Pode a IA identificar marcadores de depressão em amostras de escrita ?

Ferramentas de grau de investigação, maioritariamente usadas em rastreio e não como diagnóstico autónomo. Suficientemente eficazes para que várias universidades as pilotem em admissões de aconselhamento.

Background

Research-grade tools, mostly used in screening and not as standalone diagnoses. Effective enough that several universities pilot them in counseling intake.

AI can identify depression markers in writing samples by analyzing language patterns, such as vocabulary, syntax, and sentiment. Research has shown that individuals with depression often exhibit distinct linguistic characteristics, including increased use of negative words, first-person singular pronouns ("I," "me," "my"), and words related to sadness or loss (e.g., "tearful," "grief," "failure"). Natural language processing (NLP) and machine learning algorithms can be trained to recognize these patterns and predict the likelihood of depression in a given writing sample. These methods have been applied in various studies, including analyses of social media posts, personal essays, and clinical interview transcripts, demonstrating promising results in detecting depression from written text. The National Institute of Mental Health (NIMH) has highlighted the growing body of evidence supporting these approaches, emphasizing their potential for early intervention and scalable mental health screening.