Dysphagia is a common complication among stroke patients, significantly increasing the risk of aspiration pneumonia, malnutrition, and mortality. Traditional diagnostic techniques, such as bedside screening and videofluoroscopic swallowing studies, are limited by accessibility, reliability, and invasiveness. To address the challenges of limited data and complex multimodal signals, we propose a large language model (LLM)-based framework for dysphagia screening. This framework integrates multi-modal physiological signals-including laryngeal vibration, nasal airflow, and swallowing sound-and leverages the powerful reasoning capabilities of LLMs for analysis. A medically-informed prompt template is designed to incorporate individual attributes, key biosignal features, and task instructions, effectively guiding the LLM to focus on dysphagia-related patterns. A total of 217 participants were recruited in this study, including 109 post-stroke patients with dysphagia and 108 healthy individuals, generating 1,391 dysphagic and 1,273 healthy control samples. Evaluation demonstrates that the proposed method achieves a classification accuracy of 96.3%, significantly outperforming baseline models. Notably, the model maintains robust performance in few-shot learning settings, indicating strong generalization capabilities. The proposed LLM-based framework offers a promising solution to early-stage clinical dysphagia screening by effectively integrating multimodal biosignals and leveraging prompt-driven reasoning, with extensive applicability in clinical practice.
Liu et al. (2026) studied this question.