Automated Detection and Classification of Delusion-related Content in Naturalistic Audio Diaries Using Multi-Agent Language Models
arXiv:2605.24755v1 Announce Type: cross Abstract: Speech monologues recorded in naturalistic settings provide opportunities to characterize mental illness phenomenology and detect symptom exacerbation. Large language models (LLMs) offer new possibilities for automating this process, as they require annotated data primarily for evaluation rather than training. In this paper, we present a novel automated, multi-agent LLM pipeline for the fine-grained, multi-label extraction of language suggestive of delusional beliefs, associated affective responses, and behavioral responses from transcripts of
The rapid advancement of LLMs now enables their application in highly nuanced and sensitive fields like mental health diagnostics, moving beyond general language tasks.
This development signals a significant expansion of AI into clinical assessment, offering tools for early detection and personalized monitoring of mental health conditions.
AI can now interpret complex naturalistic speech patterns for symptomology, potentially shifting mental health diagnosis and intervention strategies from purely human-led to AI-augmented.
- · Mental healthcare providers
- · AI developers
- · Patients with mental health conditions
- · Psychiatric research
- · Traditional diagnostic methods (potentially)
- · Companies relying on manual transcription and analysis
- · Individuals concerned about data privacy
Automated, continuous monitoring of mental health indicators becomes feasible, allowing for proactive interventions.
The integration of AI into clinical practice necessitates new ethical guidelines and regulatory frameworks for sensitive health data and diagnostic accuracy.
Personalized mental healthcare, powered by AI, could lead to more effective treatments and a reduction in symptom exacerbation, potentially improving public health outcomes and economic productivity.
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