The Role of AI in the Management of Movement Disorders

Abstract

Artificial intelligence (AI) has emerged as a transformative force in the management of movement disorders. This review explores the various applications of AI across the spectrum of care, from diagnosis to clinical workflows, treatment, and monitoring. Recent advancements include deep phenotyping tools like the Next Move in Movement Disorders (NEMO) project for hyperkinetic disorders, diagnostic platforms such as DystoniaNet, and biomarker identification systems for early Parkinson's disease detection. AI may revolutionize treatment selection through technologies like DystoniaBoTXNet and adaptive deep brain stimulation systems. For symptom monitoring, innovations like the Emerald device and smartphone-based assessment tools enable continuous, objective evaluation. AI may also enhance patient care through improved telemedicine capabilities and ambient listening. Despite these promising developments, recent critiques highlight methodological concerns in AI research, emphasizing the need for rigorous validation and transparency. The future of AI in movement disorders requires balancing technological innovation with clinical expertise to improve patient outcomes.

Keywords

movement disorders - machine learning - parkinsonism - hyperkinesia - wireless monitoring

Publication History

Accepted Manuscript online:
29 April 2025

Article published online:
26 May 2025

• References

• 1 Dorsey ER, Omberg L, Waddell E. et al. Deep phenotyping of Parkinson's disease. J Parkinsons Dis 2020; 10 (03) 855-873
• 2 Vizcarra JA, Yarlagadda S, Xie K, Ellis CA, Spindler M, Hammer LH. Artificial intelligence in the diagnosis and quantitative phenotyping of hyperkinetic movement disorders: a systematic review. J Clin Med 2024; 13 (23) 7009
• 3 Dorsey ER, Sherer T, Okun MS, Bloem BR. The emerging evidence of the Parkinson pandemic. J Parkinsons Dis 2018; 8 (s1): S3-S8
• 4 van der Stouwe AMM, Tuitert I, Giotis I. et al. Next move in movement disorders (NEMO): developing a computer-aided classification tool for hyperkinetic movement disorders. BMJ Open 2021; 11 (10) e055068
• 5 Dalenberg JR, Peretti DE, Marapin LR, van der Stouwe AMM, Renken RJ, Tijssen MAJ. Next move in movement disorders: neuroimaging protocols for hyperkinetic movement disorders. Front Hum Neurosci 2024; 18: 1406786
• 6 Valeriani D, Simonyan K. A microstructural neural network biomarker for dystonia diagnosis identified by a DystoniaNet deep learning platform. Proc Natl Acad Sci U S A 2020; 117 (42) 26398-26405
• 7 Yao D, O'Flynn LC, Simonyan K. DystoniaBoTXNet: novel neural network biomarker of botulinum toxin efficacy in isolated dystonia. Ann Neurol 2023; 93 (03) 460-471
• 8 Simonyan K, Yao D. DystoniaDBSNet: a novel deep learning biomarker of predictive treatment outcomes in dystonia. Brain Stimul 2025; 18 (01) 274
• 9 Junker J, Hall J, Berman BD. et al; Dystonia Coalition Study Group. Longitudinal predictors of health-related quality of life in isolated dystonia. J Neurol 2024; 271 (02) 852-863
• 10 Albanese A, Bhatia K, Bressman SB. et al. Phenomenology and classification of dystonia: a consensus update. Mov Disord 2013; 28 (07) 863-873
• 11 Friedrich MU, Relton S, Wong D, Alty J. Computer vision in clinical neurology: a review. JAMA Neurol 2025; . Epub ahead of print
• 12 Vaillancourt DE, Barmpoutis A, Wu SS. et al; AIDP Study Group. Automated imaging differentiation for Parkinsonism. JAMA Neurol 2025; e250112 . Epub ahead of print
• 13 Yang Y, Yuan Y, Zhang G. et al. Artificial intelligence-enabled detection and assessment of Parkinson's disease using nocturnal breathing signals. Nat Med 2022; 28 (10) 2207-2215
• 14 Hällqvist J, Bartl M, Dakna M. et al. Plasma proteomics identify biomarkers predicting Parkinson's disease up to 7 years before symptom onset. Nat Commun 2024; 15 (01) 4759
• 15 Makarious MB, Leonard HL, Vitale D. et al. Multi-modality machine learning predicting Parkinson's disease. NPJ Parkinsons Dis 2022; 8 (01) 35
• 16 Deik A. Potential benefits and perils of incorporating ChatGPT to the movement disorders clinic. J Mov Disord 2023; 16 (02) 158-162
• 17 Mohajer B. Faster, more practical, but still accurate: deep learning for diagnosis of progressive supranuclear palsy. Radiol Artif Intell 2024; 6 (03) e240181
• 18 Zampatti S, Farro J, Peconi C. et al. AI-powered neurogenetics: supporting patient's evaluation with Chatbot. Genes (Basel) 2024; 16 (01) 29
• 19 Simuni T, Chahine LM, Poston K. et al. A biological definition of neuronal α-synuclein disease: towards an integrated staging system for research. Lancet Neurol 2024; 23 (02) 178-190
• 20 Dietiker C, Tanner C. Evolving perspectives on α-synuclein testing. JAMA Neurol 2025; . Epub ahead of print
• 21 Gibbons C, Bellaire B, Levine T, Freeman R. A novel diagnostic method for detection and quantitation of cutaneous phosphorylated alpha-synuclein (S26.004). Neurology 2024; 102 (7, supplement 1)
• 22 Howey KD, Li M, Christenson PR, Larsen PA, Oh SH. AI-QuIC: machine learning for automated detection of misfolded proteins in seed amplification assays. bioRxiv 2024. Epub ahead of print
• 23 Magesh PR, Myloth RD, Tom RJ. An explainable machine learning model for early detection of Parkinson's disease using LIME on DaTSCAN imagery. Comput Biol Med 2020; 126: 104041
• 24 Mushta I, Koks S, Popov A, Lysenko O. Exploring the potential imaging biomarkers for Parkinson's disease using machine learning approach. Bioengineering (Basel) 2024; 12 (01) 11
• 25 Khachnaoui H, Khlifa N, Mabrouk R. Machine learning for early Parkinson's disease identification within SWEDD group using clinical and DaTSCAN SPECT imaging features. J Imaging 2022; 8 (04) 97
• 26 Gupta R, Kumari S, Senapati A, Ambasta RK, Kumar P. New era of artificial intelligence and machine learning-based detection, diagnosis, and therapeutics in Parkinson's disease. Ageing Res Rev 2023; 90: 102013
• 27 Huang EW, Wang S, Zhai C. VisAGE: integrating external knowledge into electronic medical record visualization. Pac Symp Biocomput 2018; 23: 578-589
• 28 Ayala Solares JR, Diletta Raimondi FE, Zhu Y. et al. Deep learning for electronic health records: a comparative review of multiple deep neural architectures. J Biomed Inform 2020; 101: 103337
• 29 Smith CM, Weathers AL, Lewis SL. An overview of clinical machine learning applications in neurology. J Neurol Sci 2023; 455: 122799
• 30 Cloud LJ, Jinnah HA. Treatment strategies for dystonia. Expert Opin Pharmacother 2010; 11 (01) 5-15
• 31 Oehrn CR, Cernera S, Hammer LH. et al. Chronic adaptive deep brain stimulation versus conventional stimulation in Parkinson's disease: a blinded randomized feasibility trial. Nat Med 2024; 30 (11) 3345-3356
• 32 Cole ER, Miocinovic S. Are we ready for automated deep brain stimulation programming?. Parkinsonism Relat Disord 2025; 134: 107347
• 33 Liu Y, Zhang G, Tarolli CG. et al. Monitoring gait at home with radio waves in Parkinson's disease: a marker of severity, progression, and medication response. Sci Transl Med 2022; 14 (663) eadc9669
• 34 Acevedo G, Lange F, Calonge C, Peach R, Wong JK, Guarin DL. VisionMD: an open-source tool for video-based analysis of motor function in movement disorders. NPJ Parkinsons Dis 2025; 11 (01) 27
• 35 Islam MS, Rahman W, Abdelkader A. et al. Using AI to measure Parkinson's disease severity at home. NPJ Digit Med 2023; 6 (01) 156
• 36 Duggan MJ, Gervase J, Schoenbaum A. et al. Clinician experiences with ambient scribe technology to assist with documentation burden and efficiency. JAMA Netw Open 2025; 8 (02) e2460637
• 37 Small WR, Wiesenfeld B, Brandfield-Harvey B. et al. Large language model-based responses to patients' in-basket messages. JAMA Netw Open 2024; 7 (07) e2422399
• 38 Li MH, Mestre TA, Fox SH, Taati B. Automated assessment of levodopa-induced dyskinesia: evaluating the responsiveness of video-based features. Parkinsonism Relat Disord 2018; 53: 42-45
• 39 Fereshtehnejad SM, Lökk J. Challenges of teleneurology in the care of complex neurodegenerative disorders: the case of Parkinson's disease with possible solutions. Healthcare (Basel) 2023; 11 (24) 3187
• 40 Mahajan A, Lees AJ. “The machine will see you now”: a clinician's perspective on artificial “intelligence” in clinical care. Mov Disord Clin Pract 2025; . Epub ahead of print
• 41 Landers M, Saria S, Espay AJ. Will artificial intelligence replace the movement disorders specialist for diagnosing and managing Parkinson's disease?. J Parkinsons Dis 2021; 11 (s1): S117-S122
• 42 Karpov OE, Pitsik EN, Kurkin SA. et al. Analysis of publication activity and research trends in the field of AI medical applications: network approach. Int J Environ Res Public Health 2023; 20 (07) 5335
• 43 Dzialas V, Doering E, Eich H. et al; International Parkinson Movement Disorders Society-Neuroimaging Study Group. Houston, we have AI problem! Quality issues with neuroimaging-based artificial intelligence in Parkinson's disease: a systematic review. Mov Disord 2024; 39 (12) 2130-2143
• 44 Wiest IC, Leßmann ME, Wolf F. et al. Anonymizing medical documents with local, privacy preserving large language models: the LLM-Anonymizer. medRxiv 2024. Epub ahead of print
• 45 Mandl KD, How AI. How AI could reshape health care—rise in direct-to-consumer models. JAMA 2025; . Epub ahead of print