Artificial Intelligence Approaches for Early Prediction of Parkinson's Disease.

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Abstract

Parkinson's disease (PD) is a progressive neurodegenerative disorder that affects both motor and non-motor functions, primarily due to the gradual loss of dopaminergic neurons in the substantia nigra. Traditional diagnostic methods largely depend on clinical symptom evaluation, which often leads to delays in detection and treatment. However, in recent years, artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), have emerged as groundbreaking techniques for the diagnosis and management of PD. This review explores the emergent role of AI-driven techniques in early disease detection, continuous monitoring, and the development of personalized treatment strategies. Advanced AI applications, including medical imaging analysis, speech pattern recognition, gait assessment, and the identification of digital biomarkers, have shown remarkable potential in improving diagnostic accuracy and patient care. Additionally, AI-driven telemedicine solutions enable remote and real-time disease monitoring, addressing challenges related to accessibility and early intervention. Despite these promising advancements, several hurdles remain, such as concerns over data privacy, the interpretability of AI models, and the need for rigorous validation before clinical implementation. With PD cases expected to rise significantly by 2030, further research and interdisciplinary collaboration are crucial to refining AI technologies and ensuring their reliability in medical practice. By bridging the gap between technology and neurology, AI has the potential to revolutionize PD management, paving the way for precision medicine and better patient outcomes.

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