Explainable Artificial Intelligence for Clinical Trust in Parkinson's Disease: A Scoping Review on Non‐Invasive Digital Biomarkers
TL;DR
Current evidence suggests that XAI in PD research provides methodological tools that may support transparency and model understanding, but evidence for clinical trust, usability, and real‐world deployment remains limited.
Abstract
Parkinson's disease (PD) increasingly relies on non‐invasive digital biomarkers and artificial intelligence (AI) methods for early diagnosis, symptom monitoring, and disease management. However, the growing use of complex machine learning and deep learning models introduces challenges related to model opacity, limiting clinical interpretability and potentially hindering adoption in healthcare settings. Explainable artificial intelligence (XAI) has emerged as a potential approach for improving transparency and supporting understanding of model behavior. This scoping review examined the application of XAI techniques in AI systems based on low‐cost, non‐invasive digital biomarkers for PD. Following PRISMA‐ScR guidelines, literature searches were performed in Scopus, PubMed, and Web of Science, identifying 36 eligible studies published between 2021 and 2025. Extracted data included clinical application, data modality, AI models, XAI approaches, explanation characteristics, explainability application, and explanation evaluation strategies. The reviewed studies showed widespread adoption of post hoc attribution‐based methods, particularly SHAP and LIME, across applications including diagnosis, symptom assessment, disease progression monitoring, and treatment‐state identification. XAI was commonly employed for feature ranking, biomarker exploration, model optimization, and explanatory analysis. However, explainability evaluation was highly heterogeneous, with most studies relying primarily on descriptive interpretation or plausibility assessments. Formal evaluation of explanation fidelity, clinician‐centered usability studies, and prospective clinical validation were largely absent. Current evidence suggests that XAI in PD research provides methodological tools that may support transparency and model understanding, but evidence for clinical trust, usability, and real‐world deployment remains limited. The field appears methodologically active but still immature for routine clinical implementation.