Academic Journal
Artificial intelligence in clinical neurophysiology: IFCN handbook chapter.
| Τίτλος: | Artificial intelligence in clinical neurophysiology: IFCN handbook chapter. |
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| Συγγραφείς: | Tannemaat MR; Department of Neurology, Leiden University Medical Centre, Leiden, the Netherlands. Electronic address: m.r.tannemaat@lumc.nl., Luijten JAM; Department of Neurology, Leiden University Medical Centre, Leiden, the Netherlands; Department of Neurology, Amsterdam University Medical Centre, Amsterdam, the Netherlands., Frauscher B; Duke Comprehensive Epilepsy Center, Duke University Medical Center & Department of Biomedical Engineering, Duke Pratt School of Engineering, Durham, NC, USA., Alvarado-Rojas C; Department of Electronics Engineering, School of Engineering. Pontificia Universidad Javeriana. Bogota, Colombia., Aurlien H; Department of Clinical Neurophysiology, Haukeland University Hospital, Bergen, Norway; Natus Medical, Bergen, Norway., Nilsen KB; Institute of Clinical Medicine, University of Oslo and Section for Clinical Neurophysiology, Department of Neurology, Oslo University Hospital, Oslo, Norway., van Alfen N; Department of Neurology, Clinical Neuromuscular Imaging Group, Radboudumc Research Institute for Medical Innovation, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands., Kwan P; Department of Neuroscience, School of Translational Medicine, Faculty of Medicine Nursing and Health Sciences, Monash University, Victoria, Australia; Department of Neurology, The Alfred Hospital, Victoria, Australia., Beniczky S; Danish Epilepsy Centre, Dianalund, Copenhagen University Hospital Rigshospitalet and University of Copenhagen, Denmark. |
| Πηγή: | Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology [Clin Neurophysiol] 2026 Dec; Vol. 192, pp. 2112407. Date of Electronic Publication: 2026 Sep 20. |
| Τύπος έκδοσης: | Journal Article; Review |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Elsevier Country of Publication: Netherlands NLM ID: 100883319 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-8952 (Electronic) Linking ISSN: 13882457 NLM ISO Abbreviation: Clin Neurophysiol Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Amsterdam : Elsevier, c1999- |
| Ιατρικοί όροι (MeSH): | Artificial Intelligence*/trends , Neurophysiology*/methods , Neurophysiology*/trends, Electroencephalography/methods ; Electromyography/methods ; Humans ; Soft Computing |
| Περίληψη: | 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. (Copyright © 2026. Published by Elsevier B.V.) |
| Competing Interests: | Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: MT M.R. Tannemaat has been paid for consultancies for ArgenX, UCB Pharma, Johnson and Johnson, Peervoice and Medtalks, and has received research funding from ZonMW, NWO, ArgenX, UCB Pharma, NMD Pharma and Huma. The NWO-funded project ARISE-NMD (ARtificial Intelligence for the analySis of Electromyography for precision diagnosis of NeuroMuscular Diseases) received in-cash and in-kind co-funding from Cadwell. All reimbursements were received by Leiden University Medical Center. JL reports no relevant disclosures. BF Birgit Frauscher has received personal fees as speaker or on serving on advisory boards from Jazz and Natus. She is Deputy Editor of Epileptic Disorders, Associate Editor of Journal of Clinical Neurophysiology and editorial board member of Annals of Neurology, Clinical Journal of Sleep Medicine, and Sleep Medicine. Finally, her lab received funding from the American Academy of Sleep Medicine, American Epilepsy Society, Canadian Institutes of Health Research, Eisai, and start-up funding of Duke University. These activities were partially related to the content of this manuscript. CA reports no relevant disclosures. HA is Senior Director, Clinical Research at Natus Medical. KN reports no relevant disclosures. NA works as an ultrasound instructor for Sonoskills and performs editorial services for Wiley Publishing; all payment goes to their employer. PK reports no relevant disclosures. SB reports no relevant disclosures. |
| Entry Date(s): | Date Created: 20260924 Date Completed: 20261006 Latest Revision: 20261006 |
| Update Code: | 20261007 |
| DOI: | 10.1016/j.clinph.2026.2112407 |
| PMID: | 42785050 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1872-8952 |
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| DOI: | 10.1016/j.clinph.2026.2112407 |