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 d...
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Published in | Journal of neurology Vol. 268; no. 5; pp. 1623 - 1642 |
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Main Authors | , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.05.2021
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
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Abstract | 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. |
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AbstractList | 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.
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.
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. BackgroundArtificial 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.DiscussionThe 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.ConclusionAI 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. 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. |
Author | Seshadri, Ashok Saleem, Sidra Rasul, Bakhtiar Patel, Karan Patel, Urvish K. Malik, Preeti Yao, Robert Yousufuddin, Mohammed Arumaithurai, Kogulavadanan Anwar, Arsalan |
Author_xml | – sequence: 1 givenname: Urvish K. orcidid: 0000-0002-6702-298X surname: Patel fullname: Patel, Urvish K. email: dr.urvish.patel@gmail.com organization: Department of Neurology and Public Health, Icahn School of Medicine at Mount Sinai – sequence: 2 givenname: Arsalan surname: Anwar fullname: Anwar, Arsalan organization: Department of Neurology, UH Cleveland Medical Center – sequence: 3 givenname: Sidra surname: Saleem fullname: Saleem, Sidra organization: Department of Neurology, University of Toledo – sequence: 4 givenname: Preeti surname: Malik fullname: Malik, Preeti organization: Department of Public Health, Icahn School of Medicine at Mount Sinai – sequence: 5 givenname: Bakhtiar surname: Rasul fullname: Rasul, Bakhtiar organization: Department of Public Health, Icahn School of Medicine at Mount Sinai – sequence: 6 givenname: Karan surname: Patel fullname: Patel, Karan organization: Department of Neuroscience, Johns Hopkins University – sequence: 7 givenname: Robert surname: Yao fullname: Yao, Robert organization: Department of Biomedical Informatics, Arizona State University and Mayo Clinic Arizona – sequence: 8 givenname: Ashok surname: Seshadri fullname: Seshadri, Ashok organization: Department of Psychiatry, Mayo Clinic Health System – sequence: 9 givenname: Mohammed surname: Yousufuddin fullname: Yousufuddin, Mohammed organization: Department of Internal Medicine, Mayo Clinic Health System – sequence: 10 givenname: Kogulavadanan surname: Arumaithurai fullname: Arumaithurai, Kogulavadanan organization: Department of Neurology, Mayo Clinic Health System |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31451912$$D View this record in MEDLINE/PubMed |
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Copyright | Springer-Verlag GmbH Germany, part of Springer Nature 2019 Journal of Neurology is a copyright of Springer, (2019). All Rights Reserved. Springer-Verlag GmbH Germany, part of Springer Nature 2019. |
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Keywords | Deep learning Stroke SUDEP Concussion Alzheimer’s disease Technology Epilepsy Machine learning Artificial intelligence Neurological disorders Movement disorders |
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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... 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... BackgroundArtificial intelligence (AI) has influenced all aspects of human life and neurology is no exception to this growing trend. The aim of this paper is... BACKGROUNDArtificial intelligence (AI) has influenced all aspects of human life and neurology is no exception to this growing trend. The aim of this paper is... |
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SubjectTerms | Artificial intelligence Autonomic nervous system Cognitive ability Concussion Convulsions & seizures Deep brain stimulation Deep learning Dementia disorders EEG Epilepsy Fibrillation Learning algorithms Machine learning Medicine Medicine & Public Health Neuroimaging Neurological diseases Neurological disorders Neurology Neuroradiology Neurosciences Patients Phenotypes Prognosis Review Seizures Surgery |
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Title | Artificial intelligence as an emerging technology in the current care of neurological disorders |
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