There are many barriers to mental health services, including cost and stigma. Even when individuals receive professional care, assessments are intermittent and may be limited in part by the cyclical nature of psychiatric symptoms. The human voice might have the potential to serve as a valuable biomarker in the identification, early diagnosis or monitoring of psychiatric conditions. Therefore, this protocol presents a proposed scoping review with the aim of synthesising existing knowledge on the application of artificial intelligence (AI) or machine learning (ML) in the management of individuals at risk of/suffering from schizophrenia through audio samples as a biomarker.

Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines and Arksey & O’Malley’s scoping review framework (with recent advancements), we systematically mapped the literature on the application of voice-based biomarkers in schizophrenia. Several databases (PubMed/MEDLINE, Scopus, Web of Science, IEEE Xplore, Embase, Compendex, CINAHL, Scientific Information Database, Magiran, IranMedex and Barakat knowledge network system) will be systematically searched for relevant studies through 2025. All searches will be conducted for peer-reviewed articles/studies published in Persian and English between 1 January 2012 and 1 September 2025. Two researchers will independently carry out screening of the included studies and extraction of data. Any discrepancies will be resolved by consensus. In case no initial consensus is reached, a third researcher will be consulted to make a decision. Findings will be presented narratively in the form of text, summary tables, charts and figures for each research question.

This proposed scoping review is based on publicly available information and is also a review of primary studies, so ethics and publication ethics approval are not required because all data from this study have been previously published. The findings of this review will be published in a peer-reviewed journal and presented at national or international congresses and conferences. Importantly, the initial results from this review will serve as a basis for the design and validation of an intelligent clinical decision support system based on acoustic biomarkers for patients with schizophrenia, using AI or ML techniques.

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