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:大型语言模型最近展示了在诸如总结、对话生成和问答等各种自然语言任务中令人印象深刻的零弹能力。尽管临床医学中有许多有希望的应用(例如医疗记录文件、治疗准则-检验),但在现实世界环境中采用这些模型在很大程度上受到限制,因为它们倾向于产生事实错误、有时甚至是有毒的语句。在本文件中,我们探讨了大型语言模型促进和简化医疗指南和建议参考的能力:通过使这些模型能够获取外部护理点工具,以回应医生的询问,我们展示了在各种临床情景中,实际基础、有用性和安全性都大为改善。</s>