Applications of speech analysis in diseases’ assessment, prediction and diagnosis: a scoping review

Abstract

Background: Speech production is a coordinated physiological process and a vital digital biomarker for health assessment. Recent advances in artificial intelligence (AI), particularly in representation learning, have substantially expanded the application of speech analysis across diverse clinical domains.

Methods: This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). Five major bibliographic databases were systematically searched for studies published between 2015 and 2025. Eligible studies applied AI-driven speech analysis for clinical diagnosis or monitoring, while those lacking quantitative evaluation or sufficient methodological detail were excluded.

Results: A total of 124 studies were analyzed, covering neurological, psychiatric, and respiratory disorders. The field has transitioned from traditional machine learning with handcrafted features to deep learning and foundation models. Parkinson’s disease, Alzheimer’s disease, depression, and coronavirus disease 2019 (COVID-19) are the most frequently investigated conditions. The included studies were charted and synthesized to map disease coverage, methodological trends, and clinical application scenarios.

Conclusion: Speech analysis offers a non-invasive approach for early disease detection and remote monitoring in telemedicine. To support clinical translation, future research should prioritize model robustness and interpretability across diverse clinical populations.

Keywords

Speech analysis / disease phenotyping / neurological disorders / psychiatric disorders / respiratory disorders

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