ArticleAnnals of pediatric cardiology
Comparing closed and open large language models on pediatric cardiology board exam performance.
Article in Annals of pediatric cardiology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- From test-taking to test-building: Using artificial intelligence to strengthen board examination preparation.Annals of pediatric cardiologyArticle
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Authors and funding
3 authors.
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Abstract
Large language models (LLMs) have gained traction in medicine, but there is limited research comparing closed- and open-source models in subspecialty contexts. This study evaluated ChatGPT-4.0o and DeepSeek-R1 on a pediatric cardiology board-style examination to quantify their accuracy and discuss educational and clinical utility. ChatGPT-4.0o and DeepSeek-R1 were used to answer 88 text-based multiple choice questions across 11 pediatric cardiology subtopics from a Pediatric Cardiology Board Review textbook. DeepSeek-R1's processing time per question was measured. ChatGPT-4.0o and DeepSeek-R1 achieved 70% (62/88) and 68% (60/88) accuracy, respectively (
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