MIRE: A medical information enhanced framework for long-tail medical dialogue synthesis
Bo Lv, Chen Tang, Nayu Liu, Guoxin Yu, Xin Liu, Riyan Zhang, Yue Yu · Expert Systems with Applications · 2025
In recent years, deep-learning-based approaches for medical dialogue generation have become the predominant paradigm. However, real-world medical dialogues often face data imbalance issues, especially long-tail distribution problems. The scarcity of training samples for low-resource diseases makes it challenging for language models to provide accurate and comprehensive diagnostic support. In this paper, we propose MIRE, a novel framework that leverages external medical knowledge of tail diseases and dialogue data of common diseases to guide large language models (LLMs) in generating synthetic dialogues for tail diseases. Specifically, MIRE retrieves and crawls medical information about tail diseases from multiple online sources, enhancing subtype coverage in the generated synthetic dialogues. Moreover, we introduce a style transfer mechanism that utilises rich style templates extracted from common disease conversations to guide LLMs in augmenting dialogues in low-resource domains, thereby narrowing the gap between synthetic and real human dialogues. To evaluate the effectiveness of our method in addressing the long-tail disease problems, we construct a long-tail medical dialogue dataset, named TailMed. Experimental results show that training the model with a mixture of synthetic dialogues and the original dataset significantly improves both automatic metrics and human evaluations. Specifically, the model trained on the MIRE-enhanced dataset outperforms the original by over 20% in average metrics for tail diseases. These results demonstrate the potential of MIRE to enhance clinical dialogue systems, enabling more equitable diagnostic assistance for rare and underrepresented diseases, and contributing to improved accessibility in intelligent healthcare applications.