Objective: The main purpose of this study was to assess how AI and ML methods integrate multiple biomarkers for diagnosing Parkinson’s Disease (PD) to improve diagnostic accuracy and patient risk stratification. 

Background: PD is a long-term neurodegenerative disease currently diagnosed based on motor symptoms, leading to delayed diagnosis and medical care. Neuroimaging, biofluids, genetics, and digital health biomarkers have been explored as potential ways to detect the disease earlier. AI/ML systems analyze vast, complex datasets to detect subtle disease patterns before clinical symptoms appear, redefining early diagnosis and treatment approaches.

Method: This study is based on a systematic review of 63 PubMed-indexed articles published in the last decade, selecting original research, systematic reviews, and meta-analyses applying AI/ML to PD biomarker detection.

Results: The results show that AI-assisted analysis of speech patterns and gait abnormalities identifies prodromal PD more accurately than conventional evaluations. Retinal imaging biomarkers correlated with dopaminergic loss and may serve as noninvasive diagnostic tools. ML models applied to neuromelanin MRI, PET, and SPECT refine PD differentiation from atypical parkinsonisms and enhance diagnostic accuracy. Metabolic and inflammatory profiles in cerebrospinal fluid and plasma exosomes identified molecular signatures of early PD, increasing diagnostic specificity. AI-based genetic models improved PD risk prediction through polygenic risk scores. The reviewed studies also showed the effectiveness of AI in optimizing real-time deep brain stimulation (DBS) parameters and reducing motor fluctuations.

Conclusion: However, challenges remain, including database standardization, algorithm bias, and clinical validation of predictive models. The integration of AI and ML with multimodal biomarkers is a promising approach to improving early diagnosis, assessing disease progression, and personalizing PD treatment. Digital health technologies, advanced neuroimaging, and biochemical biomarkers are shifting PD diagnosis and management. Further studies should focus on data integration, large-scale trials, and improving algorithm interpretability to ensure AI-based diagnostic solutions are readily implemented in clinical practice.

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