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These technologies are revolutionizing the way we utilize medical data, enabling improved disease classification, more precise diagnoses, better treatment selection, therapeutic monitoring, and highly accurate prognostication. Different ML and DL models have been used to distinguish between electromyography signals in normal individuals and those with amyotrophic lateral sclerosis and myopathy, with accuracy ranging from 67% to 99.5%. DL models have also been successfully applied in neuromuscular ultrasound, with the use of segmentation techniques achieving diagnostic accuracy of at least 90% for nerve entrapment disorders, and 87% for inflammatory myopathies. Other successful AI applications include prediction of treatment response, and prognostication including prediction of intensive care unit admissions for patients with myasthenia gravis. Despite these remarkable strides, significant knowledge, attitude, and practice gaps persist, including within the field of electrodiagnostic and neuromuscular medicine. In this narrative review, we highlight the fundamental principles of AI and draw parallels with the intricacies of human brain networks. Specifically, we explore the immense potential that AI holds for applications in electrodiagnostic studies, neuromuscular ultrasound, and other aspects of neuromuscular medicine. While there are exciting possibilities for the future, it is essential to acknowledge and understand the limitations of AI and take proactive steps to mitigate these challenges. This collective endeavor holds immense potential for the advancement of healthcare through the strategic and responsible integration of AI technologies.<\/jats:p>","DOI":"10.1002\/mus.28023","type":"journal-article","created":{"date-parts":[[2023,12,27]],"date-time":"2023-12-27T22:58:33Z","timestamp":1703717913000},"page":"260-272","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["The role of artificial intelligence in electrodiagnostic and neuromuscular medicine:\n                    <scp>Current<\/scp>\n                    state and future directions"],"prefix":"10.1002","volume":"69","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8629-5066","authenticated-orcid":false,"given":"Mohamed A.","family":"Taha","sequence":"first","affiliation":[{"name":"Neuromuscular Center, Neurological Institute Cleveland Clinic Cleveland Ohio USA"},{"name":"Department of Neurology Massachusetts General Hospital Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9738-9547","authenticated-orcid":false,"given":"John A.","family":"Morren","sequence":"additional","affiliation":[{"name":"Neuromuscular Center, Neurological Institute Cleveland Clinic Cleveland Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,12,27]]},"reference":[{"key":"e_1_2_20_2_1","doi-asserted-by":"publisher","DOI":"10.1177\/0008125619864925"},{"key":"e_1_2_20_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/JRPROC.1960.287598"},{"key":"e_1_2_20_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02478259"},{"key":"e_1_2_20_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/4284.4286"},{"key":"e_1_2_20_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12525-021-00475-2"},{"key":"e_1_2_20_7_1","doi-asserted-by":"crossref","unstructured":"OngsuleeP.Artificial intelligence machine learning and deep learning. 2017 15th International Conference on ICT and Knowledge Engineering (ICT&KE).2017:1\u20136.","DOI":"10.1109\/ICTKE.2017.8259629"},{"key":"e_1_2_20_8_1","volume-title":"Deep learning","author":"Goodfellow I","year":"2016"},{"key":"e_1_2_20_9_1","volume-title":"Supervised and Unsupervised Learning for Data Science","author":"Berry MW","year":"2019"},{"key":"e_1_2_20_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/2.485891"},{"key":"e_1_2_20_11_1","doi-asserted-by":"publisher","DOI":"10.5772\/51275"},{"key":"e_1_2_20_12_1","doi-asserted-by":"publisher","DOI":"10.4103\/0253-7613.169598"},{"key":"e_1_2_20_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02478291"},{"key":"e_1_2_20_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.10.013"},{"key":"e_1_2_20_15_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco_a_01199"},{"key":"e_1_2_20_16_1","article-title":"Attention is all you need","volume":"30","author":"Vaswani A","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"e_1_2_20_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2765202"},{"key":"e_1_2_20_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3261988"},{"key":"e_1_2_20_19_1","doi-asserted-by":"crossref","unstructured":"XueN TrigueroI FigueredoGP Landa\u2010SilvaD.Evolving deep CNN\u2010LSTMs for inventory time series prediction. 2019 IEEE Congress on Evolutionary Computation (CEC).2019:1517\u20131524.","DOI":"10.1109\/CEC.2019.8789957"},{"key":"e_1_2_20_20_1","doi-asserted-by":"crossref","unstructured":"JinX YuX WangX et al.Prediction for time series with CNN and LSTM. 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