Neuromuscular diseases are biologically diverse, clinically heterogeneous, and often difficult to diagnose and treat, highlighting the need for computational tools that can help resolve overlapping phenotypes and support timely, mechanism-informed interventions. This narrative review synthesizes recent advances in artificial intelligence (AI) and machine learning applied to neuromuscular diseases across diagnosis, outcome modeling, biomarker development, and therapeutics. AI-based approaches may assist clinical and genetic diagnosis from phenotypic data; however, early phenotype-driven tools have seen limited clinician adoption due to modest accuracy, usability challenges, and poor workflow integration. Electrophysiological studies remain central to diagnosing neuromuscular diseases, and AI shows promise for accurate classification of electrophysiological signals. Predictive models for disease outcome and progression—particularly in amyotrophic lateral sclerosis—are under active investigation, but most remain at an early stage of development and are not yet ready for routine clinical use. Digital biomarkers derived from imaging, gait, voice, and wearable sensors are emerging, with MRI-based quantification of muscle fat replacement representing the most mature and widely accepted application to date. Efforts to apply AI to therapeutic discovery, including drug repurposing and optimization of gene-based therapies, are ongoing but have thus far yielded limited clinical translation. Persistent barriers to broader adoption include disease rarity, data scarcity, heterogeneous acquisition protocols, inconsistent terminology, limited external validation, insufficient model explainability, and lack of seamless integration into clinical workflows. Addressing these challenges is essential to moving AI tools from the laboratory into clinical practice. We conclude with a practical checklist of considerations intended to guide the development and adoption of AI tools in neuromuscular disease care.
Wunsch et al. (Tue,) studied this question.