This Special Communication outlines a pragmatic, governance-aware schema for implementing digital twins (DTs) within a Learning Health System (LHS) and a companion Learning Research System (LRS), using multiple sclerosis (MS) as a translational model. Within the LHS, routine clinical data are transformed into actionable insights, while the LRS extends this process to discovery and iterative improvement. The NEurOinformatics Network (NEON) initiative initiated a DT framework for MS as a foundation for AI-enabled precision neurology. The DT integrates multimodal longitudinal data, including electronic health records, MRI, biomarkers, digital behavioral metrics, and registry datasets to model disease trajectories and guide individualized care. Synthetic data augment underrepresented cohorts, improving representativeness while safeguarding privacy. NEON’s multi-phase roadmap spans ideation through early clinical translation. Integrated within the Great Plains Institutional Development Award (GP IDeA) Clinical and Translational Research Network, the project advances data infrastructure, workforce development, and AI-driven translational science across IDeA states. Emerging insights highlight the importance of team science and institution-wide collaboration, while addressing barriers related to interoperability, digital biomarker validation, and limited AI workforce capacity. Collectively, this work positions DTs as scalable and generalizable assets for precision neurology within LHS and LRS environments.