LoRA-Tuned Large Language Models for Dementia Detection via Multi-View Speech-Derived Features
arXiv:2606.28445v1 Announce Type: cross Abstract: Early detection of dementia enables timely intervention, and reflecting cognitive impairment, spontaneous speech offers a non-invasive screening modality. Conventional approaches often focus on a single representational dimension -- such as acoustic descriptors, pause modeling, automatic speech recognition (ASR) transcripts, or multimodal fusion -- limiting integrative reasoning across heterogeneous cognitive symptoms. We propose a low-rank adaptation (LoRA)-tuned large language model (LLM) that performs structured multi-view reasoning over fou
The increasing sophistication of large language models and fine-tuning techniques like LoRA allows for more nuanced analysis of complex data like speech, enabling more effective healthcare applications.
This development indicates a significant leap in using AI for non-invasive early disease detection, potentially revolutionizing diagnostics and preventative healthcare strategies.
Traditional single-dimension diagnostic methods are being superseded by multi-view AI analysis, offering a more comprehensive and integrative approach to medical screening.
- · Healthcare providers
- · AI healthcare startups
- · Elderly care sector
- · Patients with cognitive impairment
- · Traditional diagnostic companies
- · Manual speech pathology analyses
Widespread adoption of AI-powered diagnostic tools for neurological conditions.
Reduced burden on healthcare systems due to earlier and more accurate dementia diagnoses.
New ethical guidelines and regulatory frameworks for AI in sensitive medical diagnostics.
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Read at arXiv cs.LG