A Continued Pretrained LLM Approach for Automatic Medical Note Generation

Anonymous

Abstract: LLMs are revolutionizing NLP tasks. However, the most powerful LLM, like GPT-4, is too costly for most domain-specific scenarios. We present the first continuously trained 13B Llama2-based LLM that is purpose-built for medical conversations and measured on automated scribing. Our results show that our model outperforms GPT-4 in PubMedQA with 76.6\% accuracy and matches its performance in summarizing medical conversations into SOAP notes. Notably, our model exceeds GPT-4 in capturing a higher number of correct medical concepts and outperforms human scribes with higher correctness and completeness.

Paper Type: short

Research Area: Summarization

Contribution Types: NLP engineering experiment, Approaches to low-resource settings, Approaches low compute settings-efficiency

Languages Studied: English