Klinische Neurophysiologie, Table of Contents Klinische Neurophysiologie 2024; 55(04): 256-258DOI: 10.1055/a-2346-5352 FNTA | Fortbildung Künstliche Intelligenz in der Neurophysiologie Michael Dietrich Recommend Article Abstract Buy Article Full Text References Literatur 1 Liu X, Faes L, Kale AU. et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit Health 2019; 1: e271-e297 2 Ernst K, Rémi J. Künstliche Intelligenz in der Neurologie. Nervenheilkunde 2023; 42: 603-611 3 da Silva Lourenço C, Tjepkema-Cloostermans MC, van Putten MJAM. Ultrafast review of ambulatory EEGs with deep learning. Clinical Neurophysiology 2023; 154: 43-48 4 Kural MA, Jing J, Fürbass F. et al. Accurate identification of EEG recordings with interictal epileptiform discharges using a hybrid approach: Artificial intelligence supervised by human experts. Epilepsia 2022; 63: 1064-1073 5 Tveit J, Aurlien H, Plis S. et al. Automated Interpretation of Clinical Electroencephalograms Using Artificial Intelligence. JAMA Neurol 2023; 80: 805 6 Taha MA, Morren JA. The role of artificial intelligence in electrodiagnostic and neuromuscular medicine: Current state and future directions. Muscle Nerve 2023; 69: 260-272 7 Kendall R, Werner RA. Interrater reliability of the needle examination in lumbosacral radiculopathy. Muscle Nerve 2006; 34: 238-241 8 Tannemaat MR, Kefalas M, Geraedts VJ. et al. Distinguishing normal, neuropathic and myopathic EMG with an automated machine learning approach. Clin Neurophysiol 2023; 146: 49-54 9 Hubers D, Potters W, Paalvast O. et al. Artificial intelligence-based classification of motor unit action potentials in real-world needle EMG recordings. Clin Neurophysiol 2023; 156: 220-227 10 de Jonge S, Potters WV, Verhamme C. Artificial intelligence for automatic classification of needle EMG signals: A scoping review. Clin Neurophysiol 2024; 159: 41-55 11 Haque F, Reaz MBI, Chowdhury MEH. et al. Performance Analysis of Conventional Machine Learning Algorithms for Diabetic Sensorimotor Polyneuropathy Severity Classification Using Nerve Conduction Studies. Comput Intell Neurosci 2022 2022; 1-13 12 Porr B, Daryanavard S, Bohollo LM. et al. Real-time noise cancellation with deep learning. PLoS One 2022; 17: e0277974 13 Computers make mistakes and AI will make things worse — the law must recognize that. Nature 2024; 625: 631-631
