Autoimmune diseases are dynamic, varied, and difficult to characterize using traditional clinical visits and static datasets. As drug developers rely on AI and biomarker-driven approaches to understand disease activity and therapeutic response, they face a fundamental limitation: most available data offers only isolated snapshots in time, missing the dynamic transitions that truly define immune-mediated conditions.
In this discussion, our speakers will explore how longitudinal, temporally relevant, multimodal data collected through direct connections with individuals can help unlock new opportunities across early R&D for immune-mediated diseases. Attendees will gain insights on how these data can be used to sharpen R&D confidence in early-phase programs and support AI-driven innovation in autoimmune drug development.
- Learn how to redesign your autoimmune data strategy by identifying gaps in current data sets and determining where continuous data collection can strengthen biomarkers and translational efforts
- Apply real world and molecular signals to detect early flare signatures and characterize transitions between disease states
- Integrate longitudinal evidence into biomarker development decisions
- Strengthen translational and early development strategy with time-based patterns in immune activity data