AI-driven precision diagnosis and treatment in Parkinson's disease: a comprehensive review and experimental analysis.

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 to 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.

RESULTS

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.

CONCLUSION

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.