Introduction
• 10.2. Contract Research Organizations
• 10.3. Healthcare Providers
• 10.4. Pharmaceutical Companies
• 10.5. Research Laboratories

11. Americas Artificial Intelligence in Pharmaceutical Market

• 11.1. Introduction
• 11.2. Argentina
• 11.3. Brazil
• 11.4. Canada
• 11.5. Mexico
• 11.6. United States

12. Asia-Pacific Artificial Intelligence in Pharmaceutical Market

• 12.1. Introduction
• 12.2. Australia
• 12.3. China
• 12.4. India
• 12.5. Indonesia
• 12.6. Japan
• 12.7. Malaysia
• 12.8. Philippines
• 12.9. Singapore
• 12.10. South Korea
• 12.11. Taiwan
• 12.12. Thailand
• 12.13. Vietnam

13. Europe, Middle East & Africa Artificial Intelligence in Pharmaceutical Market

• 13.1. Introduction
• 13.2. Denmark
• 13.3. Egypt
• 13.4. Finland
• 13.5. France
• 13.6. Germany
• 13.7. Israel
• 13.8. Italy
• 13.9. Netherlands
• 13.10. Nigeria
• 13.11. Norway
• 13.12. Poland
• 13.13. Qatar
• 13.14. Russia
• 13.15. Saudi Arabia
• 13.16. South Africa
• 13.17. Spain
• 13.18. Sweden
• 13.19. Switzerland
• 13.20. Turkey
• 13.21. United Arab Emirates
• 13.22. United Kingdom

14. Competitive Landscape

• 14.1. Market Share Analysis, 2023
• 14.2. FPNV Positioning Matrix, 2023
• 14.3. Competitive Scenario Analysis
  • 14.3.1. Sanofi, Formation Bio, and OpenAI collaborate to transform drug development through AI integration
  • 14.3.2. NVIDIA BioNeMo transforms drug discovery with generative AI models to accelerate pharmaceutical innovation
  • 14.3.3. Denmark pioneers AI innovation in pharmaceuticals with groundbreaking NVIDIA supercomputer collaboration
• 14.4. Strategy Analysis & Recommendation

Companies Mentioned

• 1. AiCure, LLC
• 2. Aspen Technology Inc.
• 3. Atomwise Inc.
• 4. BenevolentAI SA
• 5. BioSymetrics Inc.
• 6. BPGbio Inc.
• 7. Butterfly Network, Inc.
• 8. Cloud Pharmaceuticals, Inc.
• 9. Cyclica by Recursion Pharmaceuticals, Inc.
• 10. Deargen Inc.
• 11. Deep Genomics Incorporated
• 12. Deloitte Touche Tohmatsu Limited
• 13. Euretos Services BV
• 14. Exscientia PLC
• 15. Google LLC
• 16. Insilico Medicine
• 17. Intel Corporation
• 18. International Business Machines Corporation
• 19. InveniAI LLC
• 20. Isomorphic Labs Limited
• 21. Microsoft Corporation
• 22. Novo Nordisk A/S
• 23. NVIDIA Corporation
• 24. Oracle Corporation
• 25. SANOFI WINTHROP INDUSTRIE
• 26. Turbine Ltd.
• 27. Viseven Europe OU
• 28. XtalPi Inc.