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  11. Almanac - Retrieval-augmented Language Models For Clinical Medicine.
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  3. Research Domains
  5. Information And Computing Sciences
  7. Artificial Intelligence
  9. Natural Language Processing
  11. Almanac - Retrieval-augmented Language Models For Clinical Medicine.

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Almanac - Retrieval-Augmented Language Models for Clinical Medicine.

Cyril Zakka1, Rohan Shad2, Akash Chaurasia3

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

NEJM AI
|February 12, 2024

View abstract on PubMed

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.