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LLMs can reveal clinical associations via comparison questions, aiding medical decision-making.

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel method to extract associations between clinical variables from large language models (LLMs) using structured comparison questions. This approach, demonstrated in domains like COPD and multiple sclerosis, employs patient comparison triplet questions and a statistical model to estimate correlations without direct access to the LLM's internal workings. The method aims to provide a cautious pathway from implicit correlations within LLM training data to potential causal statements, supporting medical decision-making. AI

IMPACT Introduces a new method for inferring clinical associations from LLMs, potentially aiding medical research and decision-making.

RANK_REASON This is a research paper published on arXiv detailing a new method for extracting clinical associations from LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs can reveal clinical associations via comparison questions, aiding medical decision-making.

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This is a research paper published on arXiv detailing a new method for extracting clinical associations from LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Fabian Kabus, Kian Kordtomeikel, Thomas Brox, Heinz Wiendl, Daiana Stolz, Harald Binder ·

    Eliciting associations between clinical variables from LLMs via comparison questions across populations

    arXiv:2605.06335v1 Announce Type: new Abstract: The training data of large language models (LLMs) comprises a wide range of biomedical literature, reflecting data from many different patient populations. We investigate how it might be possible to recover information on correlatio…

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