Diagnostic Performance of Artificial Intelligence for Parkinson’s Disease Detection Using Handwriting and Drawing Tasks: A Systematic Review and Meta-Analysis

This systematic review and meta-analysis aimed to evaluate the performance of deep learning (DL)-based methods for classifying individuals with Parkinson’s disease (PD) from handwriting and drawing tasks, following PRISMA guidelines. Google Scholar, IEEE Xplore, PubMed/MEDLINE, ScienceDirect, and Science.gov were searched from 1 July to 17 July 2026, without publication date restrictions. Eligible studies evaluated a DL architecture for PD detection using handwriting or drawing and reported at least one of five performance metrics: accuracy, sensitivity, specificity, precision, or F1-score. Two reviewers independently performed study selection, data extraction, and risk-of-bias assessment using PROBAST+AI. Performance estimates were logit-transformed and pooled separately using random-effects models. Heterogeneity was assessed using Cochran’s Q, I2, and τ2, and results were presented using forest plots with 95% confidence intervals. Potential sources of heterogeneity were investigated through subgroup analyses according to DL architecture, dataset, task, and validation procedure, together with sensitivity analyses. Publication bias was assessed using Egger’s test, and certainty of evidence was evaluated using GRADE. Of 5233 unique records identified, 30 studies were included. Overall pooled performance exceeded 92% across the evaluated metrics. No significant differences were found between fully DL and hybrid approaches or among DL algorithms. Dataset-related differences were observed but were inconsistent across performance metrics. Combined handwriting and drawing tasks consistently achieved higher performance (>95%) than individual tasks, while cross-validation was associated with higher accuracy, specificity, and sensitivity than hold-out validation. Despite these promising findings, substantial heterogeneity and very low certainty of evidence limit the current clinical applicability of these approaches. More standardized protocols, subject-level data separation, and external validation in independent populations are needed to develop robust and generalizable AI models and clarify the potential of handwriting and drawing as non-invasive tools for PD detection.