aceAssisting Screening of Adolescent Depression With Large Language Models as Symptom Detectors: Algorithm Development and Validation
Background: Depression prevalence is rising in adolescents and young adults (ages 10-24 years). Traditional screening methods for depression, such as the Patient Health Questionnaire (PHQ-9), are particularly challenging for adolescents. AI has potential to help with this, but the scarcity of annotated datasets highlights the need for off-the-shelf approaches. In this study, we investigate the feasibility of using state-of-the-art large language models (LLMs) to detect depressive symptoms in adolescent settings…
In a pilot on adolescent health records, off-the-shelf language models found depressive symptoms in clinical notes far better than a word-search baseline, with FLAN-T5 giving the most precise results.
The pilot annotated only 22 patients, compared models under non-standardized prompting, and the authors state the findings need validation in broader populations and for other mental health conditions.
Evidence
- Peer-reviewedJMIR Formative Research2026-10-21
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Truvace Impact Record TRV-2026-2207, v1: “Assisting Screening of Adolescent Depression With Large Language Models as Symptom Detectors: Algorithm Development and Validation.” Truvace, 2026-10-23. /record/TRV-2026-2207 (accessed at citation time). sha256 68dbbc05a617a8fd…
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