Reading between the Lines: Leveraging Large Language Models for Global Dementia and Depression Assessment from Clinical Interviews
arXiv:2606.18019v1 Announce Type: cross Abstract: Dementia and depression are the most prevalent neuropsychiatric disorders in geriatric populations, and their overlapping symptoms pose major challenges for differential diagnosis. In this study, we investigate open-weights Large Language Models (LLMs) for predicting dementia and depression severity from speech samples collected during standardized history taking interviews with 154 German-speaking subjects. We introduce an observer-based Global Depression Scale (GDS-D) aligned with the established Global Deterioration Scale (GDS), enabling par
The rapid advancement and accessibility of open-weights Large Language Models are enabling new applications in healthcare, particularly in areas requiring nuanced pattern recognition from multimodal data like speech.
This development indicates a growing capability for AI to assist in complex medical diagnostics, potentially improving early detection and differential diagnosis of conditions like dementia and depression, thereby reducing diagnostic delays.
The ability to use LLMs for objective and scalable assessment of neuropsychiatric disorders from speech samples offers a new tool for clinicians, potentially standardizing and modernizing diagnostic processes.
- · Healthcare providers
- · Patients with neuropsychiatric disorders
- · AI developers
- · Geriatric care services
- · Traditional diagnostic methods reliant solely on subjective human observation
AI-powered diagnostic tools become more integrated into clinical practice for mental health assessments.
Improved early detection rates lead to better patient outcomes and reduced healthcare burdens over time.
The application of LLMs expands to detect other complex medical conditions through speech or text analysis, transforming medical diagnostics.
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