Artificial intelligence for early screening of depressive disorders in young adults

Abstract

Depressive disorders are highly prevalent among young adults but often remain undetected in early stages. Initial symptoms are usually subtle and overlooked, delaying access to care. Early recognition is crucial to reduce long-term consequences. Recent studies suggest that artificial intelligence (AI) could identify emotional and behavioral patterns that signal emerging depression, offering new possibilities for early screening. This narrative review summarizes current evidence on AI-based tools for detecting depressive symptoms in young adults. Publications from the past five years were searched in PubMed and Scopus using keywords such as “artificial intelligence,” “machine learning,” and “early detection of depression.” Articles focusing on youth populations and meeting basic methodological criteria were included. Available studies show that AI algorithms can detect early depressive patterns using speech, text, facial expressions, and social media behavior. Reported models demonstrate promising accuracy and could complement traditional assessments. However, concerns about data privacy, algorithmic bias, and ethical use of personal data persist. Further research is needed to validate these tools in clinical settings and to develop ethical frameworks for their implementation. The authors declare no funding support and no conflict of interest.