Artificial Intelligence Applications for Assessment, Monitoring, and Management of Parkinson Disease Symptoms: Protocol for a Systematic Review
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Abstract
BACKGROUND: Parkinson disease (PD) is the second most prevalent neurodegenerative disease, with around 10 million people with PD worldwide. Current assessments of PD symptoms are conducted by questionnaires and clinician assessments and have many limitations, including unreliable reporting of symptoms, little autonomy for patients over their disease management, and standard clinical review intervals regardless of disease status or clinical need. To address these limitations, digital technologies including wearable sensors, smartphone apps, and artificial intelligence (AI) methods have been implemented for this population. Many reviews have explored the use of AI in the diagnosis of PD and management of specific symptoms; however, there is limited research on the application of AI to the monitoring and management of the range of PD symptoms. A comprehensive review of the application of AI methods is necessary to address the gap of high-quality reviews and highlight the developments of the use of AI within PD care. OBJECTIVE: The purpose of this protocol is to guide a systematic review to identify and summarize the current applications of AI applied to the assessment, monitoring, and management of PD symptoms. METHODS: This review protocol was structured using the PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols) and the Population, Intervention, Comparator, Outcome, and Study (PICOS) frameworks. The following 5 databases will be systematically searched: PubMed, IEEE Xplore, Institute for Scientific Information's Web of Science, Scopus, and the Cochrane Library. Title and abstract screening, full-text review, and data extraction will be conducted by 2 independent reviewers. Data will be extracted into a predetermined form, and any disagreements in screening or extraction will be discussed. Risk of bias will be assessed using the Cochrane Collaboration Risk of Bias 2 tool for randomized trials and the Mixed Methods Appraisal Tool for nonrandomized trials. RESULTS: As of April 2023, this systematic review has not yet been started. It is expected to begin in May 2023, with the aim to complete by September 2023. CONCLUSIONS: The systematic review subsequently conducted as a product of this protocol will provide an overview of the AI methods being used for the assessment, monitoring, and management of PD symptoms. This will identify areas for further research in which AI methods can be applied to the assessment or management of PD symptoms and could support the future implementation of AI-based tools for the effective management of PD. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/46581.
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Trajectory forecasting empowers personalized therapeutic strategies for increasingly precise PD management.
“trajectory forecasting empowers personalized therapeutic strategies for increasingly precise PD management”
From Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine (2026) · cites this paper as Bounsall et al., 2023
The Introduction says AI-based symptom assessment and management may provide more tailored care for people with Parkinson disease. It does not say that forecasting disease trajectories enables therapeutic strategies or increasingly precise management; the paper describes tailored care as a potential benefit. (LLM, conf=0.83)
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Research works printed but not linked: 1
- [17]National Collaborating Centre for Chronic Conditions Parkinson's Disease: National Clinical Guideline for Diagnosis and Management in Primary and Secondary Care 2006 London, UK Royal College of Physicians 21089238
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Onciul, R., Tataru, C.-I., Dumitru, A. V., Crivoi, C., Serban, M., Covache-Busuioc, R.-A., Radoi, M. P., & Toader, C. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14020550 · Cited by 113 · see citing works
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- Authors
- Bounsall, K., Milne-Ives, M., Hall, A., Carroll, C., & Meinert, E.
- Affiliations
- University of Plymouth (GB)
- National Health Service (GB)
- Harvard University (US)
- Corresponding author
- Meinert, Edward
- Venue
- JMIR Research Protocols
- Publisher
- JMIR Publications Inc.
- Published
- 2023-06-14
- Volume
- 12
- Pages
- e46581
- ISSN
- 1929-0748 (ISSN-L)
- PMC
- PMC10337354
- OpenAlex
- W4362569256
- Cited by
- 16
- References
- 61
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 7 · 2023: 2
- FWCI
- 0.65
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Research Protocols (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Newcastle University ePrints (Newcastle Univesity) (cc-by)
- Funders
- Parkinson's UK (H-2101)
- Topics
- Health Sciences › Medicine › Neurology › Parkinson's Disease Mechanisms and Treatments
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- Parkinson's disease, artificial intelligence, symptom assessment, symptom monitoring, symptom management, wearable sensors, mobile application, digital health, neurodegenerative diseases
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