AI Research

AI-Based Clinical Trial Solutions for Patient Matching Market

Published

1 day ago

September 9, 2025

AI-Based Clinical Trial Solutions for Patient Matching Market

AI Based Clinical Trial Solutions for Patient Matching market was valued at approximately USD 300 million, and it is anticipated to grow to around USD 2.1 billion by 2033, representing a compound annual growth rate (CAGR) of about 22% between 2024 and 2033.

AI-Based Clinical Trial Solutions for Patient Matching Market Overview

The AI Based Clinical Trial Solutions for Patient Matching market is experiencing robust expansion as pharmaceutical companies and CROs increasingly rely on AI to speed up and streamline patient recruitment. Platforms powered by machine learning and natural language processing improve matching accuracy by analyzing vast datasets-such as electronic health records, genetic profiles, and clinical trial criteria-to identify eligible candidates faster. Real-time integration of heterogeneous data and AI-driven predictiveness enhance enrollment efficiency and trial success rates. Regulations and privacy safeguards remain pivotal as healthcare digitization advances. North America currently leads in adoption, but growth is accelerating globally, particularly in Asia Pacific and Europe. The rise of precision medicine and demand for faster, cost-effective trials underpin the market’s strong outlook through 2033.

Request a sample copy of this report at: https://www.omrglobal.com/request-sample/ai-based-clinical-trial-solutions-for-the-patient-matching-market

Advantages of requesting a Sample Copy of the Report:

1) To understand how our report can bring a difference to your business strategy

2) To understand the analysis and growth rate in your region

3) Graphical introduction of global as well as the regional analysis

4) Know the top key players in the market with their revenue analysis

5) SWOT analysis, PEST analysis, and Porter’s five force analysis

The report further explores the key business players along with their in-depth profiling

Patient recruitment, Personalized medicine, Drug development

AI-Based Clinical Trial Solutions for Patient Matching Market Segments:

◘ By Type: IBM Watson Health, Medidata, Parexel, Oracle, Covance, Veeva Systems, Phesi

◘ By Application: Predictive modeling, Genetic matching algorithms, Electronic health records integration

Report Drivers & Trends Analysis:

The report also discusses the factors driving and restraining market growth, as well as their specific impact on demand over the forecast period. Also highlighted in this report are growth factors, developments, trends, challenges, limitations, and growth opportunities. This section highlights emerging AI-Based Clinical Trial Solutions for Patient Matching Market trends and changing dynamics. Furthermore, the study provides a forward-looking perspective on various factors that are expected to boost the market’s overall growth.

Competitive Landscape Analysis:

In any market research analysis, the main field is competition. This section of the report provides a competitive scenario and portfolio of the AI-Based Clinical Trial Solutions for Patient MatchingMarket’s key players. Major and emerging market players are closely examined in terms of market share, gross margin, product portfolio, production, revenue, sales growth, and other significant factors. Furthermore, this information will assist players in studying critical strategies employed by market leaders in order to plan counterstrategies to gain a competitive advantage in the market.

Regional Outlook:

The following section of the report offers valuable insights into different regions and the key players operating within each of them. To assess the growth of a specific region or country, economic, social, environmental, technological, and political factors have been carefully considered. The section also provides readers with revenue and sales data for each region and country, gathered through comprehensive research. This information is intended to assist readers in determining the potential value of an investment in a particular region.

» North America (U.S., Canada, Mexico)

» Europe (Germany, U.K., France, Italy, Russia, Spain, Rest of Europe)

» Asia-Pacific (China, India, Japan, Singapore, Australia, New Zealand, Rest of APAC)

» South America (Brazil, Argentina, Rest of SA)

» Middle East & Africa (Turkey, Saudi Arabia, Iran, UAE, Africa, Rest of MEA)

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Key Benefits for Stakeholders:

⏩ The study represents a quantitative analysis of the present AI-Based Clinical Trial Solutions for Patient Matching Market trends, estimations, and dynamics of the market size from 2025 to 2032 to determine the most promising opportunities.

⏩ Porter’s five forces study emphasizes the importance of buyers and suppliers in assisting stakeholders to make profitable business decisions and expand their supplier-buyer network.

⏩ In-depth analysis, as well as the market size and segmentation, help you identify current AI-Based Clinical Trial Solutions for Patient Matching Market opportunities.

⏩ The largest countries in each region are mapped according to their revenue contribution to the market.

⏩ The AI-Based Clinical Trial Solutions for Patient Matching Market research report gives a thorough analysis of the current status of the AI-Based Clinical Trial Solutions for Patient Matching Market’s major players.

Key questions answered in the report:

➧What will the market development pace of the AI-Based Clinical Trial Solutions for Patient Matching Market?

➧What are the key factors driving the AI-Based Clinical Trial Solutions for Patient Matching Market?

➧Who are the key manufacturers in the market space?

➧ What are the market openings, market hazards,s and market outline of the AI-Based Clinical Trial Solutions for Patient Matching Market?

➧What are the sales, revenue, and price analysis of the top manufacturers of the AI-Based Clinical Trial Solutions for Patient Matching Market?

➧Who are the distributors, traders, and dealers of AI-Based Clinical Trial Solutions for Patient Matching Market?

➧What are the market opportunities and threats faced by the vendors in the AI-Based Clinical Trial Solutions for Patient Matching Market?

➧What are deals, income, and value examination by types and utilizations of the AI-Based Clinical Trial Solutions for Patient Matching Market?

➧What are deals, income, and value examination by areas of enterprises in the AI-Based Clinical Trial Solutions for Patient Matching Market?

Purchase Now Up to 25% Discount on This Premium Report: https://www.omrglobal.com/buy-now/ai-based-clinical-trial-solutions-for-the-patient-matching-market

Reasons To Buy The AI-Based Clinical Trial Solutions for Patient Matching Market Report:

➼ In-depth analysis of the market on the global and regional levels.

➼ Major changes in market dynamics and competitive landscape.

➼ Segmentation on the basis of type, application, geography, and others.

➼ Historical and future market research in terms of size, share growth, volume, and sales.

➼ Major changes and assessment in market dynamics and developments.

➼ Emerging key segments and regions

➼ Key business strategies by major market players and their key methods

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Mr. Anurag Tiwari

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About Orion Market Research

Orion Market Research (OMR) is a market research and consulting company known for its crisp and concise reports. The company is equipped with an experienced team of analysts and consultants. OMR offers quality syndicated research reports, customized research reports, consulting and other research-based services. The company also offers Digital Marketing services through its subsidiary OMR Digital and Software development and Consulting Services through another subsidiary Encanto Technologies.

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AI Research

Equipping artificial intelligence with the lens of evolution

Published

23 minutes ago

September 10, 2025

Meike Drießen

Credit: Computational and Structural Biotechnology Journal (2025). DOI: 10.1016/j.csbj.2025.08.015

Artificial intelligence is now better than humans at identifying many patterns, but evolutionary relationships have always been difficult for the technology to decipher. A team from the Bioinformatics Department at Ruhr University Bochum, Germany, working under Professor Axel Mosig has trained a neural network to tackle this issue.

The AI can relate any data from different species in an evolutionary relationship and identify which characteristics have developed in what manner throughout the course of evolution.

“Our approach lets artificial intelligence look at data through the lens of evolution, in a way,” explains Vivian Brandenburg, lead author of the report published in the Computational and Structural Biotechnology Journal on August 22, 2025.

Providing prior knowledge about the ancestry tree

“Most previous AI algorithms have a hard time analyzing biological data through an evolutionary lens, because they don’t know what to look for and get confused by random patterns,” says Mosig. The team has provided its AI with prior knowledge of the phylogenetic trees of the species being analyzed.

This approach is based on classifying groups of four species into the presumably correct ancestry tree when training the AI. The tree contains information about close and distant relationships. “If all groups of four are correctly arranged, the entire ancestry tree can come into place like a puzzle,” explains Luis Hack, who also worked on the study. “The AI can then look in the sequences to identify patterns that have evolved throughout this tree.”

The kicker: This method works not only for genetic sequence data, but also for any other type of data, such as image data or structural patterns of biomolecules from various species. After the bioinformaticists from RUB initially established the approach for DNA sequence data as part of their current work, they are already exploring its applicability for image data.

“For example, you could reconstruct hypothetical images of evolutionary predecessor species,” says Hack, explaining the method’s potential for future projects.

More information:
Vivian B. Brandenburg et al, A quartet-based approach for inferring phylogenetically informative features from genomic and phenomic data, Computational and Structural Biotechnology Journal (2025). DOI: 10.1016/j.csbj.2025.08.015

Provided by
Ruhr University Bochum

Citation:
Equipping artificial intelligence with the lens of evolution (2025, September 10)
retrieved 10 September 2025
from https://phys.org/news/2025-09-equipping-artificial-intelligence-lens-evolution.html

This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no
part may be reproduced without the written permission. The content is provided for information purposes only.

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AI Research

Meta Details AI Research Efforts at TBD Lab

Published

34 minutes ago

September 10, 2025

Faizan Farooque

This article first appeared on GuruFocus.

Meta Platforms Inc. (META, Financials) is advancing its artificial intelligence ambitions through a small research group called TBD Lab, which consists of a few dozen researchers and engineers, Chief Financial Officer Susan Li said Tuesday at the Goldman Sachs Communacopia + Technology conference.

The unit, whose placeholder name has stuck, is tasked with developing next-generation foundation models over the next one to two years. Li described the team as talent-dense and said its work would help push Meta’s AI portfolio closer to the frontier.

TBD Lab is one of four groups within Meta’s Superintelligence Labs, created earlier this year after the company reorganized its AI strategy. The other groups include a products team anchored by the Meta AI assistant, an infrastructure team, and the Fundamental AI Research (FAIR) lab.

The restructuring followed senior staff departures and what was seen as a muted reception for Meta’s latest open-source Llama 4 model. CEO Mark Zuckerberg has since taken a direct role in recruiting AI talent, making offers to startups and contacting candidates himself through WhatsApp with multimillion-dollar packages.

Investors will look to Meta’s next earnings update for signs of progress in AI development and how new models could fit into its products and services.

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AI Research

Commanders vs. Packers NFL props, SportsLine Machine Learning Model AI picks: Jordan Love Over 223.5 passing

Published

59 minutes ago

September 10, 2025

Daniel Kohn

The NFL Week 2 schedule gets underway with a Thursday Night Football matchup between NFC playoff teams from a year ago. The Washington Commanders battle the Green Bay Packers beginning at 8:15 p.m. ET from Lambeau Field in Green Bay. Second-year quarterback Jayden Daniels led the Commanders to a 21-6 opening-day win over the New York Giants, completing 19 of 30 passes for 233 yards and one touchdown. Jordan Love, meanwhile, helped propel the Packers to a dominating 27-13 win over the Detroit Lions in Week 1. He completed 16 of 22 passes for 188 yards and two touchdowns.

NFL prop bettors will likely target the two young quarterbacks with NFL prop picks, in addition to proven playmakers like Terry McLaurin, Tucker Kraft and Josh Jacobs. Green Bay’s Jayden Reed has been dealing with a foot injury, but still managed to haul in a touchdown pass in the opener. The Packers enter as a 3.5-point favorite with Green Bay at -187 on the money line. Before betting any Commanders vs. Packers props for Thursday Night Football, you need to see the Commanders vs. Packers prop predictions powered by SportsLine’s Machine Learning Model AI.

Built using cutting-edge artificial intelligence and machine learning techniques by SportsLine’s Data Science team, AI Predictions and AI Ratings are generated for each player prop.

For Packers vs. Commanders NFL betting on Monday Night Football, the Machine Learning Model has evaluated the NFL player prop odds and provided Bears vs. Vikings prop picks. You can only see the Machine Learning Model player prop predictions for Washington vs. Green Bay here.

Top NFL player prop bets for Commanders vs. Packers

After analyzing the Commanders vs. Packers props and examining the dozens of NFL player prop markets, the SportsLine’s Machine Learning Model says Packers quarterback Love goes Over 223.5 passing yards (-112 at FanDuel). Love passed for 224 or more yards in eight games a year ago, despite an injury-filled season. In 15 regular-season games in 2024, he completed 63.1% of his passes for 3,389 yards and 25 touchdowns with 11 interceptions.

In a 30-13 win over the Seattle Seahawks on Dec. 15, he completed 20 of 27 passes for 229 yards and two touchdowns. Love completed 21 of 28 passes for 274 yards and two scores in a 30-17 victory over the Miami Dolphins on Nov. 28. The model projects Love to pass for 259.5 yards, giving this prop bet a 4.5 rating out of 5. See more NFL props here, and new users can also target the FanDuel promo code, which offers new users $300 in bonus bets if their first $5 bet wins: