Artificial intelligence in Parkinson's disease: A critical review of multimodal biomarkers, validation, and clinical translation

Abstract

Parkinson's disease (PD) is a heterogeneous neurodegenerative disorder whose early diagnosis and longitudinal assessment remain difficult because motor and non-motor manifestations vary across patients and clinically evident symptoms often emerge after substantial dopaminergic degeneration. Artificial intelligence (AI) can integrate heterogeneous biomarkers, but the reliability of reported performance depends strongly on dataset composition, validation design, model interpretability, and external generalisability. This PRISMA-guided review qualitatively synthesised 74 eligible studies and this review therefore evaluates how machine learning (ML), deep learning (DL), multimodal learning, and explainable and privacy-preserving AI have been used for PD detection, severity assessment, progression modelling, digital-biomarker discovery, and AI-assisted drug discovery.

The review provides a dataset-centric assessment of more than 20 publicly available benchmark datasets spanning seven biomarker modalities, including speech, gait and wearable signals, handwriting, neuroimaging, clinical assessments, molecular and multi-omics data, and multimodal combinations. Across these areas, conventional ML remains useful for smaller structured datasets, whereas DL supports automated feature learning from high-dimensional signals and images; multimodal approaches may capture complementary disease information but require larger harmonised cohorts and more complex validation. The evidence is limited by small or imbalanced samples, inconsistent evaluation protocols, overfitting, scarce independent external validation, restricted interpretability, and demographic and acquisition-related dataset shift. Consequently, high within-dataset performance should not be interpreted as clinical readiness. Future progress requires standardised benchmarks, prospective multicentre validation, explainable multimodal models, longitudinal monitoring, and privacy-preserving data collaboration.