Schedule

Roughly 40% of the clinical facts research needs never reach a structured data field: diagnoses, medication adherence, biomarkers, staging, social determinants, family history. Frontier LLMs can read that text, but at population scale they're expensive, non-deterministic, and hard to audit. This session shows how specialized medical language models extract and de-identify clinical facts at regulatory-grade accuracy: 98% F1 on PHI detection, and primary site, histology, and tumor staging extracted from unstructured pathology text at regulatory-grade accuracy (over 95%) – all at over 80% lower cost than current frontier models, with deterministic, reproducible output. Those facts become a governed, OMOP-standard real-world-evidence asset, with every value traced to its source note and every extraction carrying a confidence score. With that foundation in place, and a shared MCP boundary on top of it, use cases like cohort building, real-world evidence, clinical trial matching, and protocol design become far easier to build and to audit.

Large language models have transformed what is possible in regulatory content creation, but generating fluent text is only the beginning. As the industry moves toward agentic workflows and increasingly automated submission processes, the defining challenge is no longer whether AI can write, but whether its outputs can be trusted.

This session explores what separates regulatory grade AI from general purpose AI, and why successful implementations in regulated environments require more than a language model. Attendees will learn how organizations are combining LLMs with deterministic logic, structured domain knowledge, validation frameworks, and governed human oversight to produce outputs that support the expectations of regulated content development.

Drawing on emerging industry practices and evolving regulatory expectations, the session provides a practical framework for evaluating AI solutions against the capabilities that matter most in regulated environments, including transparency, repeatability, auditability, source traceability, and accountability. Attendees will leave with a concrete framework for assessing whether an AI system is truly designed for regulatory workflows.

ny AI discussions emphasize the size of the dataset and the cleverness of the model. In practice, those factors are rarely where the real difference is made.

What actually moves things forward is quieter, and far harder to get right: clean, carefully chosen data, and one problem that genuinely matters in practice.

This session is a behind-the-scenes case study of how Innomagine moved from concept to action. We didn’t start with the technology — we started with the people behind the work, and built a connected suite of engagement solutions on one foundation: the Evidence Eagle platform.

We’ll walk through three AI applications already used every day:

• Medical Simulator helps MSLs practice realistic KOL conversations with AI-powered feedback.

• Institutional Engagement Eagle shows who to engage, why they matter, and how to reach them.

• AI-generated Engagement Plans turn existing research into clear, actionable strategies.

  Because all three use the same trusted data, insights flow seamlessly from research to engagementtoaction.

  We then turn to the question every team faces: build, or buy? Building in-house is tempting, but the hard parts surface later — curating data you can trust, keeping scarce AI talent, and maintaining infrastructure long after the pilot ends. We make the case for the “smart buy”: a focused partner whose platform, curated data, and proven use cases are ready today — so teams reach value in weeks, not years.

  The takeaway: look past the big-data hype, get the build-versus-buy call right, and start with clean data and one problem worth solving. That’s where meaningful AI adoption begins — and where real impact, for your teams and the patients at the end of it, follows.