Key Market Insights

• Presently, over 120 AI software and service providers claim to offer AI products / technologies for clinical trials to multiple end-users; over 90% of the players are headquartered in developed geographies.
• A large number of players involved in offering AI software and services have incorporated various types of AI technologies and adopted different business models to cater to the needs of end-users.
• The annual number of clinical studies, based on AI, has steadily evolved; this indicates the growing adoption of AI solutions by pharmaceutical and biotechnology companies, hospitals, research institutes and CROs.
• The rising interest is also evident from the partnership activity; more than 55% of the deals have been focused on oncological disorders.
• Given the vast potential of AI software and services in clinical studies for improving productivity and research outcomes, many investors have extended financial support; around USD 2.5 billion has been invested till date.
• Over time, several big pharma players have adopted AI software and services in clinical trials to speed up drug discovery and development programs across different therapeutic areas.
• Our proprietary value creation framework highlights the value addition and associated risks of integrating key tools and technologies in clinical trials.
• AI solutions hold significant cost saving potential, along with the ability to expedite trial outcomes and success, across various trial phases.
• The overall opportunity of AI in clinical trials is likely to grow at a CAGR of more than 15%; this opportunity is expected to be well distributed across clinical trial phases, therapeutic areas, end-users and geographical regions.

Table of Contents

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

3.5.                  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

5.4.                  Deep 6 AI

5.5.                  Innoplexus

5.6.                  IQVIA

5.7.                  Median Technologies

5.8.                  Medidata

5.9.                  Mendel.ai

5.10.                Phesi

5.11.                Saama Technologies

5.12.                Signant Health

5.13.                Trials.ai

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

6.3.12.             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

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

8.6.7.               Analysis by Geography

8.3.6.               Most Active Players: Analysis by Number of Funding Instances and Amount    

                                    Raised

8.3.7.               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

10.4.                Use Case 3: Collaboration between Teva Pharmaceuticals and Intel

10.5.                Use Case 4: Collaboration between Undisclosed Pharmaceutical Company and Antidote

10.6.                Use Case 5: Collaboration between Undisclosed Pharmaceutical Company and Cognizant

10.7.                Use Case 6: Collaboration between Cedars-Sinai Medical Center and Deep 6 AI

10.8.                Use Case 7: Collaboration between GlaxoSmithKline (GSK) and PathAI

10.9.                Use Case 8: Collaboration between Bristol Myers Squibb (BMS) and Concert AI

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

12.4.                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

13.3.2.             AI in Clinical Trials Market: Distribution by Target Therapeutic Area, 2023 and 2035

13.3.3.             AI in Clinical Trials Market: Distribution by End-user, 2023 and 2035

13.3.4.             AI in Clinical Trials Market: Distribution by Key Geographical Regions, 2023 and 2035

14. CONCLUSION

15. EXECUTIVE INSIGHTS

15.1.                Chapter Overview

15.2.                Ancora.ai

15.3.                Deep 6 AI

15.4.                Intelligencia

15.5.                nQ Medical

15.6.                Science 37

16. APPENDIX I: TABULATED DATA

17. APPENDIX II: LIST OF COMPANIES AND ORGANIZATIONS

To view more details on this report, click on the link

https://www.rootsanalysis.com/reports/ai-based-clinical-trial-solutions.html

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