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Invited Review: The role of artificial intelligence in electrodiagnostic and neuromuscular medicine: Current state and future directions
Invited Review: The role of artificial intelligence in electrodiagnostic and neuromuscular medicine: Current state and future directions
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Abstract
The rapid advancements in artificial intelligence (AI), including machine learning (ML), and deep learning (DL) have ushered in a new era of technological breakthroughs in healthcare. 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.

Objectives: The objectives of this activity are to enable the reader to: 1) Understand how machine learning and deep learning can be used to facilitate clinically useful interpretation of electrodiagnostic medicine recordings; 2) Understand how machine learning and deep learning can be used to facilitate clinically useful interpretation of signals from neuromuscular ultrasound; 3) Understand the ethical implications of artificial intelligence in the practice of neuromuscular medicine and incorporate these into the use of AI platforms. 


ACCREDITATION STATEMENT
The AANEM is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.


CREDIT DESIGNATION
The AANEM is accredited by the American Council for Continuing Medical Education (ACCME) to providing continuing education for physicians. AANEM designates this Journal-based CME activity for a maximum of 1.0 AMA PRA Category 1 Credit™. Physicians should claim only the credit commensurate with the extent of their participation in the activity. Credit expires 1/4/2027.

DISCLOSURE INFORMATION
The authors had no financial conflicts of interest.


FORMAT
PDF
Authors
Mohamed A. Taha MD, MSc, MSc and John A. Morren MD
Summary
Availability: On-Demand
Expires on Jan 04, 2027
Cost: Member: $0.00
Non-Member: $25.00
Credit Offered:
1 CME Credit
1 CEU Credit


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