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Related Experiment Video

Updated: Jul 3, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Almanac - Retrieval-Augmented Language Models for Clinical Medicine.

Cyril Zakka¹, Rohan Shad², Akash Chaurasia³

¹Department of Cardiothoracic Surgery, Stanford Medicine, Stanford, CA.

|February 12, 2024

Summary

This summary is machine-generated.

Large language models (LLMs) show promise in medicine but can err. Almanac, an LLM with medical data access, demonstrated improved accuracy and safety over standard LLMs in clinical question answering.

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Area of Science:

Artificial Intelligence
Clinical Medicine
Natural Language Processing

Background:

Large language models (LLMs) exhibit zero-shot capabilities for various natural language tasks.
Despite potential in clinical medicine, LLM adoption is hindered by factual inaccuracies and safety concerns.

Purpose of the Study:

To evaluate Almanac, an LLM framework with retrieval from curated medical resources, for medical guideline and treatment recommendations.
To compare Almanac's performance against standard LLMs (ChatGPT-4, Bing, Bard) using clinical questions.

Main Methods:

A panel of eight board-certified clinicians and two health care practitioners evaluated LLM responses.
The evaluation used a novel dataset of 314 clinical questions across nine medical specialties.
Responses from Almanac, ChatGPT-4, Bing, and Bard were compared.

Main Results:

Almanac demonstrated significant improvements in factuality and completeness compared to standard LLMs.
Almanac also showed enhanced user preference and adversarial safety.
Performance was assessed across nine medical specialties.

Conclusions:

LLMs accessing domain-specific corpora hold potential for clinical decision-making.
Rigorous testing of LLMs is crucial before clinical deployment to address limitations.
Findings supported by the National Institutes of Health, National Heart, Lung, and Blood Institute.