Table of Content

1. PREFACE
1.1. AI in Clinical Trials Overview
1.2. Key Market Insights
1.3. Scope of the Report
1.4. Research Methodology
1.5. Frequently Asked Questions
1.6. 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. MARKET 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(s)
4.2.6. Analysis by Business Model(s)
4.2.7. Analysis by Deployment Option(s)
4.2.8. Analysis by Type of AI Technology
4.2.9. Analysis by Application Area(s)
4.2.10. Analysis by Potential End-user(s)

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 INVESTMENT ANALYSIS
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
11.1. Chapter Overview
11.2. Unmet Needs in Clinical Trials
11.3. Key Assumptions and Methodology
11.4. Key Tools and Technologies
11.4.1. Blockchain
11.4.2. Big Data Analytics
11.4.3. Real-world Evidence
11.4.4. Digital Twins
11.4.5. Cloud Computing
11.4.6. Internet of Things (IoT)
11.5. Trends in Research Activity
11.6. Trends in Intellectual Capital
11.7. Extent of Innovation versus Associated Risks
11.8. Results and Discussion
12. COST SAVING ANALYSIS
12.1. Chapter Overview
12.2. Key Assumptions and Methodology
12.3. Overall Cost Saving Potential of AI in Clinical Trials, 2023-2035
12.3.1. Cost Saving Potential: Distribution by Trial Phase, 2023 and 2035
12.3.1.1. Cost Saving Potential in Phase I Clinical Trials, 2023-2035
12.3.1.2. Cost Saving Potential in Phase II Clinical Trials, 2023-2035
12.3.1.3. Cost Saving Potential in Phase III Clinical Trials, 2023-2035
12.3.2. Cost Saving Potential: Distribution by Trial Procedure, 2023 and 2035
12.3.2.1. Cost Saving Potential in Patient Recruitment, 2023-2035
12.3.2.2. Cost Saving Potential in Patient Retention, 2023-2035
12.3.2.3. Cost Saving Potential in Staffing and Administration, 2023-2035
12.3.2.4. Cost Saving Potential in Site Monitoring, 2023-2035
12.3.2.5. Cost Saving Potential in Source Data Verification, 2023-2035
12.3.2.6. Cost Saving Potential in Other Procedures, 2023-2035
12.4. Conclusion

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, 2018-2035
13.3.1. AI in Clinical Trials Market: Distribution by Trial Phase, 2023 and 2035
13.3.1.1. AI in Clinical Trials Market for Phase I, 2023-2035
13.3.1.2. AI in Clinical Trials Market for Phase II, 2023-2035
13.3.1.3. AI in Clinical Trials Market for Phase III, 2023-2035
13.3.2. AI in Clinical Trials Market: Distribution by Target Therapeutic Area, 2023 and 2035
13.3.2.1. AI in Clinical Trials Market for Cardiovascular Disorders, 2023-2035
13.3.2.2. AI in Clinical Trials Market for CNS Disorders, 2023-2035
13.3.2.3. AI in Clinical Trials Market for Infectious Diseases, 2023-2035
13.3.2.4. AI in Clinical Trials Market for Metabolic Disorders, 2023-2035
13.3.2.5. AI in Clinical Trials Market for Oncological Disorders, 2023-2035
13.3.2.6. AI in Clinical Trials Market for Other Disorders, 2023-2035
13.3.3. AI in Clinical Trials Market: Distribution by End-user, 2023 and 2035
13.3.3.1. AI in Clinical Trials Market for Pharmaceutical and Biotechnology Companies, 2023-2035
13.3.3.2. AI in Clinical Trials Market for Other End-users, 2023-2035
13.3.4. AI in Clinical Trials Market: Distribution by Key Geographical Regions, 2023 and 2035
13.3.4.1. AI in Clinical Trials Market in North America, 2023-2035
13.3.4.2. AI in Clinical Trials Market in Europe, 2023-2035
13.3.4.3. AI in Clinical Trials Market in Asia-Pacific, 2023-2035
13.3.4.4. AI in Clinical Trials Market in Middle East and North Africa, 2023-2035
10.3.4.4. AI in Clinical Trials Market in Latin America, 2023-2035

14. CONCLUSION

15. EXECUTIVE INSIGHTS
15.1. Chapter Overview
15.2. Ancora.ai
15.2.1. Company Snapshot
15.2.2. Interview Transcript: Danielle Ralic, Co-Founder, Chief Executive Officer and Chief Technology Officer
15.3. Deep 6 AI
15.3.1. Company Snapshot
15.3.2. Interview Transcript: Wout Brusselaers, Founder and Chief Executive Officer
15.4. Intelligencia
15.4.1. Company Snapshot
15.4.2. Interview Transcript: Dimitrios Skaltsas, Co-Founder and Executive Director
15.5. nQ Medical
15.5.1. Company Snapshot
15.5.2. Interview Transcript: R. A. Bavasso, Founder and Chief Executive Officer
15.6. Science 37
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 ORGANIZATION