임상시험용 AI 시장 : 시험 단계별, 대상 치료 영역별, 최종사용자별, 주요 지역별 : 업계 동향과 세계 예측(-2035년)

AI in Clinical Trials Market by Trial Phase, Target Therapeutic Area, End-User and Key Geographical Regions : Industry Trends and Global Forecasts, Till 2035

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한글목차

세계의 임상시험용 AI 시장 규모는 2035년까지의 예측 기간 중 16%의 CAGR로 확대하며, 현재 14억 2,000만 달러에서 2035년까지 85억 달러로 성장할 것으로 예측됩니다.

인공지능은 Drug Discovery, 임상시험, 치료, 진단, 개발에 혁명을 일으켜 환자 결과를 크게 개선함으로써 헬스케어 산업을 변화시킬 수 있는 엄청난 잠재력을 보여주고 있습니다. 현재 인공지능은 임상시험 프로세스를 최적화하고 환자 모집, 참여, 모니터링, 임상시험 설계의 미비점을 개선하는 데 활용되고 있습니다. 또한 첨단 음성 및 텍스트 인식 시스템은 의사와 환자 간의 효과적인 원격 커뮤니케이션을 가능하게 합니다. 또한 예측 AI 모델은 미래의 치료법 개발을 위해 대량의 데이터를 집계하고 분석할 수 있습니다. 하지만 임상 데이터세트를 규제 당국 및 데이터베이스와 통합하는 것은 AI를 헬스케어에 광범위하게 적용하는 데 있으며, 매우 중요합니다. 지난 수년간 다양한 생물학적 제제의 임상시험의 효율성을 향상시키기 위해 AI 기반 기술이 연구되어 왔으며, 이 분야에서 많은 자금 지원과 제휴 활동이 이루어졌습니다는 점은 주목할 만합니다.

또한 이 분야의 지속적인 혁신은 임상시험을 보다 효율적이고, 비용 효율적이며, 환자 중심적으로 만들어 임상시험을 크게 개선할 것으로 예상됩니다. 또한 머신러닝, 자연 언어 처리, 딥러닝, 데이터 과학은 복잡하고 시간이 많이 소요되는 임상시험을 단순화하고 보다 체계적이고 편리하게 만들 수 있을 것으로 기대됩니다. 임상시험에서 AI의 장점과 지속적인 발전에 힘입어 AI 기반 플랫폼에 대한 수요는 향후 수년간 지속될 것으로 예상됩니다.

세계의 임상시험용 AI 시장에 대해 조사했으며, 시장의 개요와 시험 단계별, 대상 치료 영역별, 최종사용자별, 지역별 동향 및 시장에 참여하는 기업의 개요 등을 제공하고 있습니다.

목차

제1장 서문

제2장 개요

제3장 서론

제4장 시장 구도

제5장 기업 개요

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

제6장 임상시험 분석

제7장 파트너십과 협업

제8장 자금조달과 투자 분석

제9장 대형 제약회사 구상

제10장 임상시험용 AI : 사용 사례

제11장 가치 창조 프레임워크 : 임상시험에서 미충족 요구에 대처하기 위한 전략 가이드

제12장 비용 삭감 분석

제13장 시장 예측과 기회 분석

제14장 결론

제15장 이그제큐티브 인사이트

제16장 부록 I : 표 형식 데이터

제17장 부록 II : 기업 및 조직 리스트

KSA

영문 목차

영문목차

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.

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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

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