A Systematic Review of Data Science in Early Voice‐Based Detection of Parkinson's and Amyotrophic Lateral Sclerosis Neurodegenerative Diseases

ABSTRACT

Recent studies have shown that voice analysis provides a feasible, non‐invasive diagnostic alternative for the early detection of neurodegenerative diseases (NDs), such as Parkinson's disease (PD) and Amyotrophic Lateral Sclerosis (ALS), using Artificial Intelligence (AI), primarily machine learning models (MLs) that can be integrated into complex clinical decision support systems (CDSSs). Although significant progress has been made, several computational challenges remain, including dataset variability, noisy and incomplete input data, and the need for robust generalization across linguistic and demographic differences. To overcome these issues, system and model reliability must be improved through extensive testing under realistic conditions and integration with clinical workflows. This comprehensive study reviews the potential of AI to analyze speech biomarkers for accurate early detection and diagnosis. Improvements over traditional methods have been achieved through the use of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Ensemble Methods (EMs), and diverse hybrid models. However, adoption of these models in real clinical practice is limited by challenges in standardization and effective real‐world applicability. Effective adoption of highly efficient AI‐based voice analysis in clinical practice requires standardized methods, collaboration among specialists from different disciplines, and evidence from real clinical trials. Physicians require comprehensive explanations of AI models to develop confidence in AI‐powered diagnostic tools. Explainable Artificial Intelligence (XAI) applications for neurodegenerative disease diagnosis remain underexplored in research, despite the need for healthcare practitioners to understand diagnostic predictions. Our study provides insights for researchers, software developers, and clinical practitioners, highlighting the need for future research on the development of robust, adaptive models that can accommodate data errors and remain fair and private across AI‐based diagnostics. Future research must build transparent AI models able to achieve high accuracy alongside interpretability standards for standard medical practice deployment. The review also systematically evaluates the quality of the data underlying the models that are reported, including the adequacy of the sample size, class balance, linguistic diversity, and recording conditions.

This article is categorized under:

Algorithmic Development > Biological Data Mining