Artificial Intelligence-based Clinical Trial Solutions for Patient Matching Market Size, Share | Report 2022-2030
Drug discovery and development is a cost-intensive and time-consuming process. According to several studies, the capital investment required for drug development could cost more than USD 1.0 billion and a time period of more than 10 years. Adoption of AI-powered solutions in managing and conducting clinical trials can be beneficial in curbing these obstacles, thereby, reducing clinical trial cycle time & cost and increasing productivity & accuracy of the trial development. Drug makers have to invest a significant amount in drug development to replenish their respective product pipelines as a majority of big sellers go off patent, which is one of the major reasons for the clinical trials’ market growth. In addition, growing public and private initiatives to support the adoption of AI-powered technological solutions in clinical trial studies are driving the market growth. Pharmaceutical and biotechnology companies are rapidly adopting AI-based tools and platforms to support their clinical research studies. Companies are using these solutions for enhanced patient identification, recruitment, engagement, and real-time monitoring. Healthcare IT expenditures account for a large share of healthcare expense in developed countries. Rising awareness levels and the rate of digital transformation in developed countries are growing rapidly. Nations are adopting AI-based tools to enhance efficiency and reduce costs.
Digitization in biomedical and clinical research is creating the way for the growth of AI-based clinical trials solutions for the patient matching market. Major pharmaceutical companies are adopting technologically advanced solutions for better clinical trial results and patient management. In addition, the COVID-19 pandemic has changed the perception of clinical trials. It has increased the utilization and penetration of AI-based solutions for reducing time & cost. Hence, these factors are increasing the adoption of AI in pharmaceutical companies. For instance, major pharmaceutical companies such as Pfizer, Johnson & Johnson, Sanofi, Novartis, and Bayer have initiated strategic alliances for the adoption of AI-based solutions in drug discovery & development and clinical trials management. Governments of developed nations such as the U.S. are providing both funding and laying out a stringent regulatory framework to boost the adoption of AI-based solutions for clinical trial studies. In addition, governments of developing nations are spreading awareness amongst stakeholders to drive new drug discoveries and accelerate patient recruitment and enhance patient engagement & monitoring using AI-powered solutions.
AI-based clinical trial solutions for patient matching can help enhance patient recruitment by reducing patient population heterogeneity by harmonizing large patient health information data from a wide array of platforms & sources such as electronic medical records (EMRs), omics data, and medical imaging. These solutions enable choosing the most appropriate patient population groups with the highest likelihood of responding to the trial and a higher probability of providing a measurable & quantifiable clinical endpoint. AI-based systems and ssolutions can be deployed in analyzing patient health information records & clinical trial eligibility criterion and matching them with recruiting clinical trial studies. For instance, AI-powered clinical trial solutions used in patient matching have increased patient recruitment in lung cancer trial studies by 58.4%. AI-based solutions integrate natural language processing (NLP) algorithms which improve the match rate between clinical trials and patient enrollment.
The therapeutic application segment has been further classified into oncology, cardiovascular diseases, neurological diseases, metabolic diseases, infectious diseases, and others.The oncology application segment portrays the highest growth potential owing to the growing expenditure towards the pre-clinical and clinical development of oncology therapy products. AI-driven solutions offer an exceptional capability to accelerate the process of development of oncology drugs with a higher success rate. The end-user segment is bifurcated into pharmaceutical companies, academia, and others. Pharmaceutical companies are rapidly adopting AI-based solutions in designing and planning their clinical trial studies. Growing awareness levels and increasing funding for adopting these advanced technologies are driving the growth.
Nice entrepreneurial startups offering patient-matching AI-based solutions to life science organizations for their clinical studies are directly contributing to the market growth. Some of the renowned market participants are Unlearn.AI, Inc.; Deep6.ai; Deep Lens AI; AmeriSourceBergen Corporation; and Antidote Technologies, Inc. For instance, a France-based startup, Owkin focuses on making clinical trial studies more collaborative by promoting collaboration amongst multiple stakeholders and integrating patient health information data arising from multiple platforms. The company uses federated learning approaches to train its algorithms. Similarly, Deep Lens AI, was founded in 2017 with the ultimate focus of delivering advanced patient matching solutions and laid the foundation of the Virtual Imaging for Pathology Education and Research (VIPER) solution. For instance, in April 2021, Deep Lens AI and Oregon Oncology Specialists entered a strategic collaboration to deploy Deep Lens AI-powered clinical trial solutions to identify eligible patients for clinical trial studies.
Key Players
Market Segmentation
By Therapeutic Application Outlook
By End-Use Outlook
By Regional Outlook
Chapter 1. Introduction
1.1. Research Objective
1.2. Scope of the Study
1.3. Definition
Chapter 2. Research Methodology
2.1. Research Approach
2.2. Data Sources
2.3. Assumptions & Limitations
Chapter 3. Executive Summary
3.1. Market Snapshot
Chapter 4. Market Variables and Scope
4.1. Introduction
4.2. Market Classification and Scope
4.3. Industry Value Chain Analysis
4.3.1. Raw Material Procurement Analysis
4.3.2. Sales and Distribution Channel Analysis
4.3.3. Downstream Buyer Analysis
Chapter 5. COVID 19 Impact on Artificial Intelligence-based Clinical Trial Solutions for Patient Matching Market
5.1. COVID-19 Landscape: Artificial Intelligence-based Clinical Trial Solutions for Patient Matching Industry Impact
5.2. COVID 19 - Impact Assessment for the Industry
5.3. COVID 19 Impact: Global Major Government Policy
5.4. Market Trends and Opportunities in the COVID-19 Landscape
Chapter 6. Market Dynamics Analysis and Trends
6.1. Market Dynamics
6.1.1. Market Drivers
6.1.2. Market Restraints
6.1.3. Market Opportunities
6.2. Porter’s Five Forces Analysis
6.2.1. Bargaining power of suppliers
6.2.2. Bargaining power of buyers
6.2.3. Threat of substitute
6.2.4. Threat of new entrants
6.2.5. Degree of competition
Chapter 7. Competitive Landscape
7.1.1. Company Market Share/Positioning Analysis
7.1.2. Key Strategies Adopted by Players
7.1.3. Vendor Landscape
7.1.3.1. List of Suppliers
7.1.3.2. List of Buyers
Chapter 8. Global Artificial Intelligence-based Clinical Trial Solutions for Patient Matching Market, By Therapeutic Application
8.1. Artificial Intelligence-based Clinical Trial Solutions for Patient Matching Market, by Therapeutic Application, 2022-2030
8.1.1. Oncology
8.1.1.1. Market Revenue and Forecast (2017-2030)
8.1.2. Cardiovascular Diseases
8.1.2.1. Market Revenue and Forecast (2017-2030)
8.1.3. Neurological Diseases or Conditions
8.1.3.1. Market Revenue and Forecast (2017-2030)
8.1.4. Metabolic Diseases
8.1.4.1. Market Revenue and Forecast (2017-2030)
8.1.5. Infectious Diseases
8.1.5.1. Market Revenue and Forecast (2017-2030)
8.1.6. Others
8.1.6.1. Market Revenue and Forecast (2017-2030)
Chapter 9. Global Artificial Intelligence-based Clinical Trial Solutions for Patient Matching Market, By End-Use
9.1. Artificial Intelligence-based Clinical Trial Solutions for Patient Matching Market, by End-Use, 2022-2030
9.1.1. Pharmaceutical Companies
9.1.1.1. Market Revenue and Forecast (2017-2030)
9.1.2. Academia
9.1.2.1. Market Revenue and Forecast (2017-2030)
9.1.3. Others
9.1.3.1. Market Revenue and Forecast (2017-2030)
Chapter 10. Global Artificial Intelligence-based Clinical Trial Solutions for Patient Matching Market, Regional Estimates and Trend Forecast
10.1. North America
10.1.1. Market Revenue and Forecast, by Therapeutic Application (2017-2030)
10.1.2. Market Revenue and Forecast, by End-Use (2017-2030)
10.1.3. U.S.
10.1.3.1. Market Revenue and Forecast, by Therapeutic Application (2017-2030)
10.1.3.2. Market Revenue and Forecast, by End-Use (2017-2030)
10.1.4. Rest of North America
10.1.4.1. Market Revenue and Forecast, by Therapeutic Application (2017-2030)
10.1.4.2. Market Revenue and Forecast, by End-Use (2017-2030)
10.2. Europe
10.2.1. Market Revenue and Forecast, by Therapeutic Application (2017-2030)
10.2.2. Market Revenue and Forecast, by End-Use (2017-2030)
10.2.3. UK
10.2.3.1. Market Revenue and Forecast, by Therapeutic Application (2017-2030)
10.2.3.2. Market Revenue and Forecast, by End-Use (2017-2030)
10.2.4. Germany
10.2.4.1. Market Revenue and Forecast, by Therapeutic Application (2017-2030)
10.2.4.2. Market Revenue and Forecast, by End-Use (2017-2030)
10.2.5. France
10.2.5.1. Market Revenue and Forecast, by Therapeutic Application (2017-2030)
10.2.5.2. Market Revenue and Forecast, by End-Use (2017-2030)
10.2.6. Rest of Europe
10.2.6.1. Market Revenue and Forecast, by Therapeutic Application (2017-2030)
10.2.6.2. Market Revenue and Forecast, by End-Use (2017-2030)
10.3. APAC
10.3.1. Market Revenue and Forecast, by Therapeutic Application (2017-2030)
10.3.2. Market Revenue and Forecast, by End-Use (2017-2030)
10.3.3. India
10.3.3.1. Market Revenue and Forecast, by Therapeutic Application (2017-2030)
10.3.3.2. Market Revenue and Forecast, by End-Use (2017-2030)
10.3.4. China
10.3.4.1. Market Revenue and Forecast, by Therapeutic Application (2017-2030)
10.3.4.2. Market Revenue and Forecast, by End-Use (2017-2030)
10.3.5. Japan
10.3.5.1. Market Revenue and Forecast, by Therapeutic Application (2017-2030)
10.3.5.2. Market Revenue and Forecast, by End-Use (2017-2030)
10.3.6. Rest of APAC
10.3.6.1. Market Revenue and Forecast, by Therapeutic Application (2017-2030)
10.3.6.2. Market Revenue and Forecast, by End-Use (2017-2030)
10.4. MEA
10.4.1. Market Revenue and Forecast, by Therapeutic Application (2017-2030)
10.4.2. Market Revenue and Forecast, by End-Use (2017-2030)
10.4.3. GCC
10.4.3.1. Market Revenue and Forecast, by Therapeutic Application (2017-2030)
10.4.3.2. Market Revenue and Forecast, by End-Use (2017-2030)
10.4.4. North Africa
10.4.4.1. Market Revenue and Forecast, by Therapeutic Application (2017-2030)
10.4.4.2. Market Revenue and Forecast, by End-Use (2017-2030)
10.4.5. South Africa
10.4.5.1. Market Revenue and Forecast, by Therapeutic Application (2017-2030)
10.4.5.2. Market Revenue and Forecast, by End-Use (2017-2030)
10.4.6. Rest of MEA
10.4.6.1. Market Revenue and Forecast, by Therapeutic Application (2017-2030)
10.4.6.2. Market Revenue and Forecast, by End-Use (2017-2030)
10.5. Latin America
10.5.1. Market Revenue and Forecast, by Therapeutic Application (2017-2030)
10.5.2. Market Revenue and Forecast, by End-Use (2017-2030)
10.5.3. Brazil
10.5.3.1. Market Revenue and Forecast, by Therapeutic Application (2017-2030)
10.5.3.2. Market Revenue and Forecast, by End-Use (2017-2030)
10.5.4. Rest of LATAM
10.5.4.1. Market Revenue and Forecast, by Therapeutic Application (2017-2030)
10.5.4.2. Market Revenue and Forecast, by End-Use (2017-2030)
Chapter 11. Company Profiles
11.1. Unlearn.AI, Inc.
11.1.1. Company Overview
11.1.2. Product Offerings
11.1.3. Financial Performance
11.1.4. Recent Initiatives
11.2. Antidote Technologies, Inc.
11.2.1. Company Overview
11.2.2. Product Offerings
11.2.3. Financial Performance
11.2.4. Recent Initiatives
11.3. Deep6.ai
11.3.1. Company Overview
11.3.2. Product Offerings
11.3.3. Financial Performance
11.3.4. Recent Initiatives
11.4. Mendel.ai
11.4.1. Company Overview
11.4.2. Product Offerings
11.4.3. Financial Performance
11.4.4. LTE Scientific
11.5. Aris Global
11.5.1. Company Overview
11.5.2. Product Offerings
11.5.3. Financial Performance
11.5.4. Recent Initiatives
11.6. Deep Lens AI
11.6.1. Company Overview
11.6.2. Product Offerings
11.6.3. Financial Performance
11.6.4. Recent Initiatives
11.7. AmeriSourceBergen Corporation
11.7.1. Company Overview
11.7.2. Product Offerings
11.7.3. Financial Performance
11.7.4. Recent Initiatives
11.8. Koneksa
11.8.1. Company Overview
11.8.2. Product Offerings
11.8.3. Financial Performance
11.8.4. Recent Initiatives
11.9. Microsoft Corporation
11.9.1. Company Overview
11.9.2. Product Offerings
11.9.3. Financial Performance
11.9.4. Recent Initiatives
11.10. GNS Healthcare
11.10.1. Company Overview
11.10.2. Product Offerings
11.10.3. Financial Performance
11.10.4. Recent Initiatives
Chapter 12. Research Methodology
12.1. Primary Research
12.2. Secondary Research
12.3. Assumptions
Chapter 13. Appendix
13.1. About Us
13.2. Glossary of Terms
Cross-segment Market Size and Analysis for
Mentioned Segments
Additional Company Profiles (Upto 5 With No Cost)
Additional Countries (Apart From Mentioned Countries)
Country/Region-specific Report
Go To Market Strategy
Region Specific Market DynamicsRegion Level Market Share Import Export AnalysisProduction AnalysisOthers