Digital twin learning health systems and multimodal biomarkers transform pain care.

Abstract

Despite scientific advances, pain care remains fragmented, inaccessible, and imprecise. We propose a future in which Digital Twin Learning Health Systems (DT-LHS) transform pain management by integrating multimodal biomarkers, real-time data streams, and adaptive learning loops to personalize care. These systems simulate individual trajectories, forecast treatment responses, and update continuously based on outcomes. CHOIR, an open-source informatics platform, operationalizes this vision, turning routine clinical care into a scalable, continuously improving experiment. By merging biological insight with dynamic modeling and real-world feedback, DT-LHS offers a path toward truly personalized, responsive, accessible, and equitable pain care.