TemporalMed: Advancing Medical Dialogues with Time-Aware Responses in Large Language Models
Yuyan Chen, Jin Zhao, Zhihao Wen, Zhixu Li, Yanghua Xiao
Abstract
Medical dialogue models predominantly emphasize generating coherent and clinically accurate responses. However, in many clinical scenarios, time plays a pivotal role, often dictating subsequent patient management and interventions. Recognizing the latent importance of temporal dynamics, this paper introduces a novel dimension to medical dialogues: timestamps. We advocate that the integration of time-sensitive directives can profoundly impact medical advice, using an illustrative example of post-surgery care with and without timestamps. Our contributions are three-fold: Firstly, we highlight the intrinsic significance of timestamps in medical conversations, marking a paradigm shift in dialogue modeling. Secondly, we present an innovative dataset and framework explicitly tailored for time-stamped medical dialogues, facilitating the model to not only provide medical counsel but also chronologically outline care regimens. Lastly, empirical evaluations indicate our method's proficiency in time-stamped tasks and reveal an uptick in performance in broader medical Q&A domains. Through our endeavors, we aspire to set new benchmarks in patient-centric and time-sensitive medical dialogue systems.