AI and Neurology

Background Artificial Intelligence is influencing medicine on all levels. Neurology, one of the most complex and progressive medical disciplines, is no exception. No longer limited to neuroimaging, where data-driven approaches were initiated, machine and deep learning methodologies are taking neurol...

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Vydáno v:Neurological research and practice Ročník 7; číslo 1; s. 11 - 9
Hlavní autoři: Bösel, Julian, Mathur, Rohan, Cheng, Lin, Varelas, Marianna S., Hobert, Markus A., Suarez, José I.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Heidelberg Springer Medizin 17.02.2025
Springer Nature B.V
BMC
Témata:
Artificial intelligence
Data-driven medicine
Decision making
Deep learning
Epilepsy
Ethics
Machine learning
Medicine
Medicine & Public Health
Movement disorders
Neural networks
Neuroimaging
Neurology
Neuropsychology
Neuroradiology
Neurosurgery
Review
Deep learning
Neurology
Neural networks
Machine learning
AI
Data-driven medicine
Artificial intelligence
ISSN:2524-3489, 2524-3489
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Popis
Shrnutí:Background Artificial Intelligence is influencing medicine on all levels. Neurology, one of the most complex and progressive medical disciplines, is no exception. No longer limited to neuroimaging, where data-driven approaches were initiated, machine and deep learning methodologies are taking neurologic diagnostics, prognostication, predictions, decision making and even therapy to very promising potentials. Main body In this review, the basic principles of different types of Artificial Intelligence and the options to apply them to neurology are summarized. Examples of noteworthy studies on such applications are presented from the fields of acute and intensive care neurology, stroke, epilepsy, and movement disorders. Finally, these potentials are matched with risks and challenges jeopardizing ethics, safety and equality, that need to be heeded by neurologists welcoming Artificial Intelligence to their field of expertise. Conclusion Artificial intelligence is and will be changing neurology. Studies need to be taken to the prospective level and algorithms undergo federated learning to reach generalizability. Neurologists need to master not only the benefits but also the risks in safety, ethics and equity of such data-driven form of medicine.
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ISSN:2524-3489
2524-3489
DOI:10.1186/s42466-025-00367-2