Clinical Large Language Model Evaluation by Expert Review (CLEVER): Framework Development and Validation

The models show comparable performance on open-ended medical question answering, suggesting that health care-specific LLMs can outperform much larger general purpose LLMs in tasks that require understanding of clinical context. We test the validity of CLEVER evaluations by conducting interannotator agreement, interclass correlation, and washout period analysis.

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Publication Details

Journal
JMIR AI
Published
2025-11-02
DOI
https://doi.org/10.2196/72153
Citations
2
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
4.96
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article

Clinical Large Language Model Evaluation by Expert Review (CLEVER): Framework Development and Validation

David Talby, Veysel Kocaman, Andrei Marian Feier, Mustafa Aytuğ Kaya
2 citations
JMIR AI
Topic Modeling
4.96
article

Clinical Large Language Model Evaluation by Expert Review (CLEVER): Framework Development and Validation

David Talby, Veysel Kocaman, Andrei Marian Feier, Mustafa Aytuğ Kaya
article en
2 citations

Abstract

The models show comparable performance on open-ended medical question answering, suggesting that health care-specific LLMs can outperform much larger general purpose LLMs in tasks that require understanding of clinical context. We test the validity of CLEVER evaluations by conducting interannotator agreement, interclass correlation, and washout period analysis.

JMIR AIVol. 4
George Mason University (US), John Snow (United States) (US)
Openalex Percentile: Top 4%
Topic Modeling
4.96
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