Artificial intelligence in clinical neurophysiology: IFCN handbook chapter.

Abstract

Artificial intelligence (AI) has the potential to transform clinical neurophysiology by enabling the automated analysis of complex physiological signals and large amounts of data. While traditional clinical interpretation relies heavily on expert visual inspection, which is vulnerable to subjectivity and inter-rater variability, AI models excel at identifying patterns in complex time series. This chapter provides an overview of current and emerging clinical applications of AI across various modalities in clinical neurophysiology, including EEG, EMG, NCS, and ultrasound. In EEG, AI demonstrates value in spike and seizure detection, comprehensive interpretation for routine and long-term monitoring, and automated classification in ICU and neonatal settings. In neuromuscular medicine, machine learning and deep learning are used to classify motor unit potentials, recognize specific disease patterns in NCS, and aid in automated segmentation and quantification for nerve and muscle ultrasound. Despite these promising results, multiple barriers must be overcome to reach clinical implementation. Key challenges include data heterogeneity, label noise, limited generalizability, and the lack of explainability in "black-box" deep learning approaches. Furthermore, regulatory and legal challenges, along with ethical concerns like algorithmic bias and clinical accountability, complicate widespread adoption. The use of large international datasets combined with robust validation approaches may enable the development of AI models that reduce diagnostic subjectivity. Ultimately, AI will likely serve as an augmentation and triage tool, allowing clinical neurophysiologists to focus on clinical reasoning while also supporting the training of future residents.