arXiv 2608.18080 Catalogs LLM Use in Depression Detection and Suicide Risk Tools

The review aggregates interdisciplinary studies that apply LLMs to electronic medical records, social media posts, and sensor inputs for early depression detection and suicide risk scoring. It covers model adaptations via prompt engineering and multimodal fusion of text, speech, and physiological data. Annotation strategies and domain-specific fine-tuning are examined for clinical interpretability.

Data from cited works show LLMs reaching variable performance on benchmark tasks, with gains from targeted prompting and fusion techniques on multimodal streams. The paper records persistent gaps in bias measurement and longitudinal outcome tracking. No deployment-scale accuracy figures or head-to-head trials against clinician baselines appear in the aggregated sources.

Ethical sections flag risks of over-reliance, data privacy failures, and unequal performance across demographic groups. The review stops short of quantifying real-world incident rates or regulatory violation patterns observed in current chatbot deployments. It under-weights operational metrics such as false-positive rates that trigger unnecessary interventions.

Next steps center on required sociotechnical frameworks and regulatory audits before clinical integration. Absence of standardized efficacy thresholds in the document leaves deployment timelines undetermined.