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The following is a summary of “Ethical and social issues in prediction of risk of severe mental illness: a scoping review and thematic analysis,” published in the May 2025 issue of BMC Psychiatry by Neiders et al. 

Over the past decade, precision psychiatry has advanced significantly, with a growing focus on predictive tools for the early identification of severe mental disorders like schizophrenia, depression, bipolar disorder and their potential ethical and social implications.  

Researchers conducted a retrospective study to review ethical and social considerations related to predictive tools in precision psychiatry.  

They performed a scoping review that included both empirical and non-empirical studies to explore ethical and social issues related to predictive tools for severe mental disorders. The search was conducted using 3 databases: Scopus, Web of Science, and PubMed. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. Bibliometric analysis and inductive thematic coding were applied to the selected articles. A qualitative thematic analysis was conducted using Atlas.ti to identify relevant themes.  

The results showed that 129 publications were included in the scoping review after screening, eligibility assessment, and citation tracing. The articles spanned various disciplines, including clinical psychology, general medicine, neuroscience, genetics, clinical genetics, psychiatry and mental health, philosophy, and ethics. Among these, 83 were theoretical studies, 35 reported empirical findings, and 11 were review articles. The qualitative thematic analysis identified 4 primary themes: Potential benefits and harms; Rights and responsibilities; Counselling, education, and communication; and Ethical issues in different applications.  

Investigators concluded that the reviewed literature highlighted multiple ethical concerns about predictive tools for severe mental disorders, though notable gaps remained regarding clinical utility, mandatory use, and empirical evidence on stigma and algorithmic risks. 

Source:bmcpsychiatry.biomedcentral.com/articles/10.1186/s12888-025-06949-3