Artificial intelligence as an emerging technology in the current care of neurological disorders
BACKGROUND: Artificial intelligence (AI) has influenced all aspects of human life and neurology is no exception to this growing trend. The aim of this paper is to guide medical practitioners on the relevant aspects of artificial intelligence, i.e., machine learning, and deep learning, to review the development of technological advancement equipped with AI, and to elucidate how machine learning can revolutionize the management of neurological diseases. This review focuses on unsupervised aspects of machine learning, and how these aspects could be applied to precision neurology to improve patient outcomes. We have mentioned various forms of available AI, prior research, outcomes, benefits and limitations of AI, effective accessibility and future of AI, keeping the current burden of neurological disorders in mind.
DISCUSSION: The smart device system to monitor tremors and to recognize its phenotypes for better outcomes of deep brain stimulation, applications evaluating fine motor functions, AI integrated electroencephalogram learning to diagnose epilepsy and psychological non-epileptic seizure, predict outcome of seizure surgeries, recognize patterns of autonomic instability to prevent sudden unexpected death in epilepsy (SUDEP), identify the pattern of complex algorithm in neuroimaging classifying cognitive impairment, differentiating and classifying concussion phenotypes, smartwatches monitoring atrial fibrillation to prevent strokes, and prediction of prognosis in dementia are unique examples of experimental utilizations of AI in the field of neurology. Though there are obvious limitations of AI, the general consensus among several nationwide studies is that this new technology has the ability to improve the prognosis of neurological disorders and as a result should become a staple in the medical community.
CONCLUSION: AI not only helps to analyze medical data in disease prevention, diagnosis, patient monitoring, and development of new protocols, but can also assist clinicians in dealing with voluminous data in a more accurate and efficient manner.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:268 |
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| Enthalten in: |
Journal of neurology - 268(2021), 5 vom: 18. Mai, Seite 1623-1642 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Patel, Urvish K [VerfasserIn] |
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| Links: |
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| Themen: |
Alzheimer’s disease |
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| Anmerkungen: |
Date Completed 18.06.2021 Date Revised 18.06.2021 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1007/s00415-019-09518-3 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM300615205 |
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a BACKGROUND: Artificial intelligence (AI) has influenced all aspects of human life and neurology is no exception to this growing trend. The aim of this paper is to guide medical practitioners on the relevant aspects of artificial intelligence, i.e., machine learning, and deep learning, to review the development of technological advancement equipped with AI, and to elucidate how machine learning can revolutionize the management of neurological diseases. This review focuses on unsupervised aspects of machine learning, and how these aspects could be applied to precision neurology to improve patient outcomes. We have mentioned various forms of available AI, prior research, outcomes, benefits and limitations of AI, effective accessibility and future of AI, keeping the current burden of neurological disorders in mind | ||
| 520 | |a DISCUSSION: The smart device system to monitor tremors and to recognize its phenotypes for better outcomes of deep brain stimulation, applications evaluating fine motor functions, AI integrated electroencephalogram learning to diagnose epilepsy and psychological non-epileptic seizure, predict outcome of seizure surgeries, recognize patterns of autonomic instability to prevent sudden unexpected death in epilepsy (SUDEP), identify the pattern of complex algorithm in neuroimaging classifying cognitive impairment, differentiating and classifying concussion phenotypes, smartwatches monitoring atrial fibrillation to prevent strokes, and prediction of prognosis in dementia are unique examples of experimental utilizations of AI in the field of neurology. Though there are obvious limitations of AI, the general consensus among several nationwide studies is that this new technology has the ability to improve the prognosis of neurological disorders and as a result should become a staple in the medical community | ||
| 520 | |a CONCLUSION: AI not only helps to analyze medical data in disease prevention, diagnosis, patient monitoring, and development of new protocols, but can also assist clinicians in dealing with voluminous data in a more accurate and efficient manner | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Review | |
| 650 | 4 | |a Alzheimer’s disease | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Concussion | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Epilepsy | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Movement disorders | |
| 650 | 4 | |a Neurological disorders | |
| 650 | 4 | |a SUDEP | |
| 650 | 4 | |a Stroke | |
| 650 | 4 | |a Technology | |
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| 700 | 1 | |a Saleem, Sidra |e verfasserin |4 aut | |
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| 700 | 1 | |a Rasul, Bakhtiar |e verfasserin |4 aut | |
| 700 | 1 | |a Patel, Karan |e verfasserin |4 aut | |
| 700 | 1 | |a Yao, Robert |e verfasserin |4 aut | |
| 700 | 1 | |a Seshadri, Ashok |e verfasserin |4 aut | |
| 700 | 1 | |a Yousufuddin, Mohammed |e verfasserin |4 aut | |
| 700 | 1 | |a Arumaithurai, Kogulavadanan |e verfasserin |4 aut | |
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