At NVIDIA GTC, Arango announced that PSI CRO reduced clinical trial site identification from weeks to minutes using SYNETIC™. This AI-enabled knowledge engine runs on the Arango Contextual Data Platform and unifies fragmented clinical research data effectively. As a result, researchers identify high-performing trial sites faster, reduce non-enrolling institutions, and potentially save millions per trial.
Selecting suitable trial sites remains critical and expensive in drug development across global clinical research organizations. Clinical trials often take up to fifteen years, while operational expenses can exceed one hundred sixty dollars per minute. However, inefficiencies persist, as many sites under-enroll and some fail to recruit even a single patient.
Building Explainable AI for Clinical Research
PSI addressed this challenge by developing SYNETIC™, an AI-driven knowledge base built on Arango’s contextual data platform. The system unifies structured data, documents, and historical trial records into a single contextual data layer. Consequently, researchers analyze connections across investigators, institutions, protocols, and outcomes with improved clarity and efficiency.
The platform combines graph relationships, vector embeddings, and search capabilities to analyze extensive clinical research datasets efficiently. Therefore, teams detect patterns, identify successful investigators, and generate site recommendations in minutes rather than weeks. Importantly, SYNETIC™ delivers explainable insights, including rationale, evidence, confidence levels, and visibility into missing information gaps.
“For AI agents to be useful, teams need to trust the recommendations,” said Andrei Seryi, Director of Knowledge Management and Process Improvement at PSI CRO. “Our AI agent doesn’t just recommend trial sites – it explains the reasoning, highlights key study factors, and shows confidence levels, all grounded in a unified, current, and trusted business context.”
“Clinical trials depend on understanding complex relationships across enormous amounts of data, but too often that context is fragmented across systems,” Seryi added. “With the Arango Contextual Data Platform, we can unify that information, ground AI in trusted and explainable data, and identify the right study sites faster – helping avoid underperforming sites that can add millions to the cost of a single trial.”
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News Source: Businesswire.com
