ORIGINAL RESEARCH article
Front. Aging Neurosci.
Sec. Parkinson’s Disease and Aging-related Movement Disorders
Volume 17 - 2025 | doi: 10.3389/fnagi.2025.1638340
This article is part of the Research TopicAdvancing personalized diagnosis and treatment in Parkinson's Disease: Integrating biomarkers, neuroimaging, and artificial intelligenceView all 16 articles
AI-Driven Precision Diagnosis and Treatment in Parkinson's Disease: A Comprehensive Review and Experimental Analysis
Provisionally accepted
- Tshwane University of Technology, Pretoria, South Africa
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Background: Parkinson's disease (PD) represents one of the most prevalent neurodegenerative disorders globally, affecting over 10 million individuals worldwide. Traditional diagnostic approaches rely heavily on clinical observation and subjective assessment, often leading to delayed or inaccurate diagnoses. The emergence of artificial intelligence (AI) technologies offers unprecedented opportunities for precision diagnosis and personalized treatment strategies in PD management.Objective: This study aims to comprehensively review current AI applications in Parkinson's disease diagnosis and treatment, evaluate existing methodologies, and present experimental results from a novel multimodal AI diagnostic framework.Methods: A systematic review was conducted across PubMed, IEEE Xplore, and Web of Science databases from 2018-2024, focusing on AI applications in PD diagnosis and treatment. Additionally, we developed and tested a hybrid machine learning model combining deep learning, computer vision, and natural language processing techniques for PD assessment using motor symptom analysis, voice pattern recognition, and gait analysis.The systematic review identified 127 relevant studies demonstrating significant advances in AI-driven PD diagnosis, with accuracy rates ranging from 78% to 96%. Our experimental framework achieved 94.2% accuracy in early-stage PD detection, outperforming traditional clinical assessment methods. The integrated approach showed particular strength in identifying subtle motor fluctuations and predicting treatment response patterns.Conclusions: AI-driven approaches demonstrate substantial potential for revolutionizing PD diagnosis and treatment personalization. The integration of multiple data modalities and advanced machine learning algorithms enables earlier detection, more accurate monitoring, and optimized therapeutic interventions. Future research should focus on large-scale clinical validation and implementation frameworks for healthcare systems.
Keywords: Parkinson's disease, artificial intelligence, machine learning, precision medicine, neurodegeneration, digital biomarkers
Received: 30 May 2025; Accepted: 15 Jul 2025.
Copyright: © 2025 TWALA. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: BHEKISIPHO TWALA, Tshwane University of Technology, Pretoria, South Africa
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