ArticleBreathe (Sheffield, England)2026
Clinical performance and practical limitations of large language models in respiratory medicine.
Article in Breathe (Sheffield, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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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Abstract
This Journal club discusses three recent studies evaluating large language models (LLMs) in respiratory medicine and allergy. In COPD, ChatGPT-3.5 was rated higher than Bing Chat by respiratory specialists for accuracy, completeness, clarity and safety, although clinically important omissions remained. In asthma, 13 out of 14 models exceeded 85% accuracy on multiple-choice questions and five exceeded 95%, but performance was lower and more variable in objective structured clinical examinations. In allergen immunotherapy, ChatGPT-4 achieved fair overall DISCERN quality, but showed insufficient
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Registered trials
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