AI in Parkinson’s Disease: A Short Review of Machine Learning Approaches for Diagnosis

Arjita Sharma, Abhishek Agarwal, Michel Kalenga Wa Kalenga, Vishal Gupta, Vishal Srivastava

Parkinson’s disease is a neurodegenerative disorder with progressive impairment in patients worldwide, featuring manifestations of both motor dysfunction and various/list-specific non-motor symptoms. Early diagnosis and personalized treatment thus remain the biggest challenges in managing the disease. Artificial intelligence (AI), especially machine learning techniques, has shown immense potential for countering such challenges during the past years. This short review aims to summarize recent innovations in applying Machine Learning (ML) and Deep Learning (DL) to Parkinson’s disease, explicitly directed toward developing diagnostic tools, the prediction of progression, and personalized treatment strategies. We discuss several ML and DL approaches, including supervised and unsupervised learning models that have been applied to classify symptoms and identify biomarkers. In addition, integrating clinical and imaging data into disease models continues to advance. This indicates the emerging role of DL in bypassing the limitations of standard methods. This review of the future of AI in Parkinson’s disease research outlines its possible directions for enhancing patient care and clinical outcomes.