LLMs can effectively refine noisy graph structures in medical signal analysis by identifying and removing redundant connections, improving both seizure detection accuracy and model interpretability.
This paper uses large language models to improve how neural networks analyze EEG brain signals for seizure detection. The key innovation is treating LLMs as 'graph refiners'—they remove unnecessary connections in a graph representation of EEG data, making the model more accurate and interpretable.