Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization

arXiv:2309.07430v4 Announce Type: replace
Abstract: Analyzing vast textual data and summarizing key information from electronic health records imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown promise in natural language processing (NLP), their effectiveness on a diverse range of clinical summarization tasks remains unproven. In this study, we apply adaptation methods to eight LLMs, spanning four distinct clinical summarization tasks: radiology reports, patient questions, progress notes, and doctor-patient dialogue. Quantitative assessments with …

abstract arxiv clinical clinicians cs.cl data diverse electronic electronic health records experts health information key language language models language processing large language large language models llms medical natural natural language natural language processing nlp processing records summarization summarizing text text summarization textual type vast