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Global AI-based Clinical Trial Solutions for Patient Matching Market Size is valued at USD 469.2 Million in 2024 and is predicted to reach USD 6205.4 Million by the year 2034 at a 29.6% CAGR during the forecast period for 2025-2034.
Key Industry Insights & Findings from the Report:
A network-wide clinical trial matching tool powered by artificial intelligence (AI) called VIPER. AI technology is intended to enable, prioritize, and expedite patient enrollment during the trial recruitment process. The use of AI-powered solutions in controlling and implementing medical testing is responsible for the market’s expansion. These solutions can help shorten the duration of the clinical trial cycle, which lowers costs and improves reliability while also improving the efficiency of trial production. The primary driver of AI-based clinical trial solutions for the patient matching market is that a massive percentage of pharmaceutical companies are forced to make significant investments in new drug development to increase the number of products in their product pipelines. Clinical and biomedical research using digitalization is creating prospects for AI-based clinical trial solutions for the patient-matching market.
Leading pharma companies are incorporating cutting-edge technological solutions for improved patient management and clinical outcomes. Government entities in developed nations fund the development of AI-based clinical trial survey technologies while establishing a strong regulatory foundation. Additionally, governments in emerging economies are educating stakeholders about AI-based clinical trial solutions so that they may concentrate on finding novel treatments and expediting patient enrollment, which will increase patient experience and monitoring. However, risks associated with data privacy might constrain AI-based clinical trial solutions for the patient-matching market in the coming years.
The Ai-based clinical trial solutions for the patient matching market are segmented based on therapeutic application and end users. Based on therapeutic applications, the AI-based clinical trial solutions for the patient matching market is segmented as oncology, cardiovascular diseases, neurological diseases or condition, metabolic diseases, infectious diseases, and others. By application, the AI-based clinical trial solutions for the patient matching market are segmented into pharmaceutical companies, academia, and others.
The pharmaceutical company’s segment is expected to hold a major share in the global AI-based clinical trial solutions for patient matching market in 2021 due to the increased emphasis on creating biomarkers and diagnostics that work better, employing AI-based innovations to find new drug targets and streamlining the application development. The expanding pharmaceutical sector is further fueling the market due to the prevalence of chronic diseases and the rising demand for new treatments. The market is also benefiting from a partnership between major pharmaceutical companies and AI providers to use AI technologies designed for the entire process of discovering new medicines.
The oncology segment is projected to grow rapidly in the global AI-based clinical trial solutions for the patient matching market. The number of AI-based clinical trial solutions for patient matching is rising due to the rising incidence rate of cancer worldwide, positively affecting the industry. Leading pharmaceutical corporations are also collaborating with AI development firms to deploy AI-based oncology technologies created for the creation of pharmaceuticals., especially in countries such as the US, Germany, the UK, China, and India.
The North America AI-based clinical trial solutions for the patient matching market are expected to register the highest market share in revenue soon. The development of a sizable patient population, affordable access to highly skilled labor, and reasonable hiring rates are the fundamental causes of this. The market’s growth is primarily driven by increased usage of AI-based technologies and rising awareness of these solutions. A greater number of startups have emerged due to the growing consumption, which is anticipated to create new prospects for the industry in this region.
In addition, Asia Pacific is projected to grow at a rapid rate in the global AI-based clinical trial solutions for the patient matching market. The growing demand for AI-based technology and the incorporation of government initiatives contribute to the region’s growth. Major market players are also boosting their R&D in AI-based technologies for clinical trials to enhance their market shares. Activities include joint ventures, agreements, affiliations, and other strategic alliances with rival market participants.
Recent Developments:
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Report Attribute |
Specifications |
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Market size value in 2024 |
USD 469.2 Million |
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Revenue forecast in 2034 |
USD 6205.4 Million |
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Growth rate CAGR |
CAGR of 29.6% from 2025 to 2034 |
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Quantitative units |
Representation of revenue in US$Mn and CAGR from 2025 to 2034 |
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Historic Year |
2021 to 2024 |
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Forecast Year |
2025-2035 |
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Report coverage |
The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
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Segments covered |
Therapeutic Application, End-Use |
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Regional scope |
North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
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Country scope |
U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico ;The UK; France; Italy; Spain; China; Japan; India; South Korea; South East Asia; South Korea; South East Asia |
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Competitive Landscape |
Unlearn.AI, Inc., Antidote Technologies, Inc., Deep6.ai, Mendel.ai, Aris Global, Deep Lens AI, AmerisourceBergen Corporation, Konaks, Microsoft Corporation, GNS Healthcare. |
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Customization scope |
Free customization report with the procurement of the report, Modifications to the regional and segment scope. Particular Geographic competitive landscape. |
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Pricing and available payment methods |
Explore pricing alternatives that are customized to your particular study requirements. |
Chapter 1. Methodology and Scope
1.1. Research Methodology
1.2. Research Scope & Assumptions
Chapter 2. Executive Summary
Chapter 3. Global AI-Based Clinical Trial Solutions for Patient Matching Market Snapshot
Chapter 4. Global AI-Based Clinical Trial Solutions for Patient Matching Market Variables, Trends & Scope
4.1. Market Segmentation & Scope
4.2. Drivers
4.3. Challenges
4.4. Trends
4.5. Investment and Funding Analysis
4.6. Industry Analysis – Porter’s Five Forces Analysis
4.7. Competitive Landscape & Market Share Analysis
4.8. Impact of Covid-19 Analysis
Chapter 5. Market Segmentation 1: by Therapeutic Application Estimates & Trend Analysis
5.1. by Therapeutic Application & Market Share, 2024 & 2034
5.2. Market Size (Value (US$ Mn) & Volume (no. of units) & Forecasts and Trend Analyses, 2021 to 2034 for the following by Therapeutic Application:
5.2.1. Oncology
5.2.2. Cardiovascular Diseases
5.2.3. Neurological Diseases or Conditions
5.2.4. Metabolic Diseases
5.2.5. Infectious Diseases
5.2.6. Others
Chapter 6. Market Segmentation 2: by End-users Estimates & Trend Analysis
6.1. by End-users & Market Share, 2024 & 2034
6.2. Market Size (Value (US$ Mn) & Volume (no. of units) & Forecasts and Trend Analyses, 2021 to 2034 for the following by End-users:
6.2.1. Pharmaceutical Companies
6.2.2. Academia
6.2.3. Others
Chapter 7. AI-Based Clinical Trial Solutions for Patient Matching Market Segmentation 3: Regional Estimates & Trend Analysis
7.1. North America
7.1.1. North America AI-Based Clinical Trial Solutions for Patient Matching Market Revenue (US$ Million) Estimates and Forecasts by Therapeutic Application, 2021-2034
7.1.2. North America AI-Based Clinical Trial Solutions for Patient Matching Market Revenue (US$ Million) Estimates and Forecasts by End-users, 2021-2034
7.1.3. North America AI-Based Clinical Trial Solutions for Patient Matching Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
7.2. Europe
7.2.1. Europe AI-Based Clinical Trial Solutions for Patient Matching Market revenue (US$ Million) by Therapeutic Application, 2021-2034
7.2.2. Europe AI-Based Clinical Trial Solutions for Patient Matching Market revenue (US$ Million) by End-users, 2021-2034
7.2.3. Europe AI-Based Clinical Trial Solutions for Patient Matching Market revenue (US$ Million) by country, 2021-2034
7.3. Asia Pacific
7.3.1. Asia Pacific AI-Based Clinical Trial Solutions for Patient Matching Market revenue (US$ Million) by Therapeutic Application, 2021-2034
7.3.2. Asia Pacific AI-Based Clinical Trial Solutions for Patient Matching Market revenue (US$ Million) by End-users, 2021-2034
7.3.3. Asia Pacific AI-Based Clinical Trial Solutions for Patient Matching Market revenue (US$ Million) by country, 2021-2034
7.4. Latin America
7.4.1. Latin America AI-Based Clinical Trial Solutions for Patient Matching Market revenue (US$ Million) by Therapeutic Application, 2021-2034
7.4.2. Latin America AI-Based Clinical Trial Solutions for Patient Matching Market revenue (US$ Million) by End-users, 2021-2034
7.4.3. Latin America AI-Based Clinical Trial Solutions for Patient Matching Market revenue (US$ Million) by country, 2021-2034
7.5. Middle East & Africa
7.5.1. Middle East & Africa AI-Based Clinical Trial Solutions for Patient Matching Market revenue (US$ Million) by Therapeutic Application, 2021-2034
7.5.2. Middle East & Africa AI-Based Clinical Trial Solutions for Patient Matching Market revenue (US$ Million) by End-users, 2021-2034
7.5.3. Middle East & Africa AI-Based Clinical Trial Solutions for Patient Matching Market revenue (US$ Million) by country, 2021-2034
Chapter 8. Competitive Landscape
8.1. Major Mergers and Acquisitions/Strategic Alliances
8.2. Company Profiles
8.2.1. Unlearn.AI, Inc.
8.2.2. Antidote Technologies, Inc.
8.2.3. Deep6.ai
8.2.4. Mendel.ai
8.2.5. Aris Global
8.2.6. Deep Lens AI
8.2.7. AmerisourceBergen Corporation
8.2.8. Konaks
8.2.9. Microsoft Corporation
8.2.10. GNS Healthcare
8.2.11. Other Prominent Players
By Therapeutic Application
By End-use
By Region-
North America-
Europe-
Asia-Pacific-
Latin America-
Middle East & Africa-
InsightAce Analytic follows a standard and comprehensive market research methodology focused on offering the most accurate and precise market insights. The methods followed for all our market research studies include three significant steps – primary research, secondary research, and data modeling and analysis - to derive the current market size and forecast it over the forecast period. In this study, these three steps were used iteratively to generate valid data points (minimum deviation), which were cross-validated through multiple approaches mentioned below in the data modeling section.
Through secondary research methods, information on the market under study, its peer, and the parent market was collected. This information was then entered into data models. The resulted data points and insights were then validated by primary participants.
Based on additional insights from these primary participants, more directional efforts were put into doing secondary research and optimize data models. This process was repeated till all data models used in the study produced similar results (with minimum deviation). This way, this iterative process was able to generate the most accurate market numbers and qualitative insights.
Secondary research
The secondary research sources that are typically mentioned to include, but are not limited to:
The paid sources for secondary research like Factiva, OneSource, Hoovers, and Statista
Primary Research:
Primary research involves telephonic interviews, e-mail interactions, as well as face-to-face interviews for each market, category, segment, and subsegment across geographies
The contributors who typically take part in such a course include, but are not limited to:
Data Modeling and Analysis:
In the iterative process (mentioned above), data models received inputs from primary as well as secondary sources. But analysts working on these models were the key. They used their extensive knowledge and experience about industry and topic to make changes and fine-tuning these models as per the product/service under study.
The standard data models used while studying this market were the top-down and bottom-up approaches and the company shares analysis model. However, other methods were also used along with these – which were specific to the industry and product/service under study.
To know more about the research methodology used for this study, kindly contact us/click here.
