---
title: 'Large Language Models Encode Clinical Knowledge'
license: CC-BY-4.0
license_url: https://creativecommons.org/licenses/by/4.0/
license_note: 'paper is CC BY 4.0 (verified on arXiv abs page 2026-11-11; icon by-4.0.png, http://creativecommons.org/licenses/by/4.0/); note is our own summary of an AI evaluation paper, not guidance for care'
source: https://iinventedlight.com/library/notes/2212-13138-llms-encode-clinical-knowledge.md
source_license: 'CC BY 4.0'
attribution: 'Summary by iinventedlight (AI-assisted). Source: Karan Singhal, Shekoofeh Azizi, Tao Tu, and 27 coauthors, Large Language Models Encode Clinical Knowledge (CC BY 4.0).'
author: 'iinventedlight (AI-assisted)'
date: 2026-11-11
status: published
approved_by: "Cole Withington, 2026-11-11 20:58 PT, Grok Bot chat widget t1717s7407: \"Publish all 100 including the LLMs Encode Clinical Knowledge note\""
checked_on: 2026-11-11
as_of: 2026-11-11
confidence: medium
untested: 'Not reproduced. Abs page and HTML key sections read 2026-11-11. No public training code URL is given in the paper; nothing was run.'
category: arxiv
topic_area: 'ai-research: AI-for-science'
tags: arxiv, language-models, evals, instruction-tuning
arxiv_id: 2212.13138
alias: 2212.13138-llms-encode-clinical-knowledge
sources:
  - id: S1
    title: 'Large Language Models Encode Clinical Knowledge'
    publisher: arXiv
    authors: 'Karan Singhal, Shekoofeh Azizi, Tao Tu, and 27 coauthors (listed on the arXiv abs page)'
    url: https://arxiv.org/abs/2212.13138
    version_read: 'v1 (26 Dec 2022)'
    accessed: 2026-11-11
    license: 'CC BY 4.0'
---
# Large Language Models Encode Clinical Knowledge

## Summary
This paper presents MultiMedQA, a combined question-answering benchmark, and evaluates PaLM, a 540-billion parameter language model, plus its instruction-tuned variant Flan-PaLM. Using a combination of prompting strategies, Flan-PaLM achieves 67.6% accuracy on MedQA, surpassing prior state-of-the-art by over 17%, and state-of-the-art accuracy on every MultiMedQA multiple-choice dataset. Human evaluation reveals key gaps in Flan-PaLM responses. They then introduce instruction prompt tuning, a parameter-efficient way to align a model to a new domain using a few exemplars. The resulting model, Med-PaLM, performs encouragingly, but remains inferior to clinicians. [S1]

## Claims
- id: C1
  claim: 'Flan-PaLM reaches 67.6% accuracy on MedQA (USMLE-style questions).'
  source: S1
  evidence: "67.6% accuracy on MedQA (US Medical License Exam questions)"
- id: C2
  claim: 'That MedQA result surpasses the prior reported state of the art by over 17%.'
  source: S1
  evidence: "surpassing prior state-of-the-art by over 17%"
- id: C3
  claim: 'Med-PaLM, after instruction prompt tuning, still remains inferior to clinicians.'
  source: S1
  evidence: "The resulting model, Med-PaLM, performs encouragingly, but remains inferior to clinicians"

## Verification
- Code available: The paper does not point to a public training-code repository. Not run.
- Reproduced: not reproduced (2026-11-11). No public code URL was found on the abs page or in the HTML sections read.
- Related work: No live library note is a paper-verification page for this work.
- Limits: The authors say the combined benchmark is not exhaustive, and they discuss limits of the evaluation setup and of current model behavior, including that Med-PaLM still trails clinicians.
- License: CC BY 4.0 on the abs page (2026-11-11; icon by-4.0.png; http://creativecommons.org/licenses/by/4.0/). Submitted 26 Dec 2022 (v1). Sources: [S1] https://arxiv.org/abs/2212.13138, v1 (26 Dec 2022). checked_on: 2026-11-11.

## When to apply
Use when asking how a language model was scored on MultiMedQA and how instruction prompt tuning produced Med-PaLM. Not guidance for care.

## Facts
- id: F1
  claim: 'Flan-PaLM reaches 67.6% accuracy on MedQA (USMLE).'
  confidence: medium
  date: 2026-11-11
  sources: [S1]
  license: CC BY 4.0
- id: F2
  claim: 'The authors report that this MedQA score surpasses the prior state of the art by over 17%.'
  confidence: medium
  date: 2026-11-11
  sources: [S1]
  license: CC BY 4.0
- id: F3
  claim: 'After instruction prompt tuning, Med-PaLM still remains inferior to clinicians.'
  confidence: medium
  date: 2026-11-11
  sources: [S1]
  license: CC BY 4.0

## Sources
- [S1] arXiv 2212.13138, https://arxiv.org/abs/2212.13138. License: CC BY 4.0. v1 (26 Dec 2022). Accessed 2026-11-11.

License: this note is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Summary by iinventedlight (AI-assisted). Paper is CC BY 4.0.

---

Agents: [/agents.md](/agents.md) | [/.well-known/agent-library.json](/.well-known/agent-library.json).