Large-language models have recently demonstrated impressive zero-shot capabilities in a variety of natural language tasks such as summarization, dialogue generation, and question-answering. Despite many promising applications in clinical medicine (e.g. medical record documentation, treatment guideline-lookup), adoption of these models in real-world settings has been largely limited by their tendency to generate factually incorrect and sometimes even toxic statements. In this paper we explore the ability of large-language models to facilitate and streamline medical guidelines and recommendation referencing: by enabling these model to access external point-of-care tools in response to physician queries, we demonstrate significantly improved factual grounding, helpfulness, and safety in a variety of clinical scenarios.

翻译：大型语言模型近期在摘要生成、对话生成和问答等多种自然语言任务中展现出令人瞩目的零样本能力。尽管在临床医学领域（如病历记录、治疗指南查询）存在诸多潜在应用，但这类模型在实际场景中的采用仍受到其易生成事实性错误甚至有害表述的显著限制。本文探讨了大型语言模型在辅助和优化医学指南及推荐引用方面的能力：通过使模型能够响应医生查询时访问外部即时护理工具，我们在多种临床场景中证明了其事实依据性、实用性和安全性的显著提升。