A comparison of the suitability of items generated by 4 large language models for pathology continuing certification

We observed significant differences in the ability of the 4 LLMs evaluated to draft items consistent with the ABPath guidelines based on SME scoring. It is important to assess the various LLMs available to determine which model best meets the needs of the user for the proposed task and not to assume equivalence.

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Publication Details

Journal
American Journal of Clinical Pathology
Published
2025-12-31
DOI
https://doi.org/10.1093/ajcp/aqaf153
Primary Topic
Biomedical Text Mining and Ontologies
Type
article
Field-Weighted Citation Impact
0.00
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article

A comparison of the suitability of items generated by 4 large language models for pathology continuing certification

Anthony Schlinsog, Bonnie Woodworth, Eric F. Glassy, Ritu Nayar et al.
American Journal of Clinical Pathology
Biomedical Text Mining and Ontologies
article

A comparison of the suitability of items generated by 4 large language models for pathology continuing certification

Anthony Schlinsog, Bonnie Woodworth, Eric F. Glassy, Ritu Nayar, Ty McCarthy, Gary W Procop, Colette Earnest
article en

Abstract

We observed significant differences in the ability of the 4 LLMs evaluated to draft items consistent with the ABPath guidelines based on SME scoring. It is important to assess the various LLMs available to determine which model best meets the needs of the user for the proposed task and not to assume equivalence.

American Journal of Clinical PathologyVol. 165(2)
Northwestern University (US), American Board of Internal Medicine (US), American Board of Emergency Medicine (US), American College of Physician Executives (US), Fundación Ramón Domínguez (ES)
Openalex Percentile: Top 33%
Biomedical Text Mining and Ontologies
DOISourceOpenAlex
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