When Symptoms Are Not Enough: Evidence-Weighting Patterns in Large Language Model Psychiatric Screening
arXiv:2605.23148v1 Announce Type: new Abstract: As demand for mental health care outpaces clinician-delivered assessment, scalable screening tools are increasingly needed. Large language models (LLMs) may identify psychiatric risk from patient narratives, but their reliability across diagnoses, demographic subgroups, and evidence-use patterns remains uncertain. We introduce a SCID-anchored benchmark of 555 semi-structured experiential interviews paired with diagnostic reference labels for anxiety disorder, major depressive disorder, post-traumatic stress disorder, and any current mental health
The accelerating capabilities of LLMs intersect with growing demand for accessible mental health screening, making this research timely.
This research provides a crucial benchmark for evaluating LLM reliability in sensitive diagnostic contexts, informing responsible AI deployment in healthcare.
Our understanding of LLMs' potential and limitations in psychiatric screening is now more grounded with specific evidence-weighting patterns and diagnostic reliability data.
- · AI developers in healthcare
- · Mental health accessibility advocates
- · Patients in underserved areas
- · Psychiatric researchers
- · Traditional diagnostic tool developers
- · Companies with unreliable AI screening products
- · Healthcare providers resistant to AI integration
Further development and clinical trials of LLM-based psychiatric screening tools will increase.
Broad adoption of LLM screening could lead to earlier interventions and reduced burden on traditional mental healthcare systems.
The integration of AI into diagnostics could reshape medical training and redefine the roles of healthcare professionals in mental health.
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