Success achieved in reducing trial costs and timelines drives 2026 platform enhancements designed to democratize clinical research analytics.
TriNetX, a global leader in real-world data solutions, shared results demonstrating how its TriNetX LIVE platform is reshaping the way pharmaceutical companies plan and conduct clinical trials. By combining extensive real-world data with artificial intelligence, the platform has reduced costly protocol changes and improved the speed and accuracy of site identification, providing measurable benefits for researchers and sponsors.
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In early 2026, TriNetX will roll out AI-powered natural language search capabilities that allow researchers to query the network directly, removing traditional barriers to advanced analytics. These features are currently in beta with select customers, who are helping refine the system ahead of its full release.
Steve Kundrot, Chief Operating Officer at TriNetX, emphasized, “We are removing the roadblocks between researchers and insights. Every data point connects to real patient outcomes. Trials are filling faster, cancers are detected sooner, and treatments reach patients months or even years earlier.”
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TriNetX achieved significant growth in 2025, expanding its global network to more than 280 million patients, up from 273 million, mapping over 10,000 clinical trial sites, and connecting with 220 healthcare organizations across four continents. The platform supported more than 1,400 peer-reviewed publications in areas including oncology, cardiology, neurology, and ophthalmology, doubling the prior year’s total and bringing the cumulative count to over 2,500 publications.
The company’s AI applications are addressing some of the biggest challenges in clinical research. By leveraging real-world data, TriNetX helped sponsors reduce protocol amendments by up to 50 percent, speeding study timelines and lowering costs. Its site identification approach now starts by identifying patients first and then locating the sites where they receive care, which in one case achieved a 63 percent site acceptance rate with an average response time of nine days. Machine learning models have improved patient recruitment, such as in Crohn’s disease, where predicted trial enrollment rates increased from 33 percent to 85 percent. An AI model for pancreatic cancer can predict risk up to 18 months in advance, using 87 predictive features to guide early detection and tailored intervention strategies, and is now being validated on a prospective cohort of six million patients.
Looking ahead, TriNetX will introduce enhancements to TriNetX LIVE that aim to make clinical research analytics accessible to all researchers. The new conversational AI interface will allow users to ask complex questions in natural language and receive instant, sophisticated analyses without technical expertise. Expanded API functionality will integrate study queries into existing systems, providing real-time patient counts, feasibility data, and site insights. These improvements are designed to remove silos, accelerate study planning, and support enterprise-wide digital transformation, bringing advanced, autonomous AI-driven decision-making closer to reality.
Kundrot concluded, “By giving scientists the ability to ask questions in plain language and receive immediate, high-quality insights, we are fundamentally changing participation in clinical research and how quickly patients can access new treatments.”
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