Springer Nature and expert.ai team up to bring AI to clinical trial planning

The collaboration launches “Clinical Trials Intelligence,” combining publisher-grade data and AI (artificial intelligence) to speed feasibility, protocol design, and execution.

Summary

Springer Nature and expert.ai announced a partnership to deliver Clinical Trials Intelligence, a new set of tools that blends Springer’s proprietary drug and trial data with expert.ai’s hybrid AI to turn unstructured content into guidance for clinical teams. The aim is faster feasibility work, clearer eligibility criteria, and smarter execution across the trial lifecycle. The solution debuts at BioTechX USA 2025 in Philadelphia. (PR Newswire)

What happened

• The partners introduced Clinical Trials Intelligence, a framework that integrates Springer Nature’s proprietary drug information (including AdisInsight) with expert.ai’s indexed datasets and hybrid AI, designed to deliver actionable trial insights in minutes. (PR Newswire)
• The tool focuses on common bottlenecks: patient and site identification, eligibility optimization, real-world evidence integration, and competitive landscape analysis—with outputs intended to support protocol design and operational decisions. (PR Newswire)
• Built-in natural language understanding converts literature and records into structured signals while keeping scientific judgment and ethics central to decision-making. (PR Newswire)

Key numbers and dates

• The dataset underpinning the solution spans 900,000+ validated global clinical trials, enriched with observational studies and scientific publications. (PR Newswire)
• Availability: Part of expert.ai’s EidenAI Suite; first public showcase at BioTechX USA 2025 (Philadelphia, Sept. 16–17, 2025). (PR Newswire)

What to watch next

• How sponsors and CROs adopt Clinical Trials Intelligence for feasibility and protocol drafting during Q4. (PR Newswire)
• Whether early users report shorter timelines from draft protocol to site start-up and improved screen-pass rates from refined eligibility logic. (PR Newswire)

Why this matters for you

Trial teams are under pressure to cut cycle time without sacrificing quality. A system that reads large volumes of literature and prior trials, then suggests tighter eligibility criteria and clearer site targeting, can reduce amendments and screen-fail waste. That means fewer back-and-forths with investigators and quicker movement from feasibility to first-patient-in—gains that compound across a portfolio.

Digital and data teams can use this as a blueprint for responsible AI in R&D. The partnership model pairs publisher-grade datasets with hybrid AI and natural language understanding, plus domain experts who keep outputs grounded. If you are building or buying similar tools, prioritize explainability, audit trails, and alignment with regulatory expectations so insights can flow straight into protocol templates, feasibility packets, and governance reviews.

Source: PR Newswire announcement and expert.ai news post. (PR Newswire)