Invited Review: The role of artificial intelligence in electrodiagnostic and neuromuscular medicine: Current state and future directions-Article

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The rapid advancements in artificial intelligence (AI) and machine learning (ML) have had a significant impact on electrodiagnostic and neuromuscular medicine. These technologies have improved disease classification, diagnosis, treatment selection, therapeutic monitoring, and prognosis in patients. ML and deep learning (DL) models have been used to accurately distinguish between normal individuals and those with conditions like amyotrophic lateral sclerosis and myopathy. DL models have also achieved high diagnostic accuracy in neuromuscular ultrasound (NMUS) for nerve entrapment disorders and inflammatory myopathies. AI has also been used for predicting treatment response and prognostication, such as intensive care unit admissions for myasthenia gravis patients. However, there are knowledge gaps and limitations in the field that need to be addressed, such as the reliance on retrospective data and the lack of standardized assessment and regulation of AI models. Efforts are needed to establish comprehensive frameworks for AI development and utilization in order to ensure safe and effective use. Despite these challenges, the integration of AI holds immense potential for improving healthcare outcomes by combining the expertise of clinicians with the accuracy of AI algorithms. Continued collaboration and proactive efforts are necessary to fully harness the potential of AI in electrodiagnostic and neuromuscular medicine.

Keywords

artificial intelligence

machine learning

electrodiagnostic

neuromuscular medicine

disease classification

diagnosis

treatment selection

prognosis

deep learning

neuromuscular ultrasound