AI-Powered Triage of Suicidal Ideation in Adolescents: A Comparative Evaluation of Large Language Models Using Synthetic Clinical Vignettes

Abstract excerpt

<h4>Objective</h4> To evaluate the performance of leading Large Language Models (LLMs) in classifying suicide risk and generating clinically appropriate action plans for adolescent psychiatric cases presented through synthetic clinical vignettes. <h4>Methods</h4> We developed 40 synthetic clinical vignettes depicting adolescents with varying levels of suicide risk, structured according to established clinical fo...

Topics

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• Algorithms
• Data Mining
• Electronic Health Records
• International Classification of Diseases
• Natural Language Processing
• Phenotype
• Suicidal Ideation
• Suicide

Identifiers and source

Literature Corpus work

16fcbdbb-c59d-571b-8b86-04f8d4382145

DOI

10.1101/2025.08.05.25333046