AI in Clinical Trials Market (2nd Edition): AI Software and Service Providers, Distribution by Trial Phase (Phase I, Phase II and Phase III), Target Therapeutic Area (Cardiovascular Disorders, CNS Disorders, Infectious Diseases, Metabolic Disorders, Oncological Disorders and Other Disorders), End-user (Pharmaceutical and Biotechnology Companies, and Other End-users) and Key Geographical Regions (North America, Europe, Asia-Pacific, Latin America, and Middle East and North Africa ): Industry Trends and Global Forecasts, 2023-2035
The AI in clinical trials market is expected to reach USD 8.50 billion by 2035 and is anticipated to grow at a CAGR of 16% during the forecast period 2023-2035
Creating a novel therapeutic intervention requires substantial resources and involves a multifaceted, resource-intensive process. The journey from conceptualization to market availability necessitates significant investments in both time and finances, often estimated at around 10 years and requiring an investment exceeding $2.5 billion for a single drug. A pivotal phase in this intricate process is the clinical trial stage, consuming nearly half of the time and capital invested in drug development. Regrettably, sponsors frequently encounter financial constraints and considerable delays in bringing drugs to market due to unsuccessful clinical trials. Over recent decades, the success rate of a drug candidate progressing from clinical trials to obtaining marketing approval has persistently remained low, fluctuating between 10% to 20%. This persistent challenge stems from various factors contributing to the failure of interventions during the clinical stage, encompassing suboptimal study design, inadequate patient recruitment, insufficient subject stratification, and a high attrition rate among clinical trial participants.
To overcome these challenges and optimize the clinical trial processes, stakeholders in the pharmaceutical industry are actively exploring innovative solutions and strategies. One noteworthy strategy involves the incorporation of Artificial Intelligence (AI) in drug development, potentially revolutionizing conventional methods, especially within clinical trials. Notably, AI possesses the capability to integrate and analyze vast datasets, empowering trial sponsors to optimize future research endeavors. By addressing issues related to trial design, patient recruitment and retention, site selection, data interpretation, and treatment assessment, AI holds the promise of enhancing and refining the entire clinical drug development process. Moreover, the investment landscape underscores the escalating interest in AI within the healthcare sector, particularly in the domain of clinical trials. The year 2021 witnessed a substantial surge, with more than $20 billion directed toward AI companies focused on healthcare, surpassing the previous investment of approximately $15 billion in 2020. This surge in investor interest signals the potential for robust growth in the AI in clinical trials market during the forecast period.
Report Coverage
Creating a novel therapeutic intervention requires substantial resources and involves a multifaceted, resource-intensive process. The journey from conceptualization to market availability necessitates significant investments in both time and finances, often estimated at around 10 years and requiring an investment exceeding $2.5 billion for a single drug. A pivotal phase in this intricate process is the clinical trial stage, consuming nearly half of the time and capital invested in drug development. Regrettably, sponsors frequently encounter financial constraints and considerable delays in bringing drugs to market due to unsuccessful clinical trials. Over recent decades, the success rate of a drug candidate progressing from clinical trials to obtaining marketing approval has persistently remained low, fluctuating between 10% to 20%. This persistent challenge stems from various factors contributing to the failure of interventions during the clinical stage, encompassing suboptimal study design, inadequate patient recruitment, insufficient subject stratification, and a high attrition rate among clinical trial participants.
To overcome these challenges and optimize the clinical trial processes, stakeholders in the pharmaceutical industry are actively exploring innovative solutions and strategies. One noteworthy strategy involves the incorporation of Artificial Intelligence (AI) in drug development, potentially revolutionizing conventional methods, especially within clinical trials. Notably, AI possesses the capability to integrate and analyze vast datasets, empowering trial sponsors to optimize future research endeavors. By addressing issues related to trial design, patient recruitment and retention, site selection, data interpretation, and treatment assessment, AI holds the promise of enhancing and refining the entire clinical drug development process. Moreover, the investment landscape underscores the escalating interest in AI within the healthcare sector, particularly in the domain of clinical trials. The year 2021 witnessed a substantial surge, with more than $20 billion directed toward AI companies focused on healthcare, surpassing the previous investment of approximately $15 billion in 2020. This surge in investor interest signals the potential for robust growth in the AI in clinical trials market during the forecast period.
Report Coverage
- The report conducts an examination of the AI in clinical trials market, analyzing it based on trial phase, target therapeutic area, end-user, and key geographical regions.
- An analysis is performed to evaluate the factors—such as drivers, restraints, opportunities, and challenges—that impact the growth of this market.
- An assessment is made of the potential advantages and obstacles within the market, offering insights into the competitive landscape for leading market players.
- Revenue forecasts for market segments are provided concerning five major regions.
- An executive summary is included, offering insights into the current state and potential evolution of the AI in Clinical Trials market. This overview delves into AI, its subfields, applications in healthcare and clinical trials, adoption challenges, and future perspectives.
- An evaluation is conducted on companies providing AI software and services for clinical trials, considering parameters like establishment year, company size, headquarters location, key offerings (device, technology/platform, service), business models, deployment options, AI technologies used, application areas, and potential end-users.
- Detailed profiles are crafted for select companies meeting specific criteria. Each profile includes company overview (establishment year, employee count, HQ location, leadership team), financial information (if available), AI-based clinical trial offerings, recent developments, and future outlook.
- An insightful analysis is presented on completed/ongoing AI-based clinical trials, considering parameters such as trial registration year, patient enrollment, phase, sponsor type, demographics, therapeutic areas, allocation models, masking, interventions, purposes, active players, and geographic locations.
- Examination is made of partnerships formed since 2018 in the AI in Clinical Trials market, encompassing utilization agreements, integrations, licensing, R&D collaborations, mergers, acquisitions, service agreements, alliances, and other relevant collaborations.
- A detailed analysis is conducted on investments (seed financing, VC funding, IPOs, grants, debt financing, equity) at various developmental stages in startups and mid-sized companies focused on AI software and services for clinical trials.
- Analysis is provided on initiatives by major pharmaceutical players in AI in clinical trials, considering parameters like initiative year, type, application area, therapeutic focus, and leading companies involved in AI-focused initiatives.
- A framework is presented depicting the implementation of advanced tools (blockchain, big data analytics, real-world evidence, digital twins, cloud computing, IoT) in different stages of clinical studies, analyzing ease of implementation and associated risks based on literature trends and patents.
- A detailed cost-saving analysis is presented projecting potential savings with AI in clinical trials until 2035, highlighting savings in different phases and procedures (recruitment, retention, staffing, administration, monitoring, data verification) with formalized figures and projections.
- AiCure
- Antidote Technologies
- Deep 6 AI
- Innoplexus
- IQVIA
- Median Technologies
- Medidata
- Mendel.ai
- Phesi
- Saama Technologies
- Signant Health
- Trials.ai
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
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
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
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
