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AI-based Clinical Trial Solution Providers Market, 2020-2030

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Report Description

The process of successfully developing a novel therapeutic intervention is both time and cost intensive. In fact, it is estimated that a prescription drug requires around 10 years and over USD 2.5 billion in capital investment, before reaching the market. In this process, clinical trials are a crucial requirement, enabling both innovators and regulators to assess the efficacy of a candidate drug and establish whether it is safe for use in humans. It is estimated that nearly 50% of the total time and capital expenditure during the drug development process, is on conducting clinical research. However, all trials are not successful; they are prone to delays (due to various reasons), and failure, both of which are known to impose enormous financial burdens on sponsors. According to a study conducted by the MIT Sloan School of Management, the rate of clinical success, defined as the proportion of trials that result in approval of the drug / therapy under investigation, was currently estimated to be 14%. The study further demonstrated that there is significant variance in the aforementioned rate across different types of therapies; for instance, for vaccines against infectious diseases, clinical success was estimated to be slightly above 30%, while for investigational anti-cancer drugs, it was 3%. Some of the key factors responsible for clinical stage product failure include inadequate study design, insufficient / incomplete patient recruitment, improper subject stratification during study conduct, and high rate of participant attrition.  

In attempts to address the abovementioned challenges, stakeholders in the pharmaceutical industry are actively exploring diverse strategies and solutions, one of which involves the collection and processing of real-world data. In fact, real-world data analysis is deemed to possess the potential to offer valuable insights from patient / healthcare provider testimonies, in order to drive future trial optimization efforts and facilitate better decision making during clinical research conduct. However, in order to generate actionable insights from real world medical data, there is a need for robust and advanced data mining technologies, such as big data analytics and artificial intelligence (AI) powered tools. Data integration, evolutionary modelling and pattern recognition using predictive AI models, can enable trial sponsors to aggregate, curate, and analyze large volumes of data, thereby, harnessing information captured during past trials to drive future therapy development initiatives. Experts also believe that the use of AI-powered solutions have the potential to address some of the commonly reported challenges, such as concerns related to clinical trial design, patient recruitment and retention, site selection, medical data interpretation and evaluation of treatment efficacy, which are encountered during trial conduct. Considering that the aforementioned issues are addressed, it is safe to presume that opting to use AI-enabled technologies in clinical trials may eventually  improve clinical R&D, and allow innovators to optimize on both time and capital investments made in such initiatives. Currently, this technology is still in its early stages, with limited adoption across the world. However, it is worth mentioning that close to USD 4 billion was invested into AI-focused healthcare startups, in 2019. We are led to believe that the opportunity for AI-based solution providers within the healthcare industry is likely to grow at a significant in the foreseen future.

Scope of the Report

The ‘AI-based Clinical Trial Solution Providers Market, 2020-2030’ report features an extensive study of the companies offering AI-based platforms for clinical trial applications, in addition to the current market landscape and their future potential. Amongst other elements, the report features:

  • A detailed assessment of the competitive landscape of AI-based solution providers based on parameters, such as area of application, year of establishment, company size and location of headquarters. 
  • Brief profiles of prominent players engaged in offering AI-based solutions for clinical trial applications. Each profile features a brief overview of the company and its proprietary technology platform(s), recent developments and an informed future outlook.
  • An analysis of the partnerships and collaborations inked in the domain, in the period between 2014 and 2020 (till May), based on several parameters, such as year of partnership, type of partnership, application mentioned in agreement, target therapeutic area mentioned in the agreement, year of partnership and type of partner, most active players and geographical analysis. 
  • An analysis of the funding and investments made in the domain, in the period between 2014 and 2020 (till May), including seed financing, venture capital financing, debt financing, grants, capital raised from IPOs and subsequent offerings, at various stages of development in companies that are engaged in this field, based on several parameters, such as number of funding instances, amount invested, type of funding, leading players and investors, and geographical analysis
  • A detailed analysis of completed, ongoing and planned clinical trials involving the use of AI, based on multiple parameters, such as trial registration year, trial phase, trial status, type of sponsor / collaborator, target therapeutic area, trial design, top sponsor, geographical location of trial and enrolled patient population.
  • An analysis of various AI related initiatives of top 10 big pharma players (based on revenue), based on multiple parameters, such as year of initiative, type of initiative, focus of initiative, area of application and target therapeutic area. In addition, leading players and leading partners have been highlighted based on the number of initiatives.
  • A case study on recent use cases, wherein various pharmaceutical / healthcare companies have employed AI-based solutions for different processes of clinical trials, highlighting different business needs of such players and key takeaways of the solution provided by AI- based solution providers.
  • An in-depth analysis of the cost saving potential across various processes of clinical drug development that can be brought about by the implementation of bespoke AI-based solutions.

One of the key objectives of the report was to understand the primary growth drivers and estimate the future opportunity within this market. Based on several parameters, such as annual number of clinical trials, average capital investment per trial across different phases and therapeutic areas, cost saving potential of AI and expected annual growth rate across various geographies, we have provided an informed estimate of the likely evolution of the market, in the mid to long term, for the period 2020-2030. The chapter features the likely distribution of the opportunity across different [A] trial phase (phase I, phase II and phase III), [B] therapeutic areas (cardiovascular disorders, CNS disorders, infectious disorders, metabolic disorders, oncological disorders and other disorders), [C] end-users (pharmaceutical companies, and academia and other users) and [D] key geographical regions (North America, Europe, Asia-Pacific and rest of the world).

In order to account for future uncertainties and to add robustness to our model, we have provided three forecast scenarios, portraying the conservative, base and optimistic tracks of the market’s evolution. The opinions and insights presented in this study were influenced by discussions conducted with multiple stakeholders in this domain. 

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

Key Questions Answered

  • Who are the leading AI-based clinical trial solution providers?
  • How has the clinical activity involving the use of AI evolved in recent years?
  • What is the focus area of big pharma players in the AI domain?
  • Which companies have raised significant amount of money in the domain?
  • What is the total cost saving potential of AI-based clinical solutions across different steps of a clinical trial?
  • What kind of partnership models are presently being used by stakeholders in the industry?
  • What factors are likely to influence the evolution of this upcoming market?
  • How is the current and future opportunity likely to be distributed across key market segments?

Contents

Chapter Outlines

Chapter 2 is an executive summary of the insights captured in our research. It offers a high-level view on the likely evolution of the artificial intelligence in clinical trials market in the mid to long term.

Chapter 3 provides a brief overview of artificial intelligence, machine learning and natural language processing. It also highlights the classification of AI, as well as the applications of AI in the healthcare domain. Further, the chapter includes various challenges associated with the adoption of AI in healthcare and its future perspectives.

Chapter 4 provides an overview of the competitive landscape of AI-based solution providers based on parameters, such as area of application, year of establishment, company size and location of headquarters.

Chapter 5 includes brief profiles of prominent companies engaged in offering AI-based platforms for clinical trials, featuring a brief overview of the company and its proprietary platform(s), recent developments and an informed future outlook.

Chapter 6 features an in-depth analysis and discussion on the various partnerships inked between the players in this domain, in the time period between 2014 and 2020, based on several parameters, such as year of partnership, type of partnership, application mentioned in agreement, target therapeutic area mentioned in the agreement, year of partnership and type of partner, most active players and geographical analysis. 

Chapter 7 presents details on various investments received by companies that are engaged in offering clinical trial services using artificial intelligence. It also includes an analysis of the funding instances that have taken place in the market, up to 2020 (till May), based on several parameters, such as number of funding instances, amount invested, type of funding, leading players and investors, and geographical analysis

Chapter 8 provides an analysis of completed, ongoing and planned clinical studies using artificial intelligence, featuring details on registration year, trial phase, trial status, type of sponsor, target therapeutic area, trial design, top sponsor, geographical location of trial and enrolled patient population.

Chapter 9 provides various AI related initiatives of top 10 big pharma players (based on revenue), based on multiple parameters, such as year of initiative, type of initiative, focus of initiative, area of application and target therapeutic area. In addition, leading players and leading partners have been highlighted based on the number of initiatives.

Chapter 10 presents a case study on recent use cases, wherein various pharmaceutical / healthcare companies have employed AI-based platforms for different processes of clinical trials, highlighting different business needs of such players and key takeaways of the solution provided by AI- based solution providers.

Chapter 11 features an insightful analysis, highlighting the cost saving potential offered by AI-based solutions across various processes of clinical trials, such as patient recruitment, patient adherence, source data verification and site monitoring, across phase I, phase II and phase III clinical trials.

Chapter 12 presents a detailed market forecast, highlighting the future potential of the AI-based clinical trial solutions market, for the time period 2020-2030. The chapter features the likely distribution of the opportunity across different  [A] trial phase (phase I, phase II and phase III), [B] therapeutic areas (cardiovascular disorders, CNS disorders, infectious disorders, metabolic disorders, oncological disorders and other disorders), [C] end-users (pharmaceutical companies, and academia and other users) and [D] key geographical regions (North America, Europe, Asia-Pacific and rest of the world).

Chapter 13 summarizes the entire report. It presents a list of key takeaways and offers our independent opinion on the current market scenario. Further, it summarizes the various evolutionary trends that are likely to influence the future of this market.

Chapter 14 is a collection of executive insight(s) of the discussions that were held with various key stakeholders in this market. 

Chapter 15 is an appendix, which provides tabulated data and numbers for all the figures included in the report.

Chapter 16 is an appendix, which contains the list of companies and organizations mentioned in the report.

Table Of Contents

1. PREFACE
1.1. Scope of the Report
1.2. Research Methodology
1.3. Chapter Outlines

2. EXECUTIVE SUMMARY

3. INTRODUCTION
3.1. Chapter Overview
3.2. Overview of Artificial Intelligence (AI)
3.2.1. Machine Learning
3.2.2. Natural Language Processing

3.2.3. Classification of AI
3.2.3.1. Reactive AI
3.2.3.2. Limited Memory AI
3.2.3.3. Theory of Mind AI
3.2.3.4. Self-Aware AI
3.2.3.5. Artificial Narrow Intelligence
3.2.3.6. Artificial General Intelligence
3.2.3.7. Artificial Super Intelligence

3.2.4. Application of AI in Healthcare
3.2.4.1. Drug Discovery
3.2.4.2. Drug Manufacturing
3.2.4.3. Drug Marketing
3.2.4.4. Diagnosis and Treatment
3.2.4.5. Clinical Trials
3.2.4.5.1. Patient Recruitment
3.2.4.5.2. Patient Monitoring
3.2.4.5.3. Patient Adherence

3.3. Key Challenges Associated with the Adoption of AI 
3.4. Future Perspectives

4. MARKET LANDSCAPE
4.1. Chapter Overview
4.2. AI-based Clinical Trial Solution Providers: Overall Market Landscape
4.2.1. Analysis by Area of Application
4.2.2. Analysis by Year of Establishment
4.2.3. Analysis by Company Size
4.2.4. Analysis by Location of Headquarters

5. COMPANY PROFILES
5.1. Chapter Overview
5.2. AiCure
5.2.1. Company and Technology Overview
5.2.2. Recent Developments and Future Outlook

5.3. Antidote
5.3.1. Company and Technology Overview
5.3.2. Recent Developments and Future Outlook

5.4. Deep Lens
5.4.1. Company and Technology Overview
5.4.2. Recent Developments and Future Outlook

5.5. Deep 6 AI
5.5.1. Company and Technology Overview
5.5.2. Recent Developments and Future Outlook

5.6. Innoplexus
5.6.1. Company and Technology Overview
5.6.2. Recent Developments and Future Outlook

5.7. Median Technologies
5.7.1. Company and Technology Overview
5.7.2. Recent Developments and Future Outlook

5.8. Mendel.ai
5.8.1. Company and Technology Overview
5.8.2. Recent Developments and Future Outlook

5.9. Phesi
5.9.1. Company and Technology Overview
5.9.2. Recent Developments and Future Outlook

5.10. Saama Technologies
5.10.1. Company and Technology Overview
5.10.2. Recent Developments and Future Outlook

5.11. Trials.ai
5.11.1. Company and Technology Overview

6. PARTNERSHIPS AND COLLABORATIONS
6.1. Chapter Overview
6.2. Partnership Models
6.3. AI-based Clinical Trial Solution Providers: Partnerships and Collaborations
6.3.1. Analysis by Year of Partnership
6.3.2. Analysis by Type of Partnership
6.3.3. Analysis by Application Mentioned in the Agreement
6.3.4. Analysis by Target Therapeutic Area Mentioned in the Agreement
6.3.5. Analysis by Year of Partnership and Type of Partner
6.3.6. Most Active Players: Analysis by Number of Partnerships
6.3.7. Geographical Analysis
6.3.8. Intercontinental and Intracontinental Agreements

7. FUNDING AND INVESTMENT ANALYSIS
7.1. Chapter Overview
7.2. Types of Funding Instances
7.3. AI-based Clinical Trial Solution Providers: Funding and Investments 
7.3.1. Analysis by Number of Funding Instances
7.3.2. Analysis by Amount Invested
7.3.3. Analysis by Type of Funding
7.3.4. Leading Players: Analysis by Amount Invested and Number of Funding Instances
7.3.5. Most Active Investors: Analysis by Number of Funding Instances
7.3.6. Geographical Analysis by Amount Invested
7.4. Concluding Remarks

8. CLINICAL TRIAL ANALYSIS
8.1. Chapter Overview
8.2. Scope and Methodology
8.3. AI-based Clinical Trial Solution Providers: Analysis of Clinical Research Activity
8.3.1. Analysis by Trial Registration Year
8.3.2. Analysis by Trial Phase
8.3.3. Analysis by Trial Status
8.3.4. Analysis by Type of Sponsor / Collaborator 
8.3.5. Analysis by Target Therapeutic Area
8.3.6. Analysis by Trial Design
8.3.7. Geographical Analysis by Number of Clinical Trials
8.3.8. Geographical Analysis by Enrolled Patient Population 
8.3.9. Geographical Analysis by Number of Clinical Trials and Trial Status
8.3.10. Geographical Analysis by Enrolled Patient Population and Trial Status

9. BIG PHARMA INITIATIVES
9.1. Chapter Overview
9.1.1. Analysis by Year of Initiative
9.1.2. Analysis by Type of Initiative
9.1.3. Analysis by Focus of Initiative
9.1.4. Analysis by Area of Application 
9.1.5. Analysis by Target Therapeutic Area

10. CASE STUDY: USE CASES
10.1. Chapter Overview
10.2. 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. 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. 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. Unnamed Pharmaceutical Company and Antidote
10.5.1. Antidote
10.5.2. Business Needs
10.5.3. Objectives Achieved and Solutions Provided

10.6. Unnamed Pharmaceutical Company and Cognizant
10.6.1. Cognizant
10.6.2. Business Needs 
10.6.3. Objectives Achieved and Solutions Offered

10.7. 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

11. COST SAVING ANALYSIS
11.1. Chapter Overview
11.2. Key Assumptions and Methodology
11.3. Overall Cost Saving Potential of AI-based Clinical Trial Solutions, 2020-2030
11.3.1. Cost Saving Potential in Phase I Clinical Trials, 2020-2030
11.3.2. Cost Saving Potential in Phase II clinical Trials, 2020-2030
11.3.3. Cost Saving Potential in Phase III clinical Trials, 2020-2030
11.3.4. Cost Saving Potential in Patient Recruitment, 2020-2030
11.3.5. Cost Saving Potential in Patient Retention, 2020-2030
11.3.6. Cost Saving Potential in Site Monitoring, 2020-2030
11.3.7. Cost Saving Potential in Source Data Verification, 2020-2030

12. MARKET SIZING AND OPPORTUNITY ANALYSIS
12.1. Chapter Overview
12.2. Key Assumptions and Forecast Methodology
12.3. Overall AI-based Clinical Trial Solutions Market Opportunity, 2020-2030
12.4. AI-based Clinical Trial Solutions Market Opportunity: Distribution by Trial Phase, 2020 and 2030

12.5. AI-based Clinical Trial Solutions Market Opportunity: Distribution by Target Therapeutic Area, 2020 and 2030
12.6. AI-based Clinical Trial Solutions Market Opportunity: Distribution by End-User, 2020 and 2030
12.7. AI-based Clinical Trial Solutions Market Opportunity: Distribution by Key Geographical Regions, 2020 and 2030
12.7.1. AI-based Clinical Trial Solutions Market Opportunity in North America, 2020-2030
12.7.1.1. AI-based Clinical Trial Solutions Market Opportunity in North America, Distribution by Target Therapeutic Area, 2020-2030
12.7.1.1.1. AI-based Clinical Trial Solutions Market Opportunity in North America: Share of Cardiovascular Disorders, 2020-2030
12.7.1.1.2. AI-based Clinical Trial Solutions Market Opportunity in North America: Share of CNS Disorders, 2020-2030
12.7.1.1.3. AI-based Clinical Trial Solutions Market Opportunity in North America: Share of Infectious Disorders, 2020-2030
12.7.1.1.4. AI-based Clinical Trial Solutions Market Opportunity in North America: Share of Metabolic Disorders, 2020-2030
12.7.1.1.5. AI-based Clinical Trial Solutions Market Opportunity in North America: Share of Oncological Disorders, 2020-2030
12.7.1.1.6. AI-based Clinical Trial Solutions Market Opportunity in North America: Share of Other Disorders, 2020-2030
12.7.1.2. AI-based Clinical Trial Solutions Market Opportunity, Distribution by End-User, 2020-2030
12.7.1.2.1. AI-based Clinical Trial Solutions Market Opportunity in North America: Share of Pharmaceutical Companies, 2020-2030
12.7.1.2.2. AI-based Clinical Trial Solutions Market Opportunity in North America: Share of Academia and Other Users, 2020-2030
12.7.2. AI-based Clinical Trial Solutions Market Opportunity in Europe, 2020-2030
12.7.2.1. AI-based Clinical Trial Solutions Market Opportunity in Europe, Distribution by Target Therapeutic Area, 2020-2030
12.7.2.1.1. AI-based Clinical Trial Solutions Market Opportunity in Europe: Share of Cardiovascular Disorders, 2020-2030
12.7.2.1.2. AI-based Clinical Trial Solutions Market Opportunity in Europe: Share of CNS Disorders, 2020-2030
12.7.2.1.3. AI-based Clinical Trial Solutions Market Opportunity in Europe: Share of Infectious Disorders, 2020-2030
12.7.2.1.4. AI-based Clinical Trial Solutions Market Opportunity in Europe: Share of Metabolic Disorders, 2020-2030
12.7.2.1.5. AI-based Clinical Trial Solutions Market Opportunity in Europe: Share of Oncological Disorders, 2020-2030
12.7.2.1.6. AI-based Clinical Trial Solutions Market Opportunity in Europe: Share of Other Disorders, 2020-2030
12.7.2.2. AI-based Clinical Trial Solutions Market Opportunity, Distribution by End-User, 2020-2030
12.7.2.2.1. AI-based Clinical Trial Solutions Market Opportunity in Europe: Share of Pharmaceutical Companies, 2020-2030
12.7.2.2.2. AI-based Clinical Trial Solutions Market Opportunity in Europe: Share of Academia and Other Users, 2020-2030
12.7.3. AI-based Clinical Trial Solutions Market Opportunity in Asia-Pacific, 2020-2030
12.7.3.1. AI-based Clinical Trial Solutions Market Opportunity in Asia-Pacific, Distribution by Target Therapeutic Area, 2020-2030
12.7.3.1.1. AI-based Clinical Trial Solutions Market Opportunity in Asia-Pacific: Share of Cardiovascular Disorders, 2020-2030
12.7.3.1.2. AI-based Clinical Trial Solutions Market Opportunity in Asia-Pacific: Share of CNS Disorders, 2020-2030
12.7.3.1.3. AI-based Clinical Trial Solutions Market Opportunity in Asia-Pacific: Share of Infectious Disorders, 2020-2030
12.7.3.1.4. AI-based Clinical Trial Solutions Market Opportunity in Asia-Pacific: Share of Metabolic Disorders, 2020-2030
12.7.3.1.5. AI-based Clinical Trial Solutions Market Opportunity in Asia-Pacific: Share of Oncological Disorders, 2020-2030
12.7.3.1.6. AI-based Clinical Trial Solutions Market Opportunity in Asia-Pacific: Share of Other Disorders, 2020-2030
12.7.3.2. AI-based Clinical Trial Solutions Market Opportunity, Distribution by End-User, 2020-2030
12.7.3.2.1. AI-based Clinical Trial Solutions Market Opportunity in Asia-Pacific: Share of Pharmaceutical Companies, 2020-2030
12.7.3.2.2. AI-based Clinical Trial Solutions Market Opportunity in Asia-Pacific: Share of Academia and Other Users, 2020-2030
12.7.4. AI-based Clinical Trial Solutions Market Opportunity in Rest of the World, 2020-2030
12.7.4.1. AI-based Clinical Trial Solutions Market Opportunity in Rest of the World, Distribution by Target Therapeutic Area
12.7.4.1.1. AI-based Clinical Trial Solutions Market Opportunity in Rest of the World: Share of Cardiovascular Disorders, 2020-2030
12.7.4.1.2. AI-based Clinical Trial Solutions Market Opportunity in Rest of the World: Share of CNS Disorders, 2020-2030
12.7.4.1.3. AI-based Clinical Trial Solutions Market Opportunity in Rest of the World: Share of Infectious Disorders, 2020-2030
12.7.4.1.4. AI-based Clinical Trial Solutions Market Opportunity in Rest of the World: Share of Metabolic Disorders, 2020-2030
12.7.4.1.5. AI-based Clinical Trial Solutions Market Opportunity in Rest of the World: Share of Oncological Disorders, 2020-2030
12.7.4.1.6. AI-based Clinical Trial Solutions Market Opportunity in Rest of the World: Share of Other Disorders, 2020-2030
12.7.4.2. AI-based Clinical Trial Solutions Market Opportunity, Distribution by End-User, 2020-2030

12.7.4.2.1. AI-based Clinical Trial Solutions Market Opportunity in Rest of the World: Share of Pharmaceutical Companies, 2020-2030

12.7.4.2.2. AI-based Clinical Trial Solutions Market Opportunity in Rest of the World: Share of Academia and Other Users, 2020-2030

13. CONCLUSION
13.1. Chapter Overview
13.2. Key Takeaways

14. EXECUTIVE INSIGHTS
14.1. Chapter Overview

14.2. Intelligencia
14.2.1. Company Snapshot
14.2.2. Interview Transcript: Dimitrios Skaltsas, Co-Founder and Executive Director

15. APPENDIX I: TABULATED DATA

16. APPENDIX II: LIST OF COMPANIES AND ORGANIZATIONS

List Of Figures

Figure 3.1 Evolution of AI
Figure 3.2 Machine Learning Algorithm: Workflow
Figure 3.3 Natural Language Processing
Figure 3.4 Classification of AI
Figure 3.5 Applications of AI in Healthcare
Figure 4.1 AI-based Clinical Trial Solution Providers: Distribution by Area of Application
Figure 4.2 AI-based Clinical Trial Solution Providers: Distribution by Year of Establishment
Figure 4.3 AI-based Clinical Trial Solution Providers: Distribution by Company Size
Figure 4.4 AI-based Clinical Trial Solution Providers: Distribution by Location of Headquarters
Figure 6.1 Partnerships and Collaborations: Cumulative Year-wise Trend, 2010-2020
Figure 6.2 Partnerships and Collaborations: Distribution by Type of Partnership
Figure 6.3 Partnerships and Collaborations: Distribution by Year and Type of Partnership, 2010-2020
Figure 6.4 Partnerships and Collaborations: Distribution by Application Mentioned in the Agreement
Figure 6.5 Partnerships and Collaborations: Distribution by Target Therapeutic Area Mentioned in the Agreement
Figure 6.6 Partnerships and Collaborations: Distribution by Year of Partnership and Type of Partner
Figure 6.7 Most Active Players: Distribution by Number of Partnerships
Figure 6.8 Partnership and Collaborations: Geographical Distribution
Figure 6.9 Partnerships and Collaborations: Intercontinental and Intracontinental Distribution
Figure 7.1 Funding and Investment Analysis: Cumulative Year-wise Trend, 2010-2020
Figure 7.2 Funding and Investment Analysis: Cumulative Amount Invested, 2010-2020 (USD Million)
Figure 7.3 Funding and Investment Analysis: Distribution by Number of Funding Instances and Type of Funding, 2010-2020
Figure 7.4 Funding and Investment Analysis: Distribution by Total Amount Invested and Type of Funding, 2010-2020 (USD Million)
Figure 7.5 Funding and Investment Analysis: Distribution by Year of Establishment and Type of Funding, 2010-2020
Figure 7.6 Leading Players: Distribution by Amount Raised and Number of Funding Instances, 2010-2020 (USD Million)
Figure 7.7 Most Active Investors: Distribution by Number of Funding Instances
Figure 7.8 Funding and Investment Analysis: Geographical Distribution by Amount Invested
Figure 7.9 Funding and Investment Analysis: Geographical Distribution of Funding Instances
Figure 7.10 Funding and Investment Summary, 2010-2020 (USD Million)
Figure 8.1 Clinical Trial Analysis: Distribution by Trial Registration Year
Figure 8.2 Clinical Trial Analysis: Distribution of Number of Patients Enrolled by Trial Registration Year
Figure 8.3 Clinical Trial Analysis: Distribution by Trial Phase
Figure 8.4 Clinical Trial Analysis: Distribution by Trial Status
Figure 8.5 Clinical Trial Analysis: Distribution by Type of Sponsor / Collaborator
Figure 8.6 Clinical Trial Analysis: Distribution by Patient Gender
Figure 8.7 Clinical Trial Analysis: Distribution by Target Therapeutic Area
Figure 8.8 Clinical Trial Analysis: Distribution by Trial Design
Figure 8.9 Clinical Trial Analysis: Distribution by Trial Allocation Model Used
Figure 8.10 Clinical Trial Analysis: Distribution by Trial Masking Adopted
Figure 8.11 Clinical Trial Analysis: Distribution by Intervention
Figure 8.12 Clinical Trial Analysis: Distribution by Trial Purpose
Figure 8.13 Clinical Trial Analysis: Distribution by Time Perspective
Figure 8.14 Clinical Trial Analysis: Geographical Distribution by Number of Clinical Trials
Figure 8.15 Clinical Trial Analysis: Geographical Distribution by Enrolled Patient Population
Figure 8.16 Clinical Trial Analysis: Geographical Distribution by Distribution by Number of Trials and Trial Status
Figure 8.17 Clinical Trial Analysis: Geographical Distribution by Distribution by Enrolled Patient Population and Trial Status
Figure 9.1 Big Pharma Initiatives: Cumulation Distribution by Year of Initiative
Figure 9.2 Big Pharma Initiatives: Distribution by Type of Initiative
Figure 9.3 Big Pharma Initiatives: Heat Map Analysis of Type of Initiative
Figure 9.4 Big Pharma Initiatives: Distribution by Focus of Initiative
Figure 9.5 Big Pharma Initiatives: Heat Map Analysis of Focus of Initiative
Figure 9.6 Big Pharma Initiatives: Distribution by Area of Application
Figure 9.7 Big Pharma Initiatives: Heat Map Analysis of Area of Application
Figure 9.8 Big Pharma Initiatives: Distribution by Target Therapeutic Area
Figure 9.9 Big Pharma Initiatives: Heat Map Analysis of Target Therapeutic Area
Figure 11.1 Overall Cost Saving Potential of AI-based Clinical Trial Solutions, 2020 and 2030 (USD Million)
Figure 11.2 Overall Cost Saving Potential of AI-based Clinical Trial Solutions, 2020-2030 (USD Million)
Figure 11.3 Cost Saving Potential in Phase I Clinical Trials, 2020-2030 (USD Million)
Figure 11.4 Cost Saving Potential in Phase II Clinical Trials, 2020-2030 (USD Million)
Figure 11.5 Cost Saving Potential in Phase II Clinical Trials, 2020-2030 (USD Million)
Figure 11.6 Cost Saving Potential in Patient Recruitment, 2020-2030 (USD Million)
Figure 11.7 Cost Saving Potential in Patient Retention, 2020-2030 (USD Million)
Figure 11.8 Cost Saving Potential in Site Monitoring, 2020-2030 (USD Million)
Figure 11.9 Cost Saving Potential in Source Data Verification, 2020-2030 (USD Million)
Figure 12.1 Overall AI-based Clinical Trial Solutions Market Opportunity, 2020-2030 (USD Million)
Figure 12.2 AI-based Clinical Trial Solutions Market Opportunity: Distribution by Trial Phase, 2020 and 2030 (USD Million)
Figure 12.3 AI-based Clinical Trial Solutions Market Opportunity: Distribution by Target Therapeutic Area, 2020 and 2030 (USD Million)
Figure 12.4 AI-based Clinical Trial Solutions Market Opportunity: Distribution by End-Users, 2020 and 2030 (USD Million)
Figure 12.5 AI-based Clinical Trial Solutions Market Opportunity: Distribution by Key Geographical Regions 2020 and 2030 (USD Million)
Figure 12.6 AI-based Clinical Trial Solutions Market Opportunity in North America, 2020-2030 (USD Million)
Figure 12.7 AI-based Clinical Trial Solutions Market Opportunity in North America: Share of Cardiovascular Disorders, 2020-2030 (USD Million)
Figure 12.8 AI-based Clinical Trial Solutions Market Opportunity in North America: Share of CNS Disorders, 2020-2030 (USD Million)
Figure 12.9 AI-based Clinical Trial Solutions Market Opportunity in North America: Share of Infectious Disorders, 2020-2030 (USD Million)
Figure 12.10 AI-based Clinical Trial Solutions Market Opportunity in North America: Share of Metabolic Disorders, 2020-2030 (USD Million)
Figure 12.11 AI-based Clinical Trial Solutions Market Opportunity in North America: Share of Oncological Disorders, 2020-2030 (USD Million)
Figure 12.12 AI-based Clinical Trial Solutions Market Opportunity in North America: Share of Other Disorders, 2020-2030 (USD Million)
Figure 12.13 AI-based Clinical Trial Solutions Market Opportunity in North America: Share of Pharmaceutical Companies, 2020-2030 (USD Million)
Figure 12.14 AI-based Clinical Trial Solutions Market Opportunity in North America: Share of Academia and Other Users, 2020-2030 (USD Million)
Figure 12.15 AI-based Clinical Trial Solutions Market Opportunity in Europe, 2020-2030 (USD Million)
Figure 12.16 AI-based Clinical Trial Solutions Market Opportunity in Europe: Share of Cardiovascular Disorders, 2020-2030 (USD Million)
Figure 12.17 AI-based Clinical Trial Solutions Market Opportunity in Europe: Share of CNS Disorders, 2020-2030 (USD Million)
Figure 12.18 AI-based Clinical Trial Solutions Market Opportunity in Europe: Share of Infectious Disorders, 2020-2030 (USD Million)
Figure 12.19 AI-based Clinical Trial Solutions Market Opportunity in Europe: Share of Metabolic Disorders, 2020-2030 (USD Million)
Figure 12.20 AI-based Clinical Trial Solutions Market Opportunity in Europe: Share of Oncological Disorders, 2020-2030 (USD Million)
Figure 12.21 AI-based Clinical Trial Solutions Market Opportunity in Europe: Share of Other Disorders, 2020-2030 (USD Million)
Figure 12.22 AI-based Clinical Trial Solutions Market Opportunity in Europe: Share of Pharmaceutical Companies, 2020-2030 (USD Million)
Figure 12.23 AI-based Clinical Trial Solutions Market Opportunity in Europe: Share of Academia and Other Users, 2020-2030 (USD Million)
Figure 12.24 AI-based Clinical Trial Solutions Market Opportunity in Asia-Pacific, 2020-2030 (USD Million)
Figure 12.25 AI-based Clinical Trial Solutions Market Opportunity in Asia-Pacific: Share of Cardiovascular Disorders, 2020-2030 (USD Million)
Figure 12.26 AI-based Clinical Trial Solutions Market Opportunity in Asia-Pacific: Share of CNS Disorders, 2020-2030 (USD Million)
Figure 12.27 AI-based Clinical Trial Solutions Market Opportunity in Asia-Pacific: Share of Infectious Disorders, 2020-2030 (USD Million)
Figure 12.28 AI-based Clinical Trial Solutions Market Opportunity in Asia-Pacific: Share of Metabolic Disorders, 2020-2030 (USD Million)
Figure 12.29 AI-based Clinical Trial Solutions Market Opportunity in Asia-Pacific: Share of Oncological Disorders, 2020-2030 (USD Million)
Figure 12.30 AI-based Clinical Trial Solutions Market Opportunity in Asia-Pacific: Share of Other Disorders, 2020-2030 (USD Million)
Figure 12.31 AI-based Clinical Trial Solutions Market Opportunity in Asia-Pacific: Share of Pharmaceutical Companies, 2020-2030 (USD Million)
Figure 12.32 AI-based Clinical Trial Solutions Market Opportunity in Asia-Pacific: Share of Academia and Other Users, 2020-2030 (USD Million)
Figure 12.33 AI-based Clinical Trial Solutions Market Opportunity in Rest of the World, 2020-2030 (USD Million)
Figure 12.34 AI-based Clinical Trial Solutions Market Opportunity in Rest of the World: Share of Cardiovascular Disorders, 2020-2030 (USD Million)
Figure 12.35 AI-based Clinical Trial Solutions Market Opportunity in Rest of the World: Share of CNS Disorders, 2020-2030 (USD Million)
Figure 12.36 AI-based Clinical Trial Solutions Market Opportunity in Rest of the World: Share of Infectious Disorders, 2020-2030 (USD Million)
Figure 12.37 AI-based Clinical Trial Solutions Market Opportunity in Rest of the World: Share of Metabolic Disorders, 2020-2030 (USD Million)
Figure 12.38 AI-based Clinical Trial Solutions Market Opportunity in Rest of the World: Share of Oncological Disorders, 2020-2030 (USD Million)
Figure 12.39 AI-based Clinical Trial Solutions Market Opportunity in Rest of the World: Share of Other Disorders, 2020-2030 (USD Million)
Figure 12.40 AI-based Clinical Trial Solutions Market Opportunity in Rest of the World: Share of Pharmaceutical Companies, 2020-2030 (USD Million)
Figure 12.41 AI-based Clinical Trial Solutions Market Opportunity in Rest of the World: Share of Academia and Other Users, 2020-2030 (USD Million)

List Of Tables

Table 4.1 AI-based Clinical Trial Solution Providers: Information on Name of Solution and Area of Application
Table 4.2 AI-based Clinical Trial Solution Providers: List of Companies
Table 5.1 AI-based Clinical Trial Solution Providers: List of Companies Profiled
Table 5.2 AiCure: Company Snapshot
Table 5.3 AiCure: Recent Developments and Future Outlook
Table 5.4 Antidote: Company Snapshot
Table 5.5 Antidote: Recent Developments and Future Outlook
Table 5.6 Deep Lens: Company Snapshot
Table 5.7 Deep Lens: Recent Developments and Future Outlook
Table 5.8 Deep 6 AI: Company Snapshot
Table 5.9 Deep 6 AI: Recent Developments and Future Outlook
Table 5.10 Innoplexus: Company Snapshot
Table 5.11 Innoplexus: Recent Developments and Future Outlook
Table 5.12 Median Technologies: Company Snapshot
Table 5.13 Median Technologies: Recent Developments and Future Outlook
Table 5.14 Mendel.ai: Company Snapshot
Table 5.15 Mendel.ai: Recent Developments and Future Outlook
Table 5.16 Phesi: Company Snapshot
Table 5.17 Phesi: Recent Developments and Future Outlook
Table 5.18 Saama Technologies: Company Snapshot
Table 5.19 Saama Technologies: Recent Developments and Future Outlook
Table 5.20 Trials.ai: Company Snapshot
Table 6.1 AI-based Clinical Trial Solution Providers: List of Partnerships and Collaborations, 2010-2020
Table 7.1 AI-based Clinical Trial Solution Providers: Funding and Investments, 2010-2020
Table 7.2 Funding and Investment Analysis: Summary of Investments
Table 14.1 AI-based Clinical Trial Solution Providers: Distribution by Area of Application
Table 14.2 AI-based Clinical Trial Solution Providers: Distribution by Year of Establishment
Table 14.3 AI-based Clinical Trial Solution Providers: Distribution by Company Size
Table 14.4 AI-based Clinical Trial Solution Providers: Distribution by Location of Headquarters
Table 14.5 Partnerships and Collaborations: Cumulative Year-wise Trend, 2010-2020
Table 14.6 Partnerships and Collaborations: Distribution by Type of Partnership
Table 14.7 Partnerships and Collaborations: Distribution by Year and Type of Partnership, 2010-2020
Table 14.8 Partnerships and Collaborations: Distribution by Application Mentioned in the Agreement
Table 14.9 Partnerships and Collaborations: Distribution by Target Therapeutic Area Mentioned in the Agreement
Table 14.10 Partnerships and Collaborations: Distribution by Year of Partnership and Type of Partner
Table 14.11 Most Active Players: Distribution by Number of Partnerships
Table 14.12 Partnership and Collaborations: Regional Distribution
Table 14.13 Funding and Investment Analysis: Cumulative Year-wise Trend, 2010-2020
Table 14.14 Funding and Investment Analysis: Cumulative Amount Invested, 2010-2020 (USD Million)
Table 14.15 Funding and Investment Analysis: Distribution by Number of Funding Instances and Type of Funding, 2010-2020
Table 14.16 Funding and Investment Analysis: Distribution by Total Amount Invested and Type of Funding, 2010-2020 (USD Million)
Table 14.17 Leading Players: Distribution by Amount Raised and Number of Funding Instances, 2010-2020 (USD Million)
Table 14.18 Most Active Investors: Distribution by Number of Funding Instances
Table 14.19 Funding and Investment Analysis: Geographical Distribution by Amount Invested
Table 14.20 Funding and Investment Analysis: Regional Distribution of Funding Instances
Table 14.21 Clinical Trial Analysis: Distribution by Trial Registration Year
Table 14.22 Clinical Trial Analysis: Distribution of Number of Patients Enrolled by Trial Registration Year
Table 14.23 Clinical Trial Analysis: Distribution by Trial Phase
Table 14.24 Clinical Trial Analysis: Distribution by Trial Status
Table 14.25 Clinical Trial Analysis: Distribution by Type of Sponsor / Collaborator
Table 14.26 Clinical Trial Analysis: Distribution by Patient Gender
Table 14.27 Clinical Trial Analysis: Distribution by Target Therapeutic Area
Table 14.28 Clinical Trial Analysis: Distribution by Trial Design
Table 14.29 Clinical Trial Analysis: Distribution by Trial Allocation Model Used
Table 14.30 Clinical Trial Analysis: Distribution by Trial Masking Adopted
Table 14.31 Clinical Trial Analysis: Distribution by Intervention
Table 14.32 Clinical Trial Analysis: Distribution by Trial Purpose
Table 14.33 Clinical Trial Analysis: Distribution by Time Perspective
Table 14.34 Big Pharma Initiatives: Cumulation Distribution by Year of Initiative
Table 14.35 Big Pharma Initiatives: Distribution by Type of Initiative
Table 14.36 Big Pharma Initiatives: Distribution by Focus of Initiative
Table 14.37 Big Pharma Initiatives: Distribution by Area of Application
Table 14.38 Big Pharma Initiatives: Distribution by Target Therapeutic Area
Table 14.39 Overall Cost Saving Potential of AI-based Clinical Trial Solutions, 2020-2030 (USD Million)
Table 14.40 Cost Saving Potential in Phase I Clinical Trials, 2020-2030 (USD Million)
Table 14.41 Cost Saving Potential in Phase II Clinical Trials, 2020-2030 (USD Million)
Table 14.42 Cost Saving Potential in Phase II Clinical Trials, 2020-2030 (USD Million)
Table 14.43 Cost Saving Potential in Patient Recruitment, 2020-2030 (USD Million)
Table 14.44 Cost Saving Potential in Patient Retention, 2020-2030 (USD Million)
Table 14.45 Cost Saving Potential in Site Monitoring, 2020-2030 (USD Million)
Table 14.46 Cost Saving Potential in Source Data Verification, 2020-2030 (USD Million)
Table 14.47 Overall AI-based Clinical Trial Solutions Market Opportunity, Conservative, Base and Optimistic Scenarios, 2020-2030 (USD Million)
Table 14.48 AI-based Clinical Trial Solutions Market Opportunity: Distribution by Trial Phase, Conservative, Base and Optimistic Scenarios, 2020 and 2030 (USD Million)
Table 14.49 AI-based Clinical Trial Solutions Market Opportunity: Distribution by Target Therapeutic Area, Conservative, Base and Optimistic Scenarios, 2020 and 2030 (USD Million)
Table 14.50 AI-based Clinical Trial Solutions Market Opportunity: Distribution by End-Users, Conservative, Base and Optimistic Scenarios, 2020 and 2030 (USD Million)
Table 14.51 AI-based Clinical Trial Solutions Market Opportunity: Distribution by Key Geographical Regions, Conservative, Base and Optimistic Scenarios, 2020 and 2030 (USD Million)
Table 14.52 AI-based Clinical Trial Solutions Market Opportunity in North America, Conservative, Base and Optimistic Scenarios, 2020-2030 (USD Million)
Table 14.53 AI-based Clinical Trial Solutions Market Opportunity in North America: Share of Cardiovascular Disorders, 2020-2030 (USD Million)
Table 14.54 AI-based Clinical Trial Solutions Market Opportunity in North America: Share of CNS Disorders, 2020-2030 (USD Million)
Table 14.55 AI-based Clinical Trial Solutions Market Opportunity in North America: Share of Infectious Disorders, 2020-2030 (USD Million)
Table 14.56 AI-based Clinical Trial Solutions Market Opportunity in North America: Share of Metabolic Disorders, 2020-2030 (USD Million)
Table 14.57 AI-based Clinical Trial Solutions Market Opportunity in North America: Share of Oncological Disorders, 2020-2030 (USD Million)
Table 14.58 AI-based Clinical Trial Solutions Market Opportunity in North America: Share of Other Disorders, 2020-2030 (USD Million)
Table 14.59 AI-based Clinical Trial Solutions Market Opportunity in North America: Share of Pharmaceutical Companies, 2020-2030 (USD Million)
Table 14.60 AI-based Clinical Trial Solutions Market Opportunity in North America: Share of Academia and Other Users, 2020-2030 (USD Million)
Table 14.61 AI-based Clinical Trial Solutions Market Opportunity in Europe, Conservative, Base and Optimistic Scenarios, 2020-2030 (USD Million)
Table 14.62 AI-based Clinical Trial Solutions Market Opportunity in Europe: Share of Cardiovascular Disorders, 2020-2030 (USD Million)
Table 14.63 AI-based Clinical Trial Solutions Market Opportunity in Europe: Share of CNS Disorders, 2020-2030 (USD Million)
Table 14.64 AI-based Clinical Trial Solutions Market Opportunity in Europe: Share of Infectious Disorders, 2020-2030 (USD Million)
Table 14.65 AI-based Clinical Trial Solutions Market Opportunity in Europe: Share of Metabolic Disorders, 2020-2030 (USD Million)
Table 14.66 AI-based Clinical Trial Solutions Market Opportunity in Europe: Share of Oncological Disorders, 2020-2030 (USD Million)
Table 14.67 AI-based Clinical Trial Solutions Market Opportunity in Europe: Share of Other Disorders, 2020-2030 (USD Million)
Table 14.68 AI-based Clinical Trial Solutions Market Opportunity in Europe: Share of Pharmaceutical Companies, 2020-2030 (USD Million)
Table 14.69 AI-based Clinical Trial Solutions Market Opportunity in Europe: Share of Academia and Other Users, 2020-2030 (USD Million)
Table 14.70 AI-based Clinical Trial Solutions Market Opportunity in Asia-Pacific, Conservative, Base and Optimistic Scenarios, 2020-2030 (USD Million)
Table 14.71 AI-based Clinical Trial Solutions Market Opportunity in Asia-Pacific: Share of Cardiovascular Disorders, 2020-2030 (USD Million)
Table 14.72 AI-based Clinical Trial Solutions Market Opportunity in Asia-Pacific: Share of CNS Disorders, 2020-2030 (USD Million)
Table 14.73 AI-based Clinical Trial Solutions Market Opportunity in Asia-Pacific: Share of Infectious Disorders, 2020-2030 (USD Million)
Table 14.74 AI-based Clinical Trial Solutions Market Opportunity in Asia-Pacific: Share of Metabolic Disorders, 2020-2030 (USD Million)
Table 14.75 AI-based Clinical Trial Solutions Market Opportunity in Asia-Pacific: Share of Oncological Disorders, 2020-2030 (USD Million)
Table 14.76 AI-based Clinical Trial Solutions Market Opportunity in Asia-Pacific: Share of Other Disorders, 2020-2030 (USD Million)
Table 14.77 AI-based Clinical Trial Solutions Market Opportunity in Asia-Pacific: Share of Pharmaceutical Companies, 2020-2030 (USD Million)
Table 14.78 AI-based Clinical Trial Solutions Market Opportunity in Asia-Pacific: Share of Academia and Other Users, 2020-2030 (USD Million)
Table 14.79 AI-based Clinical Trial Solutions Market Opportunity in Rest of the World, Conservative, Base and Optimistic Scenarios, 2020-2030 (USD Million)
Table 14.80 AI-based Clinical Trial Solutions Market Opportunity in Rest of the World: Share of Cardiovascular Disorders, 2020-2030 (USD Million)
Table 14.81 AI-based Clinical Trial Solutions Market Opportunity in Rest of the World: Share of CNS Disorders, 2020-2030 (USD Million)
Table 14.82 AI-based Clinical Trial Solutions Market Opportunity in Rest of the World: Share of Infectious Disorders, 2020-2030 (USD Million)
Table 14.83 AI-based Clinical Trial Solutions Market Opportunity in Rest of the World: Share of Metabolic Disorders, 2020-2030 (USD Million)
Table 14.84 AI-based Clinical Trial Solutions Market Opportunity in Rest of the World: Share of Oncological Disorders, 2020-2030 (USD Million)
Table 14.85 AI-based Clinical Trial Solutions Market Opportunity in Rest of the World: Share of Other Disorders, 2020-2030 (USD Million)
Table 14.86 AI-based Clinical Trial Solutions Market Opportunity in Rest of the World: Share of Pharmaceutical Companies, 2020-2030 (USD Million)
Table 14.87 AI-based Clinical Trial Solutions Market Opportunity in Rest of the World: Share of Academia and Other Users, 2020-2030 (USD Million)

Listed Companies

The following companies and organizations have been included in the report:

  1. A.I. VALI
  2. AbbVie
  3. Accenture
  4. AccuBeing
  5. AG Mednet
  6. Agent Health
  7. AiCure
  8. Aidar Health
  9. ÅKRN Scientific Consulting
  10. AliveCor
  11. Anaqua
  12. Anthem
  13. Antidote
  14. Aspen Insights
  15. AstraZeneca
  16. Avident Health
  17. Bayer
  18. Bioinfogate
  19. BlueData
  20. Bolton NHS Foundation Trust
  21. Brainpan Innovations
  22. Bristol-Myers Squibb
  23. Brite Health
  24. BullFrog AI
  25. Business Health Care Group
  26. Cambia Health Solutions
  27. Canary Speech
  28. Cancer Genetics
  29. Canon
  30. Carebox
  31. Carenet Health
  32. Carenity
  33. Carnegie Mellon University
  34. Catana Capital
  35. Cedar Health Research
  36. Celgene
  37. Central Ohio Primary Care
  38. Cerba Research
  39. Chainlink
  40. CHDI Foundation
  41. ChemAxon
  42. CIMS 
  43. Clarivate
  44. ClinArk
  45. Clinerion 
  46. Clinevo Technologies
  47. Clinical AI
  48. CliniOps
  49. Clinithink
  50. ClinTex
  51. CMIC
  52. Covance
  53. Crestle.ai
  54. Curify
  55. Darts-ip
  56. DataON
  57. Deep 6 AI
  58. Deep Lens
  59. DeepTrial
  60. Dell
  61. Department of Veterans Affairs
  62. DiA Imaging Analysis
  63. doc.ai
  64. EBSCO
  65. Egyptian Knowledge Bank
  66. eimageglobal
  67. Erlanger Health System
  68. ExperiMind Technologies
  69. fathom it group
  70. Flow Pharma
  71. GE Healthcare
  72. Genpro Research
  73. GNS Healthcare
  74. Google
  75. GlaxoSmithKline
  76. H2O.ai
  77. Halo Health 
  78. HCL
  79. Healint
  80. Healthix
  81. HealthMatch
  82. IBM
  83. ICON 
  84. iLoF - Intelligent Lab on Fiber
  85. IMNA Solutions
  86. Inato
  87. Indegene
  88. iNDX.Ai
  89. Innoplexus
  90. Inova Translational Medicine Institute
  91. Inspire
  92. Intel
  93. Intelligencia.ai
  94. Intrepid Analytics
  95. IP Australia
  96. IXICO
  97. Janssen Pharmaceuticals
  98. Johnson & Johnson
  99. Joovv
  100. Kadena
  101. Kognitic
  102. Kopernio
  103. Kryo
  104. Kx Systems
  105. KYT
  106. Leukemia & Lymphoma Society
  107. Lieber Institute for Brain Development
  108. Life Image
  109. Lokavant
  110. London Medical Imaging & Artificial Intelligence Centre
  111. Medable
  112. Medaptive Health
  113. Medairum
  114. Median Technologies
  115. Medica
  116. Medidata Solutions
  117. mediri
  118. Medtronic
  119. Mendel.ai
  120. Merck
  121. MGH Group
  122. Microsoft
  123. Mount Sinai Health System
  124. MRN
  125. Nanox
  126. NEC
  127. Nor-Tech
  128. Northern Data
  129. Novadiscovery
  130. Novartis
  131. Novoic
  132. nQ Medical
  133. Olea Medical
  134. OncoImmunity
  135. OncoSec Medical
  136. One Nucleus
  137. Oura 
  138. Owkin
  139. P360
  140. P3Life
  141. PangaeaData.AI
  142. Passage AI
  143. PatchAi
  144. PatientPoint
  145. PatienTrials
  146. Patiro
  147. Pear Therapeutics
  148. PenRad Technologies
  149. Pepgra
  150. Pfizer
  151. Pharmamodelling
  152. PHASTAR
  153. phaware
  154. Phesi 
  155. Precipio
  156. ProofPilot
  157. protocols.io
  158. PWNHealth
  159. Qmetrics Technologies
  160. QUIBIM
  161. Qure.ai
  162. Raylytic
  163. Redox 
  164. Remarque Systems
  165. Roche
  166. Royal Philips
  167. Rymedi
  168. Saama Technologies
  169. San Raffaele Hospital 
  170. Sanofi
  171. SAP
  172. Science37
  173. sensedat
  174. Sensyne Health
  175. ServiceNow
  176. SiteRx
  177. Skura Corporation
  178. Snowflake
  179. Springer Nature
  180. Syneos Health
  181. Synexus
  182. Talkdesk
  183. Translational Drug Development
  184. Teleradiology Solutions
  185. TeraRecon
  186. Teva Pharmaceuticals
  187. The ALS Association
  188. TrademarkVision
  189. tranScrip
  190. Trial Sense
  191. Trialcome
  192. TrialJectory
  193. Trials.ai
  194. TTi Health Research & Economics
  195. University of California
  196. University of Pennsylvania
  197. University of Pittsburgh
  198. Unlearn.AI
  199. Vanguard Scientific
  200. Veritas IRB
  201. VIDA
  202. Vivoryon Therapeutics
  203. Viz.ai
  204. Vizyon Technologies
  205. Vooban
  206. Wiley
  207. Winterlight Labs
  208. Worcestershire Health and Care NHS Trust
  209. Worldwide Clinical Trials
  210. Xingtai People's Hospital

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