Artificial Intelligence As an Emerging Technology in the Current Care of Neurological Disorders

Overview

Journal J Neurol

Specialty Neurology

Date 2019 Aug 28

PMID 31451912

Citations 45

Authors

Urvish K Patel

Arsalan Anwar

Sidra Saleem

Preeti Malik

Bakhtiar Rasul

Karan Patel

Robert Yao

Ashok Seshadri

Mohammed Yousufuddin

Kogulavadanan Arumaithurai

Affiliations

Soon will be listed here.

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.

Citing Articles

Artificial Intelligence for Neuroimaging in Pediatric Cancer.

Dalboni da Rocha J, Lai J, Pandey P, Myat P, Loschinskey Z, Bag A Cancers (Basel). 2025; 17(4).

PMID: 40002217 PMC: 11852968. DOI: 10.3390/cancers17040622.

The Role of Neuroinflammation and Network Anomalies in Drug-Resistant Epilepsy.

Shi J, Xie J, Li Z, He X, Wei P, Sander J Neurosci Bull. 2025; .

PMID: 39992353 DOI: 10.1007/s12264-025-01348-w.

AI and Neurology.

Bosel J, Mathur R, Cheng L, Varelas M, Hobert M, Suarez J Neurol Res Pract. 2025; 7(1):11.

PMID: 39956906 DOI: 10.1186/s42466-025-00367-2.

Telemedicine for Personalized Nutritional Intervention of Rare Diseases: A Narrative Review on Approaches, Impact, and Future Perspectives.

Eletti F, Tagi V, Greco I, Stucchi E, Fiore G, Bonaventura E Nutrients. 2025; 17(3).

PMID: 39940313 PMC: 11820740. DOI: 10.3390/nu17030455.

The Perception and Attitude of the Use of Robotics Among Medical Students, Rehabilitation Students, Including OT and PT Students and Specialists, and Healthcare Faculty Members.

Aljefiri M, Alhabsh M, Alabbasi M, Aljehani S, Somaa F J Pharm Bioallied Sci. 2025; 16(Suppl 4):S3242-S3244.

PMID: 39926992 PMC: 11805331. DOI: 10.4103/jpbs.jpbs_743_24.

References

Henricks W . "Meaningful use" of electronic health records and its relevance to laboratories and pathologists. J Pathol Inform. 2011; 2:7. PMC: 3049251. DOI: 10.4103/2153-3539.76733. View

Murray C, Vos T, Lozano R, Naghavi M, Flaxman A, Michaud C . Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012; 380(9859):2197-223. DOI: 10.1016/S0140-6736(12)61689-4. View

Gooch C, Pracht E, Borenstein A . The burden of neurological disease in the United States: A summary report and call to action. Ann Neurol. 2017; 81(4):479-484. DOI: 10.1002/ana.24897. View

Onorati F, Regalia G, Caborni C, Migliorini M, Bender D, Poh M . Multicenter clinical assessment of improved wearable multimodal convulsive seizure detectors. Epilepsia. 2017; 58(11):1870-1879. DOI: 10.1111/epi.13899. View

Wahl B, Cossy-Gantner A, Germann S, Schwalbe N . Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings?. BMJ Glob Health. 2018; 3(4):e000798. PMC: 6135465. DOI: 10.1136/bmjgh-2018-000798. View

Hassabis D, Kumaran D, Summerfield C, Botvinick M . Neuroscience-Inspired Artificial Intelligence. Neuron. 2017; 95(2):245-258. DOI: 10.1016/j.neuron.2017.06.011. View

Ganapathy K, Syed Abdul S, Nursetyo A . Artificial intelligence in neurosciences: A clinician's perspective. Neurol India. 2018; 66(4):934-939. DOI: 10.4103/0028-3886.236971. View

Varghese J, Niewohner S, Soto-Rey I, Schipmann-Miletic S, Warneke N, Warnecke T . A Smart Device System to Identify New Phenotypical Characteristics in Movement Disorders. Front Neurol. 2019; 10:48. PMC: 6363699. DOI: 10.3389/fneur.2019.00048. View

Li K, White F, Tipoe T, Liu T, Wong M, Jesuthasan A . The Current State of Mobile Phone Apps for Monitoring Heart Rate, Heart Rate Variability, and Atrial Fibrillation: Narrative Review. JMIR Mhealth Uhealth. 2019; 7(2):e11606. PMC: 6396075. DOI: 10.2196/11606. View

10.

Wu N, Phang J, Park J, Shen Y, Huang Z, Zorin M . Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening. IEEE Trans Med Imaging. 2019; 39(4):1184-1194. PMC: 7427471. DOI: 10.1109/TMI.2019.2945514. View

11.

Labovitz D, Shafner L, Reyes Gil M, Virmani D, Hanina A . Using Artificial Intelligence to Reduce the Risk of Nonadherence in Patients on Anticoagulation Therapy. Stroke. 2017; 48(5):1416-1419. PMC: 5432369. DOI: 10.1161/STROKEAHA.116.016281. View

12.

Okazaki E, Yao R, Sirven J, Crepeau A, Noe K, Drazkowski J . Usage of EpiFinder clinical decision support in the assessment of epilepsy. Epilepsy Behav. 2018; 82:140-143. DOI: 10.1016/j.yebeh.2018.03.018. View

13.

Sarica A, Cerasa A, Quattrone A . Random Forest Algorithm for the Classification of Neuroimaging Data in Alzheimer's Disease: A Systematic Review. Front Aging Neurosci. 2017; 9:329. PMC: 5635046. DOI: 10.3389/fnagi.2017.00329. View

14.

Rathore S, Habes M, Iftikhar M, Shacklett A, Davatzikos C . A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages. Neuroimage. 2017; 155():530-548. PMC: 5511557. DOI: 10.1016/j.neuroimage.2017.03.057. View

15.

Salvatore C, Cerasa A, Castiglioni I, Gallivanone F, Augimeri A, Lopez M . Machine learning on brain MRI data for differential diagnosis of Parkinson's disease and Progressive Supranuclear Palsy. J Neurosci Methods. 2013; 222:230-7. DOI: 10.1016/j.jneumeth.2013.11.016. View

16.

Amoroso N, La Rocca M, Monaco A, Bellotti R, Tangaro S . Complex networks reveal early MRI markers of Parkinson's disease. Med Image Anal. 2018; 48:12-24. DOI: 10.1016/j.media.2018.05.004. View

17.

Visscher R, Feddermann-Demont N, Romano F, Straumann D, Bertolini G . Artificial intelligence for understanding concussion: Retrospective cluster analysis on the balance and vestibular diagnostic data of concussion patients. PLoS One. 2019; 14(4):e0214525. PMC: 6445465. DOI: 10.1371/journal.pone.0214525. View

18.

Benjamin E, Virani S, Callaway C, Chamberlain A, Chang A, Cheng S . Heart Disease and Stroke Statistics-2018 Update: A Report From the American Heart Association. Circulation. 2018; 137(12):e67-e492. DOI: 10.1161/CIR.0000000000000558. View

19.

Al Kasab S, Adams R, Debenham E, Jones D, Holmstedt C . Medical University of South Carolina Telestroke: A Telemedicine Facilitated Network for Stroke Treatment in South Carolina-A Progress Report. Telemed J E Health. 2017; 23(8):674-677. DOI: 10.1089/tmj.2016.0229. View

20.

Zhai Y, Zhu W, Hou H, Sun D, Zhao J . Efficacy of telemedicine for thrombolytic therapy in acute ischemic stroke: a meta-analysis. J Telemed Telecare. 2015; 21(3):123-30. DOI: 10.1177/1357633X15571357. View