Artificial Intelligence and Neuromuscular Diseases: A Narrative Review of Applications to Diagnosis, Prognosis, and Therapeutics

Neuromuscular diseases are biologically diverse, clinically heterogeneous, and often difficult to diagnose and treat, creating an urgent need for computational tools that can resolve overlapping phenotypes and enable timely, mechanism-based therapeutics. This narrative review synthesizes recent advances in artificial intelligence as applied to neuromuscular diseases, drawing from peer-reviewed literature from the past five years. Artificial intelligence can augment diagnosis by extracting disease-relevant patterns from imaging, electrophysiology, and multimodal clinical data, improving discrimination between clinically similar entities such as Duchenne and Becker muscular dystrophy. Artificial intelligence can also enhance early detection of amyotrophic lateral sclerosis. Artificial intelligence can utilize digital biomarkers of disease progression data from gait, voice, and wearable sensors for enhanced modeling of disease outcomes. Deep learning–based multi-omics integration, high-throughput phenotypic screening, and artificial intelligence-based protein structure predictive models are accelerating the path from causative mutation, to molecular mechanism, and on to candidate therapy. Despite these advances, significant challenges remain, including data scarcity, heterogeneous acquisition methods, limited external validation, and the need for interpretable models that can win clinician acceptance. Addressing these constraints is essential to moving high-performance research tools from the laboratory to the neuromuscular clinic. Artificial intelligence has the potential to shorten the diagnostic odyssey and accelerate the historically slow development of targeted therapeutics for rare neuromuscular diseases.