Artificial Intelligence in Neuromuscular Diseases: Opportunities for a Data-Scarce Field

Neuromuscular diseases (NMDs) encompass over 800 distinct entities affecting approximately one in 1000 individuals worldwide, with progressive muscle weakness, atrophy, and motor impairment as primary clinical manifestations. The rarity of most NMDs creates fundamental challenges for artificial intelligence (AI) and machine learning (ML) applications that typically require large-scale datasets. In this narrative review we synthesize the literature published between 2018 and 2025 on AI applications across the NMD spectrum, organized by clinical application domain. We examine how AI has advanced diagnostic capabilities through genetic variant interpretation, muscle magnetic resonance imaging analysis, electromyography-based classification, and computational pathology. In disease monitoring and prognosis, wearable-derived digital biomarkers have achieved regulatory qualification (US Food and Drug Administration [FDA] and European Medicines Agency [EMA]) as clinical trial endpoints for Duchenne muscular dystrophy, while AI-driven survival models for amyotrophic lateral sclerosis (ALS) have been validated across 14 European centers. Proteomic and multi-omics analyses using ML have identified diagnostic panels for ALS. However, most reported models were developed and internally validated on single-center datasets, and few have undergone external or prospective validation or clinical implementation. Despite these achievements, research intensity varies dramatically across NMD subtypes, with ALS and Duchenne muscular dystrophy dominating while myotonic dystrophy, congenital myopathies, and metabolic myopathies remain virtually unexplored. Critical gaps persist in computational pathology, multi-center validation, and clinical translation. In this review, we discuss how federated learning, international collaborative networks (TREAT-NMD, Solve-RD, EURO-NMD), and foundation models can address these challenges, and propose directions for future AI-enhanced clinical studies in this data-scarce field.