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Review Article

Artificial-Intelligence Applications in U.S. Parkinson’s Disease Care: A Narrative Review of Diagnostic, Monitoring, and Treatment Tools

Authors

  • Gbenga Adeniyi Adediran Department of Computing and Engineering, Leeds Beckett University, Leeds, UK https://orcid.org/0009-0009-0305-3793

    gbengadediran@gmail.com

  • Sylvester Tafirenyika Department of Business Analytics, Hult International Business School, USA https://orcid.org/0009-0000-2904-8962
  • Abena Serwaa Ampomaa Agyemang Department of Physical Therapy, Outreach Physical and Occupational Therapy and Speech Rehabilitation, New York City, New York, USA
  • Ifedayo Akinfemisoye Department of Computer Engineering, Federal University of Technology, Akure, Nigeria https://orcid.org/0009-0004-1631-0852
  • Maureen Amaka Mojekwu Department of Cybersecurity, Yeshiva University, New York City, New York, USA
  • Musa Olayinka Hanafi Department of Computer Science and Engineering, University of Houston, Downtown, Houston, Texas, USA https://orcid.org/0009-0000-9155-2414
  • Charity Uzezi Akpovino Department of Rehabilitation Counseling, University of Maryland, Eastern Shore, Maryland, USA
  • Udochukwu I. Okoronkwo College of Science and Technology, University of Houston, Downtown, Houston, Texas, USA
  • Catherine Folake Fadamiro Department of Computer Science, Federal University of Technology, Akure, Nigeria https://orcid.org/0009-0006-0559-4719
  • Ubalaeze Solomon Elechi Faculty of Health Sciences and Technology, Department of Radiography, University of Nigeria, Nsukka, Enugu State, Nigeria https://orcid.org/0009-0002-3474-1002

Abstract

Parkinson’s disease (PD), one of the most common neurodegenerative disorders in the United States, is rising in prevalence and exposing persistent gaps in access to specialist care. Advances in artificial intelligence (AI) now offer new ways to improve diagnosis, monitoring, and treatment. This narrative review synthesizes recent clinical studies, regulatory filings, reimbursement policies, and expert commentary to describe how machine-learning approaches applied to wearable sensors, speech and typing analysis, neuroimaging, and adaptive deep-brain stimulation are reshaping PD care. Several of these tools, such as Apple Watch-based StrivePD and NeuroRPM, KinesiaU™ sensor kits, and Medtronic’s adaptive DBS platform, have received U.S. FDA clearance since 2020, and early trials suggest they can enrich clinical decision-making and support more continuous, personalized management. Because this is a narrative (rather than systematic) review, the literature search was not exhaustive, study quality was not graded with formal scoring instruments, and no meta-analysis was performed; consequently, selection bias and incomplete coverage are possible, and effect sizes across studies cannot be pooled or compared quantitatively. Real-world adoption also remains limited by workflow friction, regulatory and reimbursement uncertainty, data-privacy obligations, and algorithmic bias. Closing these gaps will require larger pragmatic trials, clinician training, and interoperable data infrastructure to ensure AI innovations are validated, equitable, and clinically useful for the growing U.S. PD population.

Keywords:

Parkinson's Disease Diagnosis Artificial Intelligence Healthcare Remote Patient Monitoring Deep Brain Stimulation Telemedicine

Article information

Journal

Journal of Medical Science, Biology, and Chemistry

Volume (Issue)

2(2), (2025)

Pages

59-69

Published

30-07-2025

How to Cite

Adediran, G. A., Tafirenyika, S., Agyemang, A. S. A., Akinfemisoye, I., Mojekwu, M. A., Hanafi, M. O., Akpovino, C. U., Okoronkwo, U. I., Fadamiro, C. F., & Elechi, U. S. (2025). Artificial-Intelligence Applications in U.S. Parkinson’s Disease Care: A Narrative Review of Diagnostic, Monitoring, and Treatment Tools. Journal of Medical Science, Biology, and Chemistry, 2(2), 59-69. https://doi.org/10.69739/jmsbc.v2i2.716

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