Beyond Biomarkers: Reclaiming the Central Role of Clinical Neurophysiology in Neuromuscular Disorders

Abstract

Highlights What are the main findings? Biological biomarkers and clinical neurophysiology provide complementary information on disease mechanisms and their functional expression. Across major neuromuscular disorders, neurophysiological assessment characterizes dynamic functional processes that may not be captured by biological or structural biomarkers alone. What are the implications of the main findings? Integrating neurophysiological, molecular, imaging, and quantitative approaches may improve diagnosis, longitudinal assessment, and therapeutic evaluation. Artificial intelligence may enhance quantitative signal analysis, but its clinical implementation requires rigorous validation, physiological interpretation, and expert oversight. Abstract Over the past two decades, advances in molecular genetics, immunology, neuroimaging, fluid biomarkers, and artificial intelligence have substantially transformed the diagnosis, classification, and treatment of neuromuscular disorders. However, increasing biological precision has also highlighted an important distinction between identifying disease mechanisms and characterizing their functional expression. Clinical neurophysiology occupies a specific position within this evolving diagnostic landscape by directly assessing nervous system function and providing objective information on impulse conduction, neuromuscular transmission, motor unit integrity, physiological reserve, and disease activity. Consequently, neurophysiological assessment contributes not only to diagnosis but also to differential diagnosis, longitudinal monitoring, prognostic assessment, and therapeutic evaluation. In this critical review, I examine the factors that have progressively shifted clinical neurophysiology from an interpretative clinical discipline toward a more procedural role. Using representative examples from myasthenia gravis, immune-mediated neuropathies, myopathies, and amyotrophic lateral sclerosis, I discuss how physiological information complements molecular, serological, imaging, and other biological biomarkers. I further examine the emerging role of artificial intelligence, including its potential for automated signal analysis and quantitative assessment, as well as current limitations related to generalizability, validation, and overreliance on pattern recognition. I propose a complementary conceptual framework in which biological and molecular approaches characterize disease mechanisms, while clinical neurophysiology defines their functional expression and clinical relevance in the individual patient. Within this framework, clinical neurophysiology should not be considered an alternative to precision medicine, but an integrated physiological dimension of it, connecting biological characterization with clinically actionable functional information.