The Role of Artificial Intelligence in Parkinson’s Disease: Clinical Applications and Social Implications
DOI:
https://doi.org/10.57125/FEM.2026.03.30.05Keywords:
Artificial intelligence, Clinical innovation, Digital medicine, Parkinson’s disease, socioeconomic impactAbstract
Aim: The purpose of this review is to determine the value of artificial intelligence in clinical management of Parkinson’s disease using its application in diagnostic support, disease monitoring, treatment optimisation, and a description of the socioeconomic consequences.
Study Design: This research paper uses a narrative review design, relying on peer-reviewed literature.
Methodology: The literature review was conducted as a narrative review and analysed using peer-reviewed articles on artificial intelligence, machine learning, and the development of digital health solutions for the management of Parkinson’s disease. The literature focused on AI-based diagnostic systems, symptom-monitoring tools, therapeutic optimisation plans, and impacts at the health system level was located, examined, and synthesised to present a comprehensive overview of the available evidence.
Result: The results suggest that AI-based technologies can be used to enhance early diagnosis, enable continuous and objective monitoring of motor and non-motor symptoms, assist in the preparation of personalised treatments and digital rehabilitation methods. In addition to clinical advantages, AI applications can promote access to care, reduce unnecessary hospital visits, affect workers' roles in the healthcare sector, and improve patients’ quality of life. Nonetheless, issues related to data quality, algorithmic transparency, ethics, regulations, and health equity remain major obstacles to broad clinical use.
Conclusion: Artificial intelligence is a promising technology in the Pd management, and it could change clinical practice and enhance the efficiency of the health system. It will be necessary to pay attention to ethical, regulatory, and socioeconomic aspects and guarantee ethical, fair, and efficient implementation of AI technologies into daily clinical processes.
References
Dorsey ER, Sherer T, Okun MS, Bloem BR. The emerging evidence of the Parkinson pandemic. J Parkinsons Dis. 2018;8(Suppl 1):S3-8.
Kalia LV, Lang AE. Parkinson's disease. Lancet. 2015;386(9996):896-912.
Postuma RB, Berg D, Stern M, Poewe W, Olanow CW, Oertel W, et al. MDS clinical diagnostic criteria for Parkinson's disease. Mov Disord. 2015;30(12):1591-601.
Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, et al. A guide to deep learning in healthcare. Nat Med. 2019;25(1):24-9.
Pereira CR, Pereira DR, Silva FA, Hook C, Weber SAT, Pereira LAM, et al. A step towards the automated diagnosis of Parkinson's disease: analyzing multiple structural magnetic resonance imaging features. Comput Methods Programs Biomed. 2016;136:79-88.
Rovini E, Maremmani C, Cavallo F. How wearable sensors can support Parkinson's disease diagnosis and treatment: a systematic review. Front Neurosci. 2017;11:555.
Ahlrichs C, Lawo M. Parkinson's disease motor symptom assessment using wearable sensors: a systematic review. J Biomed Inform. 2020;109:103520.
Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56.
Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60-88.
Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447-53.
Reddy S, Giri D, Patel R. Artificial intelligence diagnosis of Parkinson's disease from MRI scans. Cureus. 2024;16:e58841.
Little MA, McSharry PE, Roberts SJ, Costello DAE, Moroz IM. Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection. IEEE Trans Biomed Eng. 2009;56(4):1015-22.
Maria C, Periyasamy C. Healthcare professionals' opinions on the role of artificial intelligence in preventing cancer. In: Mohanty SN, Rocha A, Dutta PK, editors. Artificial intelligence in oncology. Cham: Springer; 2025.
Poewe W, Seppi K, Tanner CM, et al. Parkinson disease. Nat Rev Dis Primers. 2017;3:17013.
Feigin VL, Nichols E, Alam T, et al. Global, regional, and national burden of neurological disorders, 1990-2016: a systematic analysis. Lancet Neurol. 2019;18(5):459-80.
Dorsey ER, Bloem BR. The Parkinson pandemic: a call to action. JAMA Neurol. 2018;75(1):9-10.
Balestrino R, Schapira AHV. Parkinson disease. Eur J Neurol. 2020;27(1):27-42.
Bloem BR, Okun MS, Klein C. Parkinson's disease. Lancet. 2021;397(10291):2284-303.
Goetz CG, Tilley BC, Shaftman SR, et al. Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS): scale presentation and clinimetric testing results. Mov Disord. 2008;23(15):2129-70.
Postuma RB, Berg D. Prodromal Parkinson's disease: the decade past, the decade to come. Mov Disord. 2019;34(5):665-75.
Heldman DA, Espay AJ, LeWitt PA, Giuffrida JP. Clinician versus machine: reliability and responsiveness of motor endpoints in Parkinson's disease. Parkinsonism Relat Disord. 2014;20(6):590-5.
Dorsey ER, Vlaanderen FP, Engelen LJ, et al. Moving Parkinson care to the home. Mov Disord. 2016;31(9):1258-62.
Haller S, Badoud S, Nguyen D, et al. Individual detection of patients with Parkinson disease using support vector machine analysis of diffusion tensor imaging data. AJNR Am J Neuroradiol. 2012;33(11):2121-7.
Little MA, McSharry PE, Hunter EJ, Spielman J, Ramig LO. Suitability of dysphonia measurements for telemonitoring of Parkinson's disease. IEEE Trans Biomed Eng. 2009;56(4):1015-22.
Del Din S, Godfrey A, Mazza C, Lord S, Rochester L. Free-living monitoring of Parkinson's disease: lessons from the field. Mov Disord. 2016;31(9):1293-313.
Patel S, Lorincz K, Hughes R, et al. Monitoring motor fluctuations in patients with Parkinson's disease using wearable sensors. IEEE Trans Inf Technol Biomed. 2009;13(6):864-73.
Espay AJ, Hausdorff JM, Sanchez-Ferro A, et al. A roadmap for implementation of patient-centered digital outcome measures in Parkinson's disease. Parkinsonism Relat Disord. 2019;59:135-43.
Kringelbach ML, Jenkinson N, Owen SLF, Aziz TZ. Translational principles of deep brain stimulation. Nat Rev Neurosci. 2007;8(8):623-35.
Rajan R, Brennan L, Bloem BR, et al. Integrated care in Parkinson's disease: a systematic review and meta-analysis. Mov Disord. 2020;35(9):1509-31.
Char DS, Shah NH, Magnus D. Implementing machine learning in health care: addressing ethical challenges. N Engl J Med. 2018;378(11):981-3.
London AJ. Artificial intelligence and black-box medical decisions: accuracy versus explainability. Hastings Cent Rep. 2019;49(1):15-21.
Vaillancourt DE, Barmpoutis A, Wu SS, et al. Automated imaging differentiation for parkinsonism. JAMA Neurol. 2025;82(5):495-505.
Periyasamy C, Jangam KV. The current challenges in psychiatric community rehabilitation: a case study. Indian J Soc Psychiatry. 2024;40(2):193-8.
Mienye ID, Sun Y, Ileberi E. Artificial intelligence and sustainable development in Africa: a comprehensive review. Mach Learn Appl. 2024;18:100591.
Periyasamy C, Narayanan G, Ezhumalai S. Group intervention for intimate partner violence among female spouses of men with alcohol dependence syndrome: an open-label, single-group study. Indian J Psychiatry. 2025;67(3):340-5.
Sarker IH. Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions. SN Comput Sci. 2021;2(6):420.
Balyen L, Peto T. Promising artificial intelligence-machine learning-deep learning algorithms in ophthalmology. Asia Pac J Ophthalmol. 2019;8(3):264-72.
Taye MM. Understanding of machine learning with deep learning: architectures, workflow, applications and future directions. Computers. 2023;12(5):91.
Dorsey ER, Okun MS, Bloem BR. Care, convenience, comfort, confidentiality, and contagion: the 5 C's that will shape the future of telemedicine in Parkinson's disease. J Parkinsons Dis. 2020;10(3):893-7.
Fitzpatrick AL, Katz R. Artificial intelligence, health equity, and the digital divide: a systematic review of current challenges and solutions. Annu Rev Public Health. 2023;44:301-19.
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