Almanac: Retrieval-Augmented Language Models for Clinical Medicine

• URL: http://arxiv.org/abs/2303.01229v2
• Date: Wed, 31 May 2023 21:17:13 GMT
• Title: Almanac: Retrieval-Augmented Language Models for Clinical Medicine
• Abstract summary: We develop Almanac, a large language model framework augmented with retrieval capabilities for medical guideline and treatment recommendations. Performance on a novel dataset of clinical scenarios evaluated by a panel of 5 board-certified and resident physicians demonstrates significant increases in factuality.
• Score: 1.5505279143287174
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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, adoption of these models in real-world settings has been largely limited by their tendency to generate incorrect and sometimes even toxic statements. In this study, we develop Almanac, a large language model framework augmented with retrieval capabilities for medical guideline and treatment recommendations. Performance on a novel dataset of clinical scenarios (n = 130) evaluated by a panel of 5 board-certified and resident physicians demonstrates significant increases in factuality (mean of 18% at p-value < 0.05) across all specialties, with improvements in completeness and safety. Our results demonstrate the potential for large language models to be effective tools in the clinical decision-making process, while also emphasizing the importance of careful testing and deployment to mitigate their shortcomings.

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