The AI-First Protocol in Rare Diseases: Accelerating Research for the Few Who Need It Most

Rare-disease trials face unique challenges – small patient populations, dispersed sites, and limited data. This session explores how AI-first protocol design transforms feasibility and speed in rare-disease research, enabling smarter, patient-centric, and regulator-ready studies from day one. Key Takeaways:

  • How AI-first protocols reduce amendments and time-to-first-patient in small-population studies
  • Using real-world and registry data to simulate feasibility and optimize site selection
  • Building adaptive, patient-centric designs aligned with EMA and FDA expectations
  • Integrating AI into cross-functional workflows — from clinical to regulatory teams
  • Case examples showing measurable acceleration and cost savings in rare-disease trials

Human + Machine: Building an AI Culture in Pharma Data Teams

  • Human governance of AI: principles, boundaries, and a human-in-the-loop model for clinical data decisions
  • Responsible adoption: how to train, measure, and scale without losing traceability (ATR: Audit Trail Review, RBQM: Risk-Based Quality Management, quality, and ethics)
  • Practical architecture: rules + ML + agentic AI for clinical data review—what to automate vs. what to keep human