arXiv:2606.17973v1 Announce Type: new Abstract: Depression is the leading cause of disability worldwide, and early detection of symptom change is essential for timely intervention. Validated instruments such as the Patient Health Questionnaire-9 (PHQ-9) support symptom monitoring at scale, but real-world completion rates are low, introducing response bias and systematic missingness. Passive approaches that infer severity from routinely generated data could close this gap. We address this by predicting PHQ-9 total scores directly from transcripts of conversations between users and an AI mental
Advances in large language models (LLMs) and increased adoption of AI mental health tools are converging, making passive depression monitoring technically feasible.
This development offers a scalable, passive method for mental health symptom monitoring, addressing a critical gap in early intervention and reducing reliance on manual reporting.
The ability to infer mental health states from routine digital interactions, potentially shifting mental healthcare from reactive to proactive, with implications for privacy and clinical practice.
- · AI mental health platforms
- · Mental health researchers
- · Healthcare providers
- · Individuals with depression
- · Traditional diagnostic survey providers
- · Data privacy advocates (potentially)
- · Manual symptom tracking methods
Widespread integration of passive depression monitoring into digital health applications.
Development of new ethical guidelines and regulatory frameworks for AI-driven mental health assessment.
Transformation of mental healthcare delivery models, emphasizing continuous monitoring and personalized interventions.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL