1. Almanac: Retrieval-Augmented Language Models for Clinical Medicine
- Author
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William Hiesinger, Cyril Zakka, Akash Chaurasia, Rohan Shad, Alex Dalal, Jennifer Kim, Michael Moor, Kevin Alexander, Euan Ashley, Jack Boyd, Kathleen Boyd, Karen Hirsch, Curtis Langlotz, and Joanna Nelson
- Subjects
FOS: Computer and information sciences ,Artificial Intelligence (cs.AI) ,Computer Science - Computation and Language ,Computer Science - Artificial Intelligence ,Computation and Language (cs.CL) - 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 impor- tance of careful testing and deployment to mitigate their shortcomings.
- Published
- 2023