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Global AI-based Clinical Trial Solution Providers Market 2020-2030 – ResearchAndMarkets.com

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DUBLIN–(BUSINESS WIRE)–The “AI-based Clinical Trial Solution Providers Market, 2020-2030” report has been added to ResearchAndMarkets.com’s offering.

This 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.

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:

Market Insights

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. (Read more…) 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.

Amongst other elements, the report features:

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.

Key Questions Answered

Key Topics Covered

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

5.4. Deep Lens

5.5. Deep 6 AI

5.6. Innoplexus

5.7. Median Technologies

5.8. Mendel.ai

5.9. Phesi

5.10. Saama Technologies

5.11. Trials.ai

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

12.7.2. AI-based Clinical Trial Solutions Market Opportunity in Europe, 2020-2030

12.7.3. AI-based Clinical Trial Solutions Market Opportunity in Asia-Pacific, 2020-2030

12.7.4. AI-based Clinical Trial Solutions Market Opportunity in Rest of the World, 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

Companies Mentioned

For more information about this report visit https://www.researchandmarkets.com/r/lbs2qb

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