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市場調查報告書
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1616885

臨床試驗的AI市場:實驗不同階段，各對象治療領域，各終端用戶，各主要地區:到2035年前的產業趨勢與全球預測

AI in Clinical Trials Market by Trial Phase, Target Therapeutic Area, End-User and Key Geographical Regions : Industry Trends and Global Forecasts, Till 2035
出版日期: | 出版商: Roots Analysis | 英文 304 Pages | 商品交期: 最快1-2個工作天內

價格

全球人工智慧臨床試驗市場規模預計將從目前的 14.2 億美元增長到 2035 年的 85 億美元，到 2035 年的預測期間複合年增長率為 16%。

人工智慧顯示出透過徹底改變藥物發現、臨床試驗、治療、診斷和開發來改變醫療保健產業的巨大潛力，顯著改善患者的治療結果。人工智慧目前被用來優化臨床試驗流程，並緩解患者招募、參與、監控和不良試驗設計等課題。此外，先進的語音和文字辨識系統可以實現醫生和患者之間有效的遠端通訊。此外，預測人工智慧模型可以匯總和分析大量數據，以用於未來的治療開發。然而，將臨床數據集與監管機構及其資料庫整合對於人工智慧在醫療保健領域的廣泛應用至關重要。值得注意的是，多年來，人們一直在探索基於人工智慧的技術來提高各種生物製劑臨床試驗的效率，並且在該領域有大量的資金和合作活動。

此外，該領域持續的技術創新預計將促使臨床試驗過程中的顯著改進，使臨床試驗更加高效、更具成本效益且以患者為中心。此外，機器學習、自然語言處理、深度學習和資料科學有望簡化複雜且耗時的臨床試驗，使其更加結構化和便利。在人工智慧在臨床試驗中的優勢和持續進步的推動下，預計未來幾年對基於人工智慧的平台的需求將持續下去。

本報告提供全球臨床試驗的AI市場相關調查，提供市場概要，以及實驗不同階段，各對象治療領域，各終端用戶，各地區的趨勢，及加入此市場的主要企業簡介等資訊。

目錄

第1章 序文

第2章 摘要整理

第3章 簡介

第4章 市場形勢

第5章 企業簡介

  • 章概要
  • AiCure
  • Antidote Technologies
  • Deep 6 AI
  • Innoplexus
  • IQVIA
  • Median Technologies
  • Medidata
  • Mendel.ai
  • Phesi
  • Saama Technologies
  • Signant Health
  • Trials.ai

第6章 臨床試驗的分析

第7章 夥伴關係和合作

第8章 資金籌措與投資分析

第9章 大型製藥公司的配合措施

第10章 臨床試驗的AI:使用案例

第11章 價值創造架構：解決臨床試驗中未滿足需求的策略指引

第12章 降低成本分析

第13章 市場預測與機會分析

第14章 結論

第15章 執行洞察

第16章 附錄I:表格形式資料

第17章 附錄II:企業及組織一覽

Product Code: RA100441

AI IN CLINICAL TRIALS MARKET: OVERVIEW

As per Roots Analysis, the global AI in clinical trials market is estimated to grow from USD 1.42 billion in the current year to USD 8.5 billion by 2035, at a CAGR of 16% during the forecast period, till 2035.

The market sizing and opportunity analysis has been segmented across the following parameters:

Trial Phase

  • Phase I
  • Phase II
  • Phase III

Target Therapeutic Area

  • Cardiovascular Disorders
  • CNS Disorders
  • Infectious Diseases
  • Metabolic Disorders
  • Oncological Disorders
  • Other Disorders

End-user

  • Pharmaceutical and Biotechnology Companies
  • Other End-users

Key Geographical Regions

  • North America
  • Europe
  • Asia-Pacific
  • Middle East and North Africa
  • Latin America

AI IN CLINICAL TRIALS MARKET: GROWTH AND TRENDS

Artificial intelligence has demonstrated immense potential in transforming the healthcare industry by revolutionizing drug discovery, clinical trials, treatment, diagnosis, and development, leading to significant improvements in patient outcomes. Currently, it is being used to optimize clinical trial processes and mitigate challenges, such as poor patient recruitment, engagement, monitoring, and study design. In addition, advanced speech and text recognition systems enable effective remote physician-patient communication. Further, predictive AI models can aggregate and analyze large volumes of data for future therapy development. Nevertheless, the integration of clinical datasets with regulatory authorities and their databases is crucial for the widespread application of AI in healthcare. It is worth mentioning that over the years, AI-based technologies have been explored to improve the efficiency of clinical trials for different biologics, with significant funding and partnership activity in this domain.

Additionally, with consistent innovation in the field, a substantial improvement during clinical trials is anticipated by making the trials more efficient, cost-effective and patient-centric. Moreover, machine learning, natural language processing, deep learning and data science are expected to simplify complex and time-consuming clinical studies, making them more structured and convenient. Driven by the benefits of AI in clinical trials and ongoing advancements, the demand for AI-based platforms is expected to continue in the coming years.

AI IN CLINICAL TRIALS MARKET: KEY INSIGHTS

The report delves into the current state of the AI in clinical trials market and identifies potential growth opportunities within the industry. Some key findings from the report include:

1. 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.

2. 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.

3. 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.

4. The rising interest is also evident from the partnership activity; more than 55% of the deals have been focused on oncological disorders.

5. Given the vast potential of AI software and services in clinical studies for improving productivity and research outcomes, many investors have extended financial support; over USD 2.3 billion has been invested till date.

6. 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.

7. AI solutions hold significant cost saving potential, along with the ability to expedite trial outcomes and success, across various trial phases.

8. 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.

AI IN CLINICAL TRIALS MARKET: KEY SEGMENTS

Phase III Clinical Studies Segment is the Fastest Growing Segment of the AI in Clinical Trials Market During the Forecast Period

Based on the trial phase, the market is segmented into phase I, phase II and phase III. At present, phase II clinical studies hold the maximum share of the AI in clinical trial market. This trend is unlikely to change in the near future. It is worth highlighting that AI in clinical trials market for phase III clinical studies is likely to grow at a higher CAGR.

Oncological Disorders Segment is Likely to Dominate the AI of Clinical Trials Market During the Forecast Period

Based on therapeutic areas, the market is segmented into cardiovascular disorders, CNS disorders, infectious diseases, metabolic disorders, oncological disorders and other disorders. It is worth highlighting that, at present, oncological disorders segment holds the larger share in the AI in clinical trials market.

Currently, Biotechnology and Pharmaceutical Companies Segment Occupies the Largest Share of the AI in Clinical Trial Market

Based on end-users, the market is segmented into pharmaceutical and biotechnology companies, and other end-users. It is worth highlighting that, at present, pharmaceutical and biotechnology companies hold a larger share in the AI in clinical trials market. This trend is likely to remain the same in the coming decade.

North America Accounts for the Largest Share of the Market

Based on key geographical regions, the market is segmented into North America, Europe, Asia-Pacific, Middle East and North Africa, and Latin America. Majority share is expected to be captured by players based in North America and Europe. It is worth highlighting that, over the years, the market in North America is expected to grow at a higher CAGR.

Example Players in the AI in Clinical Trials Market

  • AiCure
  • Antidote Technologies
  • Deep 6 AI
  • Innoplexus
  • IQVIA
  • Median Technologies
  • Medidata
  • Mendel.ai
  • Phesi
  • Saama Technologies
  • Signant Health
  • Trials.ai

Primary Research Overview

The opinions and insights presented in this study were influenced by discussions conducted with multiple stakeholders. The research report features detailed transcripts of interviews held with the following industry stakeholders:

  • Co-Founder, Chief Executive Officer and Chief Technology Officer, Ancora.ai
  • Founder and Chief Executive Officer, Deep 6 AI
  • Co-Founder and Executive Director, Intelligencia
  • Founder and Chief Executive Officer, nQ Medical
  • Chief Technology Officer, Chief Commercial Officer, Chief Delivery Officer and Head of Marketing, Science 37

AI IN CLINICAL TRIALS MARKET: RESEARCH COVERAGE

  • Market Sizing and Opportunity Analysis: The report features an in-depth analysis of the AI in clinical trials market, focusing on key market segments, including [A] trial phase, [B] target therapeutic area, [C] end-user and [D] key geographical regions.
  • Market Landscape: A comprehensive evaluation of companies offering AI software and services for clinical trials, based on various parameters, such as [A] year of establishment, [B] company size (in terms of number of employees), [C] location of headquarters, [D] key offering(s), [E] business model(s), [F] deployment option(s), [G] type of AI technology, [H] application area(s) and [I] potential end-user(s).
  • Company Profiles: In-depth profiles of key industry players offering AI software / AI solutions and services for clinical trials, focusing on [A] company overviews, [B] financial information (if available), [C] AI-based clinical trial offerings portfolio, [C] recent developments and [D] an informed future outlook.
  • Clinical Trials Analysis: Examination of completed, ongoing, and planned clinical studies leveraging AI, based on parameters like [A] trial registration year, [B] number of patients enrolled, [C] trial phase, [D] trial status, [E] type of sponsor, [F] patient gender, [G] patient age, [H] emerging focus areas, [I] target therapeutic area, [J] patient allocation model used, [K] trial masking adopted, [L] type of intervention, [M] trial purpose, [N] most active players (in terms of number of clinical trials sponsored) and [O] geography.
  • Partnerships and Collaborations: An analysis of partnerships established in this sector, since 2018, covering various types of partnership models, such as licensing agreements, R&D agreements, M&A and service alliance.
  • Funding and Investment Analysis: A detailed evaluation of the investments made in this domain, encompassing seed financing, venture capital financing, capital raised from IPOs, grants, debt financing and other equity, and subsequent offerings.
  • Big Pharma Initiatives: A detailed analysis of various initiatives focused on AI in clinical trials undertaken by big pharma companies based on multiple relevant parameters, such as [A] year of initiative, [B] type of initiative, [C] application area of AI, [D] target therapeutic area and [E] leading big pharma players (in terms of number of AI in clinical trials focused initiatives).
  • Value Creation Framework: An insightful framework depicting the implementation of several advanced tools and technologies, such as blockchain, big data analytics, real-world evidence, digital twins, cloud computing and internet of things (IoT) at different steps of a clinical study, which can assist service providers in addressing existing unmet needs. Further, it provides a comprehensive analysis on ease of implementation and associated risk in integrating above-mentioned technologies, based on the trends highlighted in published literature and patents.
  • Cost Saving Analysis: A comprehensive cost-saving analysis, highlighting the potential of AI to reduce expenses in clinical trials by 2035. We have outlined the cost-saving opportunities of AI across various trial phases (Phase I, Phase II, and Phase III) and procedures, including patient recruitment, patient retention, staffing and administration, site monitoring, source data verification, and other related processes.

KEY QUESTIONS ANSWERED IN THIS REPORT

  • How many companies are currently engaged in this market?
  • Which are the leading companies in this market?
  • What kind of partnership models are commonly adopted by industry stakeholders?
  • What factors are likely to influence the evolution of this market?
  • What is current and future market size?
  • What is the CAGR of this market?
  • How is the current and future market opportunity likely to be distributed across key market segments?

REASONS TO BUY THIS REPORT

  • The report provides a comprehensive market analysis, offering detailed revenue projections of the overall market and its specific sub-segments. This information is valuable to both established market leaders and emerging entrants.
  • Stakeholders can leverage the report to gain a deeper understanding of the competitive dynamics within the market. By analyzing the competitive landscape, businesses can make informed decisions to optimize their market positioning and develop effective go-to-market strategies.
  • The report offers stakeholders a comprehensive overview of the market, including key drivers, barriers, opportunities, and challenges. This information empowers stakeholders to stay abreast of market trends and make data-driven decisions to capitalize on growth prospects.

ADDITIONAL BENEFITS

  • Complimentary PPT Insights Packs
  • Complimentary Excel Data Packs for all Analytical Modules in the Report
  • 10% Free Content Customization
  • Detailed Report Walkthrough Session with Research Team
  • Free Updated report if the report is 6-12 months old or older

TABLE OF CONTENTS

1. PREFACE

  • 1.1. Introduction
  • 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
    • 8.6.7. 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, till 2035
    • 12.3.1. Cost Saving Potential: Distribution by Trial Phase, Current Year and 2035
      • 12.3.1.1. Cost Saving Potential in Phase I Clinical Trials, till 2035
      • 12.3.1.2. Cost Saving Potential in Phase II Clinical Trials, till 2035
      • 12.3.1.3. Cost Saving Potential in Phase III Clinical Trials, till 2035
    • 12.3.2. Cost Saving Potential: Distribution by Trial Procedure, Current Year and 2035
      • 12.3.2.1. Cost Saving Potential in Patient Recruitment, till 2035
      • 12.3.2.2. Cost Saving Potential in Patient Retention, till 2035
      • 12.3.2.3. Cost Saving Potential in Staffing and Administration, till 2035
      • 12.3.2.4. Cost Saving Potential in Site Monitoring, till 2035
      • 12.3.2.5. Cost Saving Potential in Source Data Verification, till 2035
      • 12.3.2.6. Cost Saving Potential in Other Procedures, till 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, till 2035
    • 13.3.1. AI in Clinical Trials Market: Distribution by Trial Phase, Current Year and 2035
      • 13.3.1.1. AI in Clinical Trials Market for Phase I, till 2035
      • 13.3.1.2. AI in Clinical Trials Market for Phase II, till 2035
      • 13.3.1.3. AI in Clinical Trials Market for Phase III, till 2035
    • 13.3.2. AI in Clinical Trials Market: Distribution by Target Therapeutic Area, Current Year and 2035
      • 13.3.2.1. AI in Clinical Trials Market for Cardiovascular Disorders, till 2035
      • 13.3.2.2. AI in Clinical Trials Market for CNS Disorders, till 2035
      • 13.3.2.3. AI in Clinical Trials Market for Infectious Diseases, till 2035
      • 13.3.2.4. AI in Clinical Trials Market for Metabolic Disorders, till 2035
      • 13.3.2.5. AI in Clinical Trials Market for Oncological Disorders, till 2035
      • 13.3.2.6. AI in Clinical Trials Market for Other Disorders, till 2035
    • 13.3.3. AI in Clinical Trials Market: Distribution by End-user, Current Year and 2035
      • 13.3.3.1. AI in Clinical Trials Market for Pharmaceutical and Biotechnology Companies, till 2035
      • 13.3.3.2. AI in Clinical Trials Market for Other End-users, till 2035
    • 13.3.4. AI in Clinical Trials Market: Distribution by Key Geographical Regions, Current Year and 2035
      • 13.3.4.1. AI in Clinical Trials Market in North America, till 2035
      • 13.3.4.2. AI in Clinical Trials Market in Europe, till 2035
      • 13.3.4.3. AI in Clinical Trials Market in Asia-Pacific, till 2035
      • 13.3.4.4. AI in Clinical Trials Market in Middle East and North Africa, till 2035
      • 10.3.4.4. AI in Clinical Trials Market in Latin America, till 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

List of Tables

  • Table 4.1 AI in Clinical Trials: Information on Year of Establishment, Company Size, Location of Headquarters
  • Table 4.2 AI in Clinical Trials: Information on Key Offering(s), Business Model(s) and Deployment Option(s)
  • Table 4.3 AI in Clinical Trials: Information on Type of AI Technology and Application Area(s)
  • Table 4.4 AI in Clinical Trials: Information on Potential End-user(s)
  • Table 5.1 AI in Clinical Trials: List of Companies Profiled
  • Table 5.2 AiCure: Company Snapshot
  • Table 5.3 AiCure: AI-based Clinical Trial Offerings
  • Table 5.4 AiCure: Recent Developments and Future Outlook
  • Table 5.5 Antidote Technologies: Company Snapshot
  • Table 5.6 Antidote Technologies: AI-based Clinical Trial Offerings
  • Table 5.7 Antidote Technologies: Recent Developments and Future Outlook
  • Table 5.8 Deep 6 AI: Company Snapshot
  • Table 5.9 Deep 6 AI: AI-based Clinical Trial Offerings
  • Table 5.10 Deep 6 AI: Recent Developments and Future Outlook
  • Table 5.11 Innoplexus: Company Snapshot
  • Table 5.12 Innoplexus: AI-based Clinical Trial Offerings
  • Table 5.13 Innoplexus: Recent Developments and Future Outlook
  • Table 5.14 IQVIA: Company Snapshot
  • Table 5.15 IQVIA: AI-based Clinical Trial Offerings
  • Table 5.16 IQVIA: Recent Developments and Future Outlook
  • Table 5.17 Median Technologies: Company Snapshot
  • Table 5.18 Median Technologies: AI-based Clinical Trial Offerings
  • Table 5.19 Median Technologies: Recent Developments and Future Outlook
  • Table 5.20 Medidata: Company Snapshot
  • Table 5.21 Medidata: AI-based Clinical Trial Offerings
  • Table 5.22 Medidata: Recent Developments and Future Outlook
  • Table 5.23 Mendel.ai: Company Snapshot
  • Table 5.24 Mendel.ai: AI-based Clinical Trial Offerings
  • Table 5.25 Mendel.ai: Recent Developments and Future Outlook
  • Table 5.26 Phesi: Company Snapshot
  • Table 5.27 Phesi: AI-based Clinical Trial Offerings
  • Table 5.28 Phesi: Recent Developments and Future Outlook
  • Table 5.29 Saama Technologies: Company Snapshot
  • Table 5.30 Saama Technologies: AI-based Clinical Trial Offerings
  • Table 5.31 Saama Technologies: Recent Developments and Future Outlook
  • Table 5.32 Signant Health: Company Snapshot
  • Table 5.33 Signant Health: AI-based Clinical Trial Offerings
  • Table 5.34 Signant Health: Recent Developments and Future Outlook
  • Table 5.35 Trials.ai: Company Snapshot
  • Table 5.36 Trials.ai: AI-based Clinical Trial Offerings
  • Table 6.1 AI in Clinical Trials: List of Partnerships and Collaborations, since 2018
  • Table 7.1 AI in Clinical Trials: List of Funding and Investments, since 2018
  • Table 7.2 Funding and Investment Analysis: Summary of Investments
  • Table 15.1 Ancora.ai: Company Snapshot
  • Table 15.2 Deep 6 AI: Company Snapshot
  • Table 15.3 Intelligencia: Company Snapshot
  • Table 15.4 nQ Medical: Company Snapshot
  • Table 15.5 Science 37: Company Snapshot
  • Table 16.1 AI in Clinical Trials: Distribution by Year of Establishment
  • Table 16.2 AI in Clinical Trials: Distribution by Company Size
  • Table 16.3 AI in Clinical Trials: Distribution by Location of Headquarters
  • Table 16.4 AI in Clinical Trials: Distribution by Company Size and Location of Headquarters (Region)
  • Table 16.5 AI in Clinical Trials: Distribution by Key Offering(s)
  • Table 16.6 AI in Clinical Trials: Distribution by Business Model(s)
  • Table 16.7 AI in Clinical Trials: Distribution by Deployment Option(s)
  • Table 16.8 AI in Clinical Trials: Distribution by Type of AI Technology
  • Table 16.9 AI in Clinical Trials: Distribution by Application Area(s)
  • Table 16.10 AI in Clinical Trials: Distribution by Potential End-user(s)
  • Table 16.11 IQVIA: Annual Revenues, FY 2018 Onwards (USD Million)
  • Table 16.12 Median Technologies: Annual Revenues, FY 2018 Onwards (EUR Million)
  • Table 16.13 Dassault Systems (Parent Company of Medidata): Annual Revenues, FY 2018 Onwards (EUR Million)
  • Table 16.14 Clinical Trial Analysis: Distribution by Trial Registration Year
  • Table 16.15 Clinical Trial Analysis: Distribution of Patients Enrolled by Trial Registration Year
  • Table 16.16 Clinical Trial Analysis: Distribution by Trial Phase
  • Table 16.17 Clinical Trial Analysis: Distribution by Trial Status
  • Table 16.18 Clinical Trial Analysis: Distribution by Trial Registration Year and Status
  • Table 16.19 Clinical Trial Analysis: Distribution by Type of Sponsor
  • Table 16.20 Clinical Trial Analysis: Distribution by Patient Gender
  • Table 16.21 Clinical Trial Analysis: Distribution by Patient Age
  • Table 16.22 Clinical Trial Analysis: Distribution by Target Therapeutic Area
  • Table 16.23 Clinical Trial Analysis: Distribution by Type of Patient Allocation Model Used
  • Table 16.24 Clinical Trial Analysis: Distribution by Type of Trial Masking Adopted
  • Table 16.25 Clinical Trial Analysis: Distribution by Type of Intervention
  • Table 16.26 Clinical Trial Analysis: Distribution by Trial Purpose
  • Table 16.27 Most Active Players: Distribution by Number of Clinical Trials
  • Table 16.28 Partnerships and Collaborations: Cumulative Year-wise Trend, since 2018
  • Table 16.29 Partnerships and Collaborations: Distribution by Type of Partnership
  • Table 16.30 Partnerships and Collaborations: Distribution by Year and Type of Partnership, since 2018
  • Table 16.31 Partnerships and Collaborations: Distribution by Application Area
  • Table 16.32 Partnerships and Collaborations: Distribution by Target Therapeutic Area
  • Table 16.33 Partnerships and Collaborations: Distribution by Type of Partner
  • Table 16.34 Most Active Players: Distribution by Number of Partnership
  • Table 16.35 Partnerships and Collaborations: Local and International Agreements
  • Table 16.36 Partnerships and Collaborations: Intercontinental and Intracontinental Agreements
  • Table 16.37 Funding and Investment Analysis: Cumulative Year-wise Trend, since 2018
  • Table 16.38 Funding and Investment Analysis: Distribution by Amount Invested (USD Million)
  • Table 16.39 Funding and Investment Analysis: Distribution by Type of Funding
  • Table 16.40 Funding and Investment Analysis: Distribution by Year and Type of Funding
  • Table 16.41 Funding and Investment Analysis: Distribution by Type of Funding and Amount Invested (USD Million)
  • Table 16.42 Most Active Players: Distribution by Amount Raised and Number of Funding Instances
  • Table 16.43 Leading Investors: Distribution by Number of Funding Instances
  • Table 16.44 Funding and Investment Analysis: Distribution of Amount Invested by Geography
  • Table 16.45 Funding and Investment Analysis: Distribution of Number of Funding Instances by Geography
  • Table 16.46 Big Pharma Initiatives: Distribution by Year of Initiative
  • Table 16.47 Big Pharma Initiatives: Distribution by Type of Initiative
  • Table 16.48 Big Pharma Initiatives: Distribution by Application Area of AI
  • Table 16.49 Big Pharma Initiatives: Distribution by Target Therapeutic Area
  • Table 16.50 Value Creation Framework: Trends in Research Activity
  • Table 16.51 Value Creation Framework: Trends in Intellectual Property
  • Table 16.52 Overall Cost Saving Potential of AI in Clinical Trials, Current Year and 2035 (USD Million)
  • Table 16.53 Overall Cost Saving Potential of AI in Clinical Trials, till 2035 (USD Million)
  • Table 16.54 Cost Saving Potential: Distribution by Trial Phase, Current Year and 2035 (USD Million)
  • Table 16.55 Cost Saving Potential in Phase I Clinical Trials, till 2035 (USD Million)
  • Table 16.56 Cost Saving Potential in Phase II Clinical Trials, till 2035 (USD Million)
  • Table 16.57 Cost Saving Potential in Phase III Clinical Trials, till 2035 (USD Million)
  • Table 16.58 Cost Saving Potential: Distribution by Trial Procedures, Current Year and 2035 (USD Million)
  • Table 16.59 Cost Saving Potential in Patient Recruitment, till 2035 (USD Million)
  • Table 16.60 Cost Saving Potential in Patient Retention, till 2035 (USD Million)
  • Table 16.61 Cost Saving Potential in Staffing and Administration, till 2035 (USD Million)
  • Table 16.62 Cost Saving Potential in Site Monitoring, till 2035 (USD Million)
  • Table 16.63 Cost Saving Potential in Source Data Verification, till 2035 (USD Million)
  • Table 16.64 Cost Saving Potential in Other Procedures, till 2035 (USD Million)
  • Table 16.65 Global AI in Clinical Trials Market, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 16.66 AI in Clinical Trials Market: Distribution by Trial Phase, Current Year and 2035
  • Table 16.67 AI in Clinical Trials Market: Distribution by Trial Phase, Current Year and 2035 (USD Million)
  • Table 16.68 AI in Clinical Trials Market for Phase I, Conservative, Base and Optimistic Scenarios, till 2035 (USD Million)
  • Table 16.69 AI in Clinical Trials Market for Phase II, Conservative, Base and Optimistic Scenarios, till 2035 (USD Million)
  • Table 16.70 AI in Clinical Trials Market for Phase III, Conservative, Base and Optimistic Scenarios, till 2035 (USD Million)
  • Table 16.71 AI in Clinical Trials Market: Distribution by Target Therapeutic Area, Current Year and 2035
  • Table 16.72 AI in Clinical Trials Market: Distribution by Target Therapeutic Area, Current Year and 2035 (USD Million)
  • Table 16.73 AI in Clinical Trials Market for Cardiovascular Disorders, Conservative, Base and Optimistic Scenarios, till 2035 (USD Million)
  • Table 16.74 AI in Clinical Trials Market for CNS Disorders, Conservative, Base and Optimistic Scenarios, till 2035 (USD Million)
  • Table 16.75 AI in Clinical Trials Market for Infectious Diseases, Conservative, Base and Optimistic Scenarios, till 2035 (USD Million)
  • Table 16.76 AI in Clinical Trials Market for Metabolic Disorders, Conservative, Base and Optimistic Scenarios, till 2035 (USD Million)
  • Table 16.77 AI in Clinical Trials Market for Oncological Disorders, Conservative, Base and Optimistic Scenarios, till 2035 (USD Million)
  • Table 16.78 AI in Clinical Trials Market for Other Disorders, Conservative, Base and Optimistic Scenarios, till 2035 (USD Million)
  • Table 16.79 AI in Clinical Trials Market: Distribution by End-user, Current Year and 2035
  • Table 16.80 AI in Clinical Trials Market: Distribution by End-user, Current Year and 2035 (USD Million)
  • Table 16.81 AI in Clinical Trials Market for Biotechnology and Pharmaceutical Companies, Conservative, Base and Optimistic Scenarios, till 2035 (USD Million)
  • Table 16.82 AI in Clinical Trials Market for Academia and Other End-users, Conservative, Base and Optimistic Scenarios, till 2035 (USD Million)
  • Table 16.83 AI in Clinical Trials Market: Distribution by Key Geographical Regions, Current Year and 2035 (USD Million)
  • Table 16.84 AI in Clinical Trials Market in North America, Conservative, Base and Optimistic Scenarios, till 2035 (USD Million)
  • Table 16.85 AI in Clinical Trials Market in Europe, Conservative, Base and Optimistic Scenarios, till 2035 (USD Million)
  • Table 16.86 AI in Clinical Trials Market in Asia-Pacific, Conservative, Base and Optimistic Scenarios, till 2035 (USD Million)
  • Table 16.87 AI in Clinical Trials Market in Middle East and North Africa, Conservative, Base and Optimistic Scenarios, till 2035 (USD Million)
  • Table 16.88 AI in Clinical Trials Market in Latin America, Conservative, Base and Optimistic Scenarios, till 2035 (USD Million)