AI in Clinical Trials Market (2nd Edition): AI Software and Service Providers - Distribution by Trial Phase, Target Therapeutic Area, End-user and Key Geographical Regions: Industry Trends and Global Forecasts, 2023-2035

List of Tables

INTRODUCTION

The global AI in clinical trials market is estimated to be worth $ 1.4 billion in 2023 and expected to grow at compounded annual growth rate (CAGR) of 16% during the forecast period.

The process of successfully developing a novel therapeutic intervention is both time and cost intensive. In fact, it is estimated that a drug requires around 10 years and over $ 2.5 billion capital investment, before reaching the market. , In this process, clinical trials play a crucial role for assessing the drug's efficacy and safety in humans. These trials account for nearly 50% of the time and capital expenditure during drug development. However, sponsors face financial burdens and significant delays in marketing drugs due to unsuccessful clinical trials. Over the past few decades, the success rate of a drug candidate advancing the clinical trials to obtaining marketing approval has remained relatively constant at approximately 10% - 20%. This can be attributed to the factors contributing to clinical stage intervention failure, including inadequate study design, incomplete patient recruitment, improper subject stratification and high rate of clinical trial participant attrition. In order to overcome these challenges and streamline the clinical trial processes, stakeholders in the pharmaceutical industry are exploring innovative solutions and strategies. One such innovative strategy involves integrating AI in drug development, which has the potential to revolutionize traditional methods, particularly in clinical trials. It is worth noting that artificial intelligence in clinical trials can help integrate and analyze large volumes of data, enabling trial sponsors to optimize future research initiatives. Additionally, by addressing issues related to trial design, patient recruitment and retention, site selection, data interpretation, and treatment evaluation, AI has the potential to enhance and refine the entire process of clinical drug development. Moreover, in the first nine months of 2021, more than $20 billion was invested into artificial intelligence companies focused on healthcare, exceeding the prior investment, which was around $15 billion in 2020. Therefore, with the rising interest of investors in this field, we anticipate the AI in clinical trials market to witness healthy growth during the forecast period.

SCOPE OF THE REPORT

The 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 report features an extensive study of the current market landscape, market size and future opportunities associated with the AI in clinical trials market, during the given forecast period. Further, the report highlights the efforts of several stakeholders engaged in this rapidly emerging segment of the pharmaceutical industry. Key takeaways of the AI in clinical trials market report are briefly discussed below.

Benefits and Growing Demand for Artificial Intelligence Solutions for Patient Recruitment and Clinical Data Analysis

AI solutions have emerged as a promising tool in the drug development process. These AI tools help companies improve the accuracy and efficiency of testing, accelerate drug development and optimize clinical trial outcomes. In addition, leveraging AI software in clinical trials helps increasing patient recruitment and retention, reduces trial time and cost, and provides more accurate clinical data analysis, personalized medicine, trial design and real-time patient monitoring. It is worth highlighting that the ability of AI to automate and streamline labor-intensive tasks, improve decision-making processes, and identify patterns and trends in complex datasets has garnered significant attention and interest from stakeholders in the pharmaceutical industry. In May 2023, US based Owkin received letter of support from the European Medicines Agency (EMA) for the use of proprietary deep learning models for oncology clinical trial analysis; the company believes that this can reduce the clinical trial failure rates in randomized clinical trial. Further several artificial intelligence companies have developed AI-powered platforms that optimize patient identification for clinical trials. Additionally, AI algorithms can be trained to analyze large amounts of data in electronic health records to identify eligible participants.

Owing to these applications and recognition of the immense potential of AI by researchers and sponsors, the demand for AI clinical trials is likely to continue to grow and transform the landscape of drug development by improving patient outcomes in clinical trials.

Current Market Landscape of AI in Clinical Trials: AI Software and Service Providers

The AI in clinical trials market landscape features a mix of large, mid-sized and small companies. Currently, around 130 players have the required expertise to offer various software and services to streamline clinical studies. Notably, at present, around 80% of these AI in clinical trials software and service providers are focusing on leveraging machine learning and deep learning algorithms, as they minimize data-based errors by accessing various data points simultaneously. Recent developments in this field indicate that the artificial intelligence companies in clinical trials are upgrading their capabilities to accommodate the current and anticipated demand for these software and services.

Partnership and Collaboration Trends in the AI in Clinical Trials Market

In recent years, several artificial intelligence companies have inked partnerships related to AI in clinical trials domain with other industry / non-industry players. It is worth highlighting that, since 2018, a significant number of strategic partnerships have been inked in the AI in clinical trials industry. It is worth highlighting that product / technology utilization and integration agreements are the most common types of partnerships inked by stakeholders in the AI clinical trials field. Owing to several advantages of artificial intelligence in clinical trials, stakeholders are acquiring other industry players offering AI solutions / AI software for different clinical trial applications in order to expand their capabilities and build a comprehensive product / service portfolio. In February 2023, ZS acquired Trials.ai, an intelligent study design company, to enhance its end-to-end solutions to reimagine study design for its clients. In addition, several big pharma companies, such as Bristol Myers Squibb, GlaxoSmithKline (GSK), Johnson & Johnson, Merck, Pfizer and Roche, have also taken partnership initiatives related to AI in clinical trials, indicating the promise and benefits that AI technology holds in clinical trials.

Key Trends in the AI in Clinical Trials Market

In the past six years, around 600 completed / ongoing clinical trials utilized AI tools and technologies for evaluating drugs / therapies for different therapeutic areas, indicating the substantial efforts made by researchers engaged in this domain. Further, most of the clinical studies were designed for the purpose of diagnostics and treatment. It is worth noting that the University of California, the National Institute of Allergy and Infectious Diseases, and Mayo Clinic are among the most active sponsors of completed / ongoing clinical trials involving AI solutions.

Rise in Investment in AI in Clinical Trials Market

The heightened interest in the AI in clinical trials market can be validated by the fact that, in the last five years, close to $2.5 billion has been invested in companies engaged in providing AI software and services for clinical trials by several investors based across the globe. The majority of the funds have been raised through venture rounds, followed by seed financing rounds. In addition, several big pharma players, such as Bristol Myers Squibb, Merck, Novartis, Pfizer and Sanofi have also invested in AI software and service providers for clinical trials. In June 2021, Antidote Technologies raised $23 million to expand its digital patient engagement programs and clinical trial recruitment services.

AI in Clinical Trials Market Size

Driven by the rising demand for artificial intelligence in clinical trials, lucrative opportunities are expected to emerge for players offering AI technology for clinical studies. The global market for AI in clinical trials is anticipated to grow at a significant pace, with a CAGR of 16% during the forecast period. Among the therapeutic areas for which AI tools are leveraged in clinical trials, oncological disorders are most likely to adopt these AI solutions for streamlining processes, such as patient recruitment and retention, trial design, site selection, clinical data analysis, patient monitoring and personalized treatment. In terms of end-users, biotechnology and pharmaceutical companies are likely to hold the majority share (75%) of the AI in clinical trials market.

Key Artificial Intelligence Companies Supporting Clinical Trials

Examples of the key companies engaged in the AI in clinical trials domain (the complete list of players is available in the full report) include (in alphabetic order) Acclinate, AiCure, Aidar Health, Aitia, A.I. VALI, Ancora.ai, Antidote Technologies, Beacon Biosignals, BUDDI.AI, ConcertAI, Curify, Deep 6 AI, ICON, Innoplexus, Massive Bio, Median Technologies, Novadiscovery, Owkin, PHASTAR, SiteRx and Viz.ai. This market report also includes an easily searchable excel database of all the AI software / AI solutions and service providers for clinical trials worldwide.

The research report presents an in-depth analysis, highlighting the capabilities of various stakeholders engaged in this market, across different geographies. Amongst other elements, the market report includes:

An executive summary of the insights captured during our research. It offers a high-level view on the current scenario of AI in clinical trials market and its likely evolution in the mid to long term.
A general overview of artificial intelligence in clinical trials, highlighting details on artificial intelligence and its subfields. It also presents information on the applications of AI in healthcare and clinical trials, and challenges associated with the adoption of AI. Additionally, it features a discussion on the future perspectives of the AI in clinical trials industry.
A detailed assessment of the current market landscape of the companies offering AI software and service for clinical trials, based on several relevant parameters, such as year of establishment, company size (in terms of number of employees), location of headquarters, key offering(s) (device, technology / platform and service), business model(s) (software as a service (SaaS), technology licensing, CRO / fee-for-service model and product provider), deployment option(s) (cloud-based and on-premise), type of AI technology (machine learning, deep learning, natural language processing and others), application area(s) (data analysis, medical imaging, patient recruitment, trial design, site selection, patient engagement, integrated patient care, patient trial monitoring, personalized treatment and report generation) and potential end-user(s) (pharmaceutical / biotechnology companies, hospitals, research institutes and CROs).
Elaborate profiles of the prominent companies (shortlisted based on a proprietary criterion) developing AI software / AI solutions and offering services for clinical trials. Each profile features a brief overview of the company (including information on its year of establishment, number of employees, location of headquarters and key members of the leadership team), financial information (if available), details related to AI-based clinical trial offerings, recent developments and an informed future outlook.
An insightful clinical trial analysis of completed / ongoing clinical trials leveraging AI, based on various relevant parameters, such as trial registration year, number of patients enrolled, trial phase, trial status, type of sponsor, patient gender, patient age, emerging focus areas, target therapeutic area, patient allocation model used, trial masking adopted, type of intervention, trial purpose, most active players (in terms of number of clinical trials sponsored) and geography.
A detailed analysis of the partnerships inked between stakeholders in the AI in clinical trials market, since 2018, covering product / technology utilization agreements, product / technology integration agreements, technology licensing agreements, research and development agreements, product development agreements, mergers and acquisitions, service agreements, service alliances and other relevant agreements.
An analysis of the investments made, including seed financing, venture capital financing, capital raised from IPOs, grants, debt financing and other equity, and subsequent offerings, at various stages of development in start-ups, small and mid-sized companies that are focused on offering AI software and services for clinical trials.
A detailed analysis of the initiatives taken by big pharma players related to AI in clinical trials, based on various relevant parameters, such as year of initiative, type of initiative, application area of AI, target therapeutic area and leading big pharma players (in terms of number of AI in clinical trials focused initiatives).
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 detailed analysis on ease of implementation and associated risk in integrating above-mentioned technologies, based on the trends highlighted in published literature and patents.
A detailed cost saving analysis, highlighting the overall cost saving potential of AI in clinical trials till 2035. We have highlighted the cost saving potential of AI in clinical trials for different trial phases (phase I, phase II and phase III) and trial procedures (patient recruitment, patient retention, staffing and administration, site monitoring, source data verification and other procedures).

One of the key objectives of this market report was to estimate the current market size, opportunity and the future growth potential of AI in clinical trials market, over the forecast period. We have provided informed estimates on the likely evolution of the market for the forecast period, 2023-2035. Additionally, historical trends of the market have also been presented for the time period, 2018-2022. Further, our year-wise projections of the current and forecasted opportunity have been segmented based on relevant parameters, such as 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). In order to account for future uncertainties associated with some of the key parameters and to add robustness to our model, we have provided three market forecast scenarios, namely conservative, base and optimistic scenarios, representing different tracks of the market growth.

The opinions and insights presented in the report were influenced by discussions held with stakeholders in this industry. The report also features detailed transcripts of interviews held with various industry stakeholders:

Danielle Ralic (Co-Founder, Chief Executive Officer and Chief Technology Officer, Ancora.ai)

Wout Brusselaers (Founder and Chief Executive Officer, Deep 6 AI)

Dimitrios Skaltsas (Co-Founder and Executive Director, Intelligencia)

R. A. Bavasso (Founder and Chief Executive Officer, nQ Medical)

Grazia Mohren (Head of Marketing), Michael Shipton (Chief Commercial Officer), Darcy Forman (Chief Delivery Officer), Troy Bryenton (Chief Technology Officer, Science 37)

All actual figures have been sourced and analyzed from publicly available information forums and primary research discussions. Financial figures mentioned in this report are in USD, unless otherwise specified.

RESEARCH METHODOLOGY

The data presented in this report has been gathered via secondary and primary research. For all our projects, we conduct interviews / surveys with experts in the area (academia, industry, medical practice and other associations) to solicit their opinions on emerging trends in the market. This is primarily useful for us to draw out our own opinion on how the market will evolve across different regions and technology segments. Wherever possible, the available data has been checked for accuracy from multiple sources of information.

The secondary sources of information include:

Annual reports
Investor presentations
SEC filings
Industry databases
News releases from company websites
Government policy documents
Industry analysts' views

While the focus has been on forecasting the market till 2035, the report also provides our independent view on various technological and non-commercial trends emerging in the industry. This opinion is solely based on our knowledge, research and understanding of the relevant market gathered from various secondary and primary sources of information.

KEY QUESTIONS ANSWERED:

Question 1: How is AI and ML used in clinical trials?

Answer: AI and machine learning are used to enhance various aspects of the clinical trial process. They can help in patient recruitment by analyzing large datasets to identify suitable candidates, improving the trial design by simulating and optimizing protocols, and aiding in data analysis by automating the extraction and interpretation of information from medical records and trial data. Additionally, AI and ML can contribute to diverse event detection and monitoring, improving safety and efficiency in clinical trials.

Question 2: How AI can improve clinical trials?

Answer: AI and machine learning can help reduce the time and cost associated with conducting clinical studies.

Question 3: What are the challenges associated with the integration of AI in clinical trials?

Answer: Integrating AI in clinical trials involves various challenges, such as ensuring data quality and availability, enhancing interpretability and transparency of AI algorithms, addressing regulatory compliance and ethical considerations, and relying on human expertise to validate and interpret AI-generated insights. Furthermore, incorporating AI tools into existing clinical trial processes and workflows can give rise to logistic and operational complexities.

Question 4: What is the role of AI in electronic health records of clinical trials data?

Answer: AI in electronic health records (EHRs) of clinical trials offer several benefits. It can help automate data extraction and analysis from EHRs, improving efficiency and accuracy. Additionally, AI algorithms can identify patterns and trends in patient data, aiding in patient stratification, adverse event detection, and treatment response prediction. Furthermore, AI can assist in identifying potential eligibility criteria for clinical trials and facilitate the identification of suitable participants.

Question 5: What are the upcoming trends in AI in clinical trial market?

Answer: The field of AI is rapidly evolving; new trends and advancements of artificial intelligence in clinical trials include the integration of tools and technologies, such as digital twins, real-world evidence, blockchain, big data analytics, cloud computing and internet of things (IoT) in order to streamline clinical trials and achieve desired outcome.

Question 6: What is the global market size of AI in clinical trials market?

Answer: The global AI in clinical trials market is estimated to be worth $ 1.4 billion in 2023.

Question 7: What are the leading market segments in the global AI in clinical trials market ?

Answer: In terms of target therapeutic area, oncological disorders are likely to capture close to 35% of the current market.

Question 8: Which region captures the largest share in the AI in clinical trials market?

Answer: Presently, the AI in clinical trials market is dominated by North America, capturing around 35% of the overall market size, followed by Asia-Pacific.

Question 9: What is the likely growth rate (CAGR) for AI in clinical trial market?

Answer: The AI in clinical trials market is projected to grow at an annualized rate (CAGR) of 16%, during the forecast period 2023-2035.

Question 10: Which are the leading artificial intelligence companies in clinical trials market?

Answer: At present, around 130 companies are engaged in providing AI software / AI solutions and services for clinical trials. Examples of top players engaged in this market (which have also been captured in this report) include Acclinate, AiCure, Beacon Biosignals, Labcorp, Owkin and SiteRx.

CHAPTER OUTLINES

Chapter 1: is a preface providing an overview of the full report, AI in Clinical Trials Market (2nd Edition): AI Software and Service Providers, 2023-2035.

Chapter 2: is an executive summary of the insights captured during our research. It offers a high-level view on the current scenario of AI in clinical trials market and its likely evolution in the mid-term and long term.

Chapter 3: provides a general overview of AI in clinical trials, highlighting details on artificial intelligence and its subfields. It also presents information on the applications of AI in healthcare and clinical trials, and challenges associated with the adoption of AI. Additionally, it features a discussion on the future perspectives of the AI in clinical trials industry.

Chapter 4: includes detailed assessment of the current market landscape of the companies offering AI in clinical trials software and service, based on several relevant parameters, such as year of establishment, company size (in terms of number of employees), location of headquarters, key offering(s) (device, technology / platform and service), business model(s) (software as a service (SaaS), technology licensing, CRO / fee-for-service model and product provider), deployment option(s) (cloud-based and on-premise), type of AI technology (machine learning, deep learning, natural language processing and others), application area(s) (data analysis, medical imaging, patient recruitment, trial design, site selection, patient engagement, integrated patient care, patient trial monitoring, personalized treatment and report generation) and potential end-user(s) (pharmaceutical / biotechnology companies, hospitals, research institutes and CROs).

Chapter 5: features profiles of the prominent companies (shortlisted based on a proprietary criterion) developing AI software / AI solutions and offering services for clinical trials. Each profile features a brief overview of the company (including information on its year of establishment, number of employees, location of headquarters and key members of the leadership team), financial information (if available), details related to AI-based clinical trial offerings, recent developments and an informed future outlook.

Chapter 6: includes insightful clinical trial analysis of completed / ongoing clinical trials leveraging AI, based on various relevant parameters, such as trial registration year, number of patients enrolled, trial phase, trial status, type of sponsor, patient gender, patient age, emerging focus areas, target therapeutic area, patient allocation model used, trial masking adopted, type of intervention, trial purpose, most active players (in terms of number of clinical trials sponsored) and geography.

Chapter 7: provides a detailed analysis of the partnerships inked between stakeholders in the AI in clinical trials market, since 2018, covering product / technology utilization agreements, product / technology integration agreements, technology licensing agreements, research and development agreements, product development agreements, mergers and acquisitions, service agreements, service alliances and other relevant agreements.

Chapter 8: includes detailed analysis of the investments made, including seed financing, venture capital financing, capital raised from IPOs, grants, debt financing and other equity, and subsequent offerings, at various stages of development in start-ups, small and mid-sized companies that are focused on offering AI software and services for clinical trials.

Chapter 9: includes detailed analysis of the initiatives taken by big pharma players related to AI in clinical trials, based on various relevant parameters, such as year of initiative, type of initiative, application area of AI, target therapeutic area and leading big pharma players (in terms of number of AI in clinical trials focused initiatives)

Chapter 10: features a detailed case study of the use cases of AI in clinical trials, presenting information on collaborations inked between various AI software and service providers, and healthcare organizations. Each use case provides a brief overview of the companies involved, business needs and details on the objectives achieved and solutions provided.

Chapter 11: features 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 detailed analysis on ease of implementation and associated risk in integrating above-mentioned technologies, based on the trends highlighted in published literature and patents.

Chapter 12: includes detailed cost saving analysis, highlighting the overall cost saving potential of AI in clinical trials till 2035. We have highlighted the cost saving potential of AI in clinical trials at different trial phases (phase I, phase II and phase III) and trial procedures (patient recruitment, patient retention, staffing and administration, site monitoring, source data verification and other procedures).

Chapter 13: presents a comprehensive market forecast and opportunity analysis, highlighting the future potential of the AI in clinical trials market till 2035. We have segregated the current and upcoming opportunity based on 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, Middle East and North Africa).

Chapter 14: summarizes the overall report. In this chapter, we have provided a list of key takeaways from the report, and expressed our independent opinion related to the research and analysis described in the previous chapters.

Chapter 15: provides the transcripts of interviews conducted with key stakeholders in this industry.

Chapter 16: is an appendix, which contains tabulated data and numbers for all the figures included in this report.

Chapter 17: is an appendix, which contains a list of companies and organizations mentioned in this report.

Product Code: RA100441

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. Overview of Artificial Intelligence (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)
- 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: List of 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. Analysis by Location of Headquarters (Country-wise)
  - 7.3.8.3. Intercontinental and Intracontinental Agreements

8. FUNDING AND INVESTMENT ANALYSIS

8.1. Chapter Overview
8.2. Types of Funding
8.3. AI in Clinical Trials: List of 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 Type of Funding and Amount Invested
- 8.3.5. Most Active Players: Analysis by Amount Raised and Number of Funding

Instances

8.3.6. Leading Investors: Analysis by Number of Funding Instances
8.3.7. Analysis of Amount Invested by Geography
8.3.8. Analysis of Number of Funding Instances by Geography

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 Unnamed 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 Unnamed 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 / 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
11.9. Summary

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 in Phase I Clinical Trials, 2023-2035
- 12.3.2. Cost Saving Potential in Phase II Clinical Trials, 2023-2035
- 12.3.3. Cost Saving Potential in Phase III Clinical Trials, 2023-2035
- 12.3.4. Cost Saving Potential in Patient Recruitment, 2023-2035
- 12.3.5. Cost Saving Potential in Patient Retention, 2023-2035
- 12.3.6. Cost Saving Potential in Staffing and Administration, 2023-2035
- 12.3.7. Cost Saving Potential in Site Monitoring, 2023-2035
- 12.3.8. Cost Saving Potential in Source Data Verification, 2023-2035
- 12.3.9. Cost Saving Potential in Other Procedures, 2023-2035

13. MARKET SIZING AND OPPORTUNITY ANALYSIS

13.1. Chapter Overview
13.2. Forecast Methodology and Key Assumptions
13.3. Global AI in Clinical Trials Market, 2023-2035
- 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 Biotechnology and Pharmaceutical Companies, 2023-2035
  - 13.3.3.2. AI in Clinical Trials Market for Other End-users, 2023-2035
- 13.3.4. AI in Clinical Trials Market: Distribution by Key Geographical Regions, 2023 and 2035
  - 13.3.4.1. AI in Clinical Trials Market in North America, 2023-2035
  - 13.3.4.2. AI in Clinical Trials Market in Europe, 2023-2035
  - 13.3.4.3. AI in Clinical Trials Market in Asia-Pacific, 2023-2035
  - 13.3.4.4. AI in Clinical Trials Market in Middle East and North Africa, 2023-2035
  - 10.3.4.4. AI in Clinical Trials Market in Latin America, 2023-2035

14. CONCLUSION

15. EXECUTIVE INSIGHTS

15.1. Chapter Overview
15.2. Ancora.ai
- 15.2.1. Company Snapshot
- 15.2.2. Interview Transcript: Danielle Ralic, Co-Founder, Chief Executive Officer and Chief Technology Officer
15.3. Deep 6 AI
- 15.3.1. Company Snapshot
- 15.3.2. Interview Transcript: Wout Brusselaers, Founder and Chief Executive Officer
15.4. Intelligencia
- 15.4.1. Company Snapshot
- 15.4.2. Interview Transcript: Dimitrios Skaltsas, Co-Founder and Executive Director
15.5. nQ Medical
- 15.5.1. Company Snapshot
- 15.5.2. Interview Transcript: R. A. Bavasso, Founder and Chief Executive Officer
15.6. Science 37
- 15.6.1. Company Snapshot
- 15.6.2. Interview Transcript: Grazia Mohren (Head of Marketing), Michael Shipton (Chief Commercial Officer), Darcy Forman (Chief Delivery Officer), Troy Bryenton (Chief Technology Officer)

16. APPENDIX I: TABULATED DATA

17. APPENDIX II: LIST OF COMPANIES AND ORGANIZATIONS