Abstract: Generating clinical notes from doctor-patient dialogues is a critical task in medical artificial intelligence.Existing me-thods typically rely on large language models(LLMs) with few-shot demonstrations but often struggle to integrate sufficient domain-specific knowledge,leading to suboptimal and less professional outputs.To address this problem,a novel iterative reflection framework is proposed,which integrates Error2Correct example learning and domain-model supervision,aiming to improve the summary quality of EMR.Specifically,a large-cale language model integrating the Error2Correct example learning mechanism is designed for the initial generation and continuous potimization of EMR,and the medical domain knowledge is integrated into the pre-generation stage.Then,this paper uses a lightweight medical pre-training language model,fine-tuned with domain data,to evaluate the refined content,integrating domain knowledge in post-generation.Finally,an iterative scheduler is introduced,which can effectively guide the model to optimize in the continuous process of reflection and improvement.Experimental results on two public datasets demonstrate that the proposed method achieves state-of-the-art performance.Compared with the fine-tuned large language models,the proposed method improves overall performance by 3.68% and 7.75% on IMCS-V2-MRG and ACI-BENCH datasets.

Key words: Large language model, Medical pre-trained model, Summarization generation, Large model reflection, Collaboration of large and small models