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

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.