Dataset Information

Almanac - Retrieval-Augmented Language Models for Clinical Medicine.

ABSTRACT:

Background

Large language models (LLMs) have recently shown impressive zero-shot capabilities, whereby they can use auxiliary data, without the availability of task-specific training examples, to complete a variety of natural language tasks, such as summarization, dialogue generation, and question answering. However, despite many promising applications of LLMs in clinical medicine, adoption of these models has been limited by their tendency to generate incorrect and sometimes even harmful statements.

Methods

We tasked a panel of eight board-certified clinicians and two health care practitioners with evaluating Almanac, an LLM framework augmented with retrieval capabilities from curated medical resources for medical guideline and treatment recommendations. The panel compared responses from Almanac and standard LLMs (ChatGPT-4, Bing, and Bard) versus a novel data set of 314 clinical questions spanning nine medical specialties.

Results

Almanac showed a significant improvement in performance compared with the standard LLMs across axes of factuality, completeness, user preference, and adversarial safety.

Conclusions

Our results show the potential for LLMs with access to domain-specific corpora to be effective in clinical decision-making. The findings also underscore the importance of carefully testing LLMs before deployment to mitigate their shortcomings. (Funded by the National Institutes of Health, National Heart, Lung, and Blood Institute.).

SUBMITTER: Zakka C

PROVIDER: S-EPMC10857783 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Publications

Almanac - Retrieval-Augmented Language Models for Clinical Medicine.

Zakka Cyril C Shad Rohan R Chaurasia Akash A Dalal Alex R AR Kim Jennifer L JL Moor Michael M Fong Robyn R Phillips Curran C Alexander Kevin K Ashley Euan E Boyd Jack J Boyd Kathleen K Hirsch Karen K Langlotz Curt C Lee Rita R Melia Joanna J Nelson Joanna J Sallam Karim K Tullis Stacey S Vogelsong Melissa Ann MA Cunningham John Patrick JP Hiesinger William W

NEJM AI 20240125 2

<h4>Background</h4>Large language models (LLMs) have recently shown impressive zero-shot capabilities, whereby they can use auxiliary data, without the availability of task-specific training examples, to complete a variety of natural language tasks, such as summarization, dialogue generation, and question answering. However, despite many promising applications of LLMs in clinical medicine, adoption of these models has been limited by their tendency to generate incorrect and sometimes even harmfu ...[more]

PMID: 38343631

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Dataset Information

Almanac - Retrieval-Augmented Language Models for Clinical Medicine.

Background

Methods

Results

Conclusions

Publications

Almanac - Retrieval-Augmented Language Models for Clinical Medicine.

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