Artificial Intelligence and Data Science for Spinal Cord Injury: Bridging Clinical and Computational Perspectives

Modern methods of data science have become powerful tools for analyzing complex, heterogeneous biomedical datasets, including those specific to spinal cord injury (SCI), enabling personalized predictions of recovery, identification of prognostic biomarkers, and insights into functional outcomes. This article summarizes the one-day Data Science Precourse, held at the 2025 annual scientific meeting of the American Spinal Injury Association (ASIA). The course was designed to illustrate advances in data science and their application to SCI research, while analyzing the unique challenges posed by SCI-specific data, such as sparse longitudinal measurements, variable injury characteristics, and diverse clinical and functional assessments. The precourse combined expert-led discussions with hands-on learning opportunities tailored for both clinical and data science audiences. Topics included addressing key challenges of artificial intelligence methods in SCI data analyses, such as learning from limited or incomplete datasets, implementing causal frameworks to understand recovery mechanisms, and ensuring robust and interpretable predictions for clinical decision-making. The event also showcased real-world applications of data science in SCI research, highlighting both solved and ongoing problems, including prognostic modeling, patient stratification, lesion analysis, and optimization of clinical trial design. The precourse culminated in the presentation by the winning teams of the 2025 ASIA Data Science Challenge.