AI in Clinical Trials Market Set for Transformative Growth Through 2040, Unlocking Opportunities Across Trial Phases, Therapeutic Areas, End Users and Key Global Regions
Dublin, Sept. 25, 2026 (GLOBE NEWSWIRE) -- "AI in Clinical Trials Market, till 2040: Distribution by Trial Phase, Target Therapeutic Area, End User and Key Geographical Regions: Industry Trends and Global Forecasts" has been added to ResearchAndMarkets.com's offering.
The global artificial intelligence in clinical trials market is projected to expand from USD 2.09 billion in 2026 to USD 18.62 billion by 2040, representing a compound annual growth rate of 17.0% during the forecast period. Market growth is being driven by increasing demand for faster drug development, more efficient patient recruitment, optimized clinical trial design, and advanced analysis of complex healthcare datasets.
Developing novel therapeutic interventions typically requires 10 to 15 years, with clinical trials accounting for approximately 50% to 70% of the total development timeline and expenditure. Design limitations, recruitment delays, patient stratification errors, and high dropout rates continue to affect trial performance. Pharmaceutical and biotechnology companies are therefore adopting artificial intelligence, machine learning, predictive analytics, and automation to improve clinical trial efficiency and increase the probability of success.
Strategic Applications of AI in Clinical Trials
Artificial intelligence supports multiple stages of clinical research, including patient recruitment, site selection, protocol design, data management, safety monitoring, and outcome prediction. AI-powered platforms analyze electronic health records, genomic information, medical imaging, and real-world data to identify eligible patients, reduce screening failures, and accelerate enrollment.
Additional applications include automated data cleaning, anomaly detection, adverse-event forecasting, remote monitoring, and the identification of safety and efficacy signals. Digital twins and predictive models also enable sponsors to simulate trial scenarios, refine inclusion criteria, assess protocol feasibility, and support adaptive clinical trial designs. These capabilities can reduce costs, improve data quality, and advance personalized and precision medicine programs.
Key AI in Clinical Trials Market Growth Drivers
The AI in clinical trials market is benefiting from the rising volume and complexity of clinical data, growing adoption of decentralized and adaptive trials, and increasing investment in precision therapies. The integration of real-world evidence with electronic health records and genetic data enables more accurate patient matching and deeper analysis of treatment outcomes.
Demand is also being supported by the pharmaceutical industry's focus on reducing development timelines and controlling clinical research costs. Predictive analytics can forecast recruitment performance, identify operational risks, and improve site selection, while automation reduces the administrative burden associated with reporting, monitoring, and data reconciliation.
Competitive Landscape
The competitive environment includes established technology providers, clinical research specialists, and emerging AI companies. Prominent participants include IQVIA, Medidata, Dassault Systemes, Oracle Health Sciences, Phesi, AiCure, Deep 6 AI, Mendel.ai, Saama Technologies, Unlearn, ConcertAI, and Tempus AI.
Market participants compete through comprehensive clinical analytics platforms, patient-matching technologies, predictive modeling, synthetic control arms, and real-time monitoring solutions. Strategic collaborations with pharmaceutical and biotechnology companies are expected to remain important as providers seek access to high-quality datasets, clinical expertise, and broader commercialization opportunities.
Emerging Industry Trends
Agentic AI, generative AI, and digital twins are among the technologies influencing the evolution of AI-enabled clinical trials. Agentic systems can manage workflows involving recruitment, risk monitoring, and protocol adjustments. Generative AI can support protocol development, synthetic data creation, regulatory documentation, and patient-facing materials, including electronic consent content.
Digital twins use historical and patient-level data to model treatment responses and support trials with smaller cohorts and improved statistical power. Together, these technologies are creating opportunities to accelerate enrollment, strengthen trial design, automate routine processes, and enable continuous clinical risk assessment.
Market Challenges
Adoption remains constrained by data privacy requirements, regulatory uncertainty, fragmented clinical systems, interoperability limitations, and the cost of implementing advanced infrastructure. Compliance with GDPR, HIPAA, and other patient-data regulations requires robust governance and security controls.
Incomplete, inconsistent, or biased datasets may also affect model performance and clinical reliability. A shortage of professionals with combined expertise in artificial intelligence and clinical research represents another barrier. Collaboration among pharmaceutical companies, technology providers, clinical research organizations, healthcare institutions, and regulators will be essential to establish validation standards and responsible deployment frameworks.
Market Segmentation and Share Insights
The AI in clinical trials market is analyzed across Phase I, Phase II, and Phase III trials. Therapeutic areas include cardiovascular disorders, central nervous system disorders, infectious diseases, metabolic disorders, oncological disorders, and other indications. End users primarily comprise pharmaceutical and biotechnology companies, alongside clinical research and healthcare stakeholders.
Oncology currently accounts for the largest market share by therapeutic area. Cancer trials generate extensive datasets across genomics, imaging, biomarkers, electronic health records, and treatment outcomes, creating strong demand for AI-based patient stratification, recruitment, and predictive analytics.
Asia-Pacific holds a significant share of the global AI in clinical trials market, supported by its large and diverse patient population, growing burden of cancer and diabetes, competitive operating costs, expanding biotechnology infrastructure, government incentives, and improving regulatory frameworks. The report also evaluates opportunities across North America, Europe, Latin America, the Middle East and North Africa, and the rest of the world.
Report Coverage
Key Questions Addressed
Strategic Value of the Report
The report provides decision-makers with detailed market forecasts, competitive intelligence, expert-validated insights, and analysis of emerging opportunities. It is designed to support pharmaceutical companies, biotechnology firms, clinical research organizations, investors, technology providers, and other stakeholders evaluating the rapidly expanding artificial intelligence in clinical trials market.
Additional benefits include dynamic Excel dashboards for analytical modules, 15% complimentary content customization, an interactive report walkthrough with the research team, and free updates for eligible report versions.
Key Topics Covered:
1. PREFACE
1.1. Scope of the Report
1.2. Research Methodology
1.3. Key Questions Answered
1.4. Chapter Outlines
2. EXECUTIVE SUMMARY
3. INTRODUCTION
3.1. Chapter Overview
3.2. Evolution of AI
3.3. Subfields of AI
3.4. Applications of AI in Healthcare
3.4.1. Drug Discovery
3.4.2. Drug Manufacturing
3.4.3. Marketing
3.4.4. Diagnosis and Treatment
3.4.5. Clinical Trials
3.5. Applications of AI in Clinical Trials
3.6. Challenges Associated with the Adoption of AI
3.7. Future Perspective
4. COMPETITIVE LANDSCAPE
4.1. Chapter Overview
4.2. AI in Clinical Trials: AI Software and Service Providers Landscape
4.2.1. Analysis by Year of Establishment
4.2.2. Analysis by Company Size
4.2.3. Analysis by Location of Headquarters
4.2.4. Analysis by Company Size and Location of Headquarters (Region-wise)
4.2.5. Analysis by Key Offering
4.2.6. Analysis by Business Model
4.2.7. Analysis by Deployment Option
4.2.8. Analysis by Type of AI Technology
4.2.9. Analysis by Application Area
4.2.10. Analysis by Potential End-user
5. COMPANY PROFILES
5.1. Chapter Overview
5.2. AiCure
5.2.1. Company Overview
5.2.2. AI-based Clinical Trial Offerings
5.2.3. Recent Developments and Future Outlook
5.3. Antidote Technologies
5.3.1. Company Overview
5.3.2. AI-based Clinical Trial Offerings
5.3.3. Recent Developments and Future Outlook
5.4. Deep 6 AI
5.4.1. Company Overview
5.4.2. AI-based Clinical Trial Offerings
5.4.3. Recent Developments and Future Outlook
5.5. Innoplexus
5.5.1. Company Overview
5.5.2. AI-based Clinical Trial Offerings
5.5.3. Recent Developments and Future Outlook
5.6. IQVIA
5.6.1. Company Overview
5.6.2. Financial Information
5.6.3. AI-based Clinical Trial Offerings
5.6.4. Recent Developments and Future Outlook
5.7. Median Technologies
5.7.1. Company Overview
5.7.2. Financial Information
5.7.3. AI-based Clinical Trial Offerings
5.7.4. Recent Developments and Future Outlook
5.8. Medidata
5.8.1. Company Overview
5.8.2. Financial Information
5.8.3. AI-based Clinical Trial Offerings
5.8.4. Recent Developments and Future Outlook
5.9. Mendel.AI
5.9.1. Company Overview
5.9.2. AI-based Clinical Trial Offerings
5.9.3. Recent Developments and Future Outlook
5.10. Phesi
5.10.1. Company Overview
5.10.2. AI-based Clinical Trial Offerings
5.10.3. Recent Developments and Future Outlook
5.11. Saama Technologies
5.11.1. Company Overview
5.11.2. AI-based Clinical Trial Offerings
5.11.3. Recent Developments and Future Outlook
5.12. Signant Health
5.12.1. Company Overview
5.12.2. AI-based Clinical Trial Offerings
5.12.3. Recent Developments and Future Outlook
5.13. Trials.AI
5.13.1. Company Overview
5.13.2. AI-based Clinical Trial Offerings
5.13.3. Recent Developments and Future Outlook
6. CLINICAL TRIAL ANALYSIS
6.1. Chapter Overview
6.2. Scope and Methodology
6.3. AI in Clinical Trials
6.3.1. Analysis by Trial Registration Year
6.3.2. Analysis by Number of Patients Enrolled
6.3.3. Analysis by Trial Phase
6.3.4. Analysis by Trial Status
6.3.5. Analysis by Trial Registration Year and Status
6.3.6. Analysis by Type of Sponsor
6.3.7. Analysis by Patient Gender
6.3.8. Analysis by Patient Age
6.3.9. Word Cloud Analysis: Emerging Focus Areas
6.3.10. Analysis by Target Therapeutic Area
6.3.11. Analysis by Study Design
6.3.11.1. Analysis by Type of Patient Allocation Model Used
6.3.11.2. Analysis by Type of Trial Masking Adopted
6.3.11.3. Analysis by Type of Intervention
6.3.11.4. Analysis by Trial Purpose
6.3.12. Most Active Players: Analysis by Number of Clinical Trials
6.3.13. Analysis of Clinical Trials by Geography
6.3.14. Analysis of Clinical Trials by Geography and Trial Status
6.3.15. Analysis of Patients Enrolled by Geography and Trial Registration Year
6.3.16. Analysis of Patients Enrolled by Geography and Trial Status
7. PARTNERSHIPS AND COLLABORATIONS
7.1. Chapter Overview
7.2. Partnership Models
7.3. AI in Clinical Trials: Partnerships and Collaborations
7.3.1. Analysis by Year of Partnership
7.3.2. Analysis by Type of Partnership
7.3.3. Analysis by Year and Type of Partnership
7.3.4. Analysis by Application Area
7.3.5. Analysis by Target Therapeutic Area
7.3.6. Analysis by Type of Partner
7.3.7. Most Active Players: Analysis by Number of Partnerships
7.3.8. Analysis by Geography
7.3.8.1. Local and International Agreements
7.3.8.2. Intercontinental and Intracontinental Agreements
8. FUNDING AND INVESTMENTS
8.1. Chapter Overview
8.2. Types of Funding
8.3. AI in Clinical Trials: Funding and Investments
8.3.1. Analysis by Year of Funding
8.3.2. Analysis by Amount Invested
8.3.3. Analysis by Type of Funding
8.3.4. Analysis by Year and Type of Funding
8.3.5. Analysis by Type of Funding and Amount Invested
8.3.6. Analysis by Application Area
8.3.7. Analysis by Geography
8.3.8. Most Active Players: Analysis by Number of Funding Instances and Amount Raised
8.3.9. Leading Investors: Analysis by Number of Funding Instances
8.4. Concluding Remarks
9. BIG PHARMA INITIATIVES
9.1. Chapter Overview
9.2. Scope and Methodology
9.3. Analysis by Year of Initiative
9.4. Analysis by Type of Initiative
9.5. Analysis by Application Area of AI
9.6. Analysis by Target Therapeutic Area
9.7. Benchmarking Analysis: Big Pharma Players
10. AI IN CLINICAL TRIALS: USE CASES
10.1. Chapter Overview
10.2. Use Case 1: Collaboration between Roche and AiCure
10.2.1. Roche
10.2.2. AiCure
10.2.3. Business Needs
10.2.4. Objectives Achieved and Solutions Provided
10.3. Use Case 2: Collaboration between Takeda and AiCure
10.3.1. Takeda
10.3.2. AiCure
10.3.3. Business Needs
10.3.4. Objectives Achieved and Solutions Provided
10.4. Use Case 3: Collaboration between Teva Pharmaceuticals and Intel
10.4.1. Teva Pharmaceuticals
10.4.2. Intel
10.4.3. Business Needs
10.4.4. Objectives Achieved and Solutions Provided
10.5. Use Case 4: Collaboration between Undisclosed Pharmaceutical Company and Antidote
10.5.1. Antidote
10.5.2. Business Needs
10.5.3. Objectives Achieved and Solutions Provided
10.6. Use Case 5: Collaboration between Undisclosed Pharmaceutical Company and Cognizant
10.6.1. Cognizant
10.6.2. Business Needs
10.6.3. Objectives Achieved and Solutions Offered
10.7. Use Case 6: Collaboration between Cedars-Sinai Medical Center and Deep 6 AI
10.7.1. Cedars-Sinai Medical Center
10.7.2. Deep 6 AI
10.7.3. Business Needs
10.7.4. Objectives Achieved and Solutions Offered
10.8. Use Case 7: Collaboration between GlaxoSmithKline (GSK) and PathAI
10.8.1. PathAI
10.8.2. GlaxoSmithKline (GSK)
10.8.3. Business Needs
10.8.4. Objectives Achieved and Solutions Provided
10.9. Use Case 8: Collaboration between Bristol Myers Squibb (BMS) and Concert AI
10.9.1. Concert AI
10.9.2. Bristol Myers Squibb (BMS)
10.9.3. Business Needs
10.9.4. Objectives Achieved and Solutions Provided
11. VALUE CREATION FRAMEWORK: A STRATEGIC GUIDE TO ADDRESS UNMET NEEDS IN CLINICAL TRIALS
12. COST SAVING ANALYSIS
13. MARKET FORECAST AND OPPORTUNITY ANALYSIS
13.1. Chapter Overview
13.2. Key Assumptions and Forecast Methodology
13.3. Global AI in Clinical Trials Market
13.3.1. AI in Clinical Trials Market: Distribution by Trial Phase
13.3.1.1. AI in Clinical Trials Market for Phase I
13.3.1.2. AI in Clinical Trials Market for Phase II
13.3.1.3. AI in Clinical Trials Market for Phase III
13.3.2. AI in Clinical Trials Market: Distribution by Target Therapeutic Area
13.3.2.1. AI in Clinical Trials Market for Cardiovascular Disorders
13.3.2.2. AI in Clinical Trials Market for CNS Disorders
13.3.2.3. AI in Clinical Trials Market for Infectious Diseases
13.3.2.4. AI in Clinical Trials Market for Metabolic Disorders
13.3.2.5. AI in Clinical Trials Market for Oncological Disorders
13.3.2.6. AI in Clinical Trials Market for Other Disorders
13.3.3. AI in Clinical Trials Market: Distribution by End-user
13.3.3.1. AI in Clinical Trials Market for Pharmaceutical and Biotechnology Companies
13.3.3.2. AI in Clinical Trials Market for Other End-users
13.3.4. AI in Clinical Trials Market: Distribution by Key Geographical Regions
13.3.4.1. AI in Clinical Trials Market in North America
13.3.4.2. AI in Clinical Trials Market in Europe
13.3.4.3. AI in Clinical Trials Market in Asia-Pacific
13.3.4.4. AI in Clinical Trials Market in Middle East and North Africa
13.3.4.5. AI in Clinical Trials Market in Latin America
14. CONCLUSION
15.. EXECUTIVE INSIGHTS
15.1. Chapter Overview
15.2. Company A
15.2.1. Company Snapshot
15.2.2. Interview Transcript: Danielle Ralic, Co-Founder, Chief Executive Officer and Chief Technology Officer
15.3. Company B
15.3.1. Company Snapshot
15.3.2. Interview Transcript: Wout Brusselaers, Founder and Chief Executive Officer
15.4. Company C
15.4.1. Company Snapshot
15.4.2. Interview Transcript: Dimitrios Skaltsas, Co-Founder and Executive Director
15.5. Company D
15.5.1. Company Snapshot
15.5.2. Interview Transcript: R. A. Bavasso, Founder and Chief Executive Officer
15.6. Company E
15.6.1. Company Snapshot
15.6.2. Interview Transcript: Troy Bryenton (Chief Technology Officer), Michael Shipton (Chief Commercial Officer), Darcy Forman (Chief Delivery Officer), Grazia Mohren (Head of Marketing)
16. APPENDIX I: TABULATED DATA
17. APPENDIX II: LIST OF COMPANIES AND ORGANIZATIONS
A selection of companies mentioned in this report includes, but is not limited to:
. AiCure
. Antidote Technologies
. Deep 6 AI
. Innoplexus
. IQVIA
. Median Technologies
. Medidata
. Mendel.ai
. Phesi
. Saama Technologies
. Signant Health
. Trials.ai
For more information about this report visit https://www.researchandmarkets.com/r/9w5im1
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