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Author

Hongyan Wang

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#large language models Open access Oct 2026

Text-based nutrient estimation by large language models: a comparative evaluation against the Chinese Food Composition Table

Large language models (LLMs) increasingly provide nutrient information, but their accuracy for Chinese foods and their consistency across repeated queries are not well established. To compare nutrient estimates from nine general-purpose LLMs with the Chinese Food Composition Table (CFCT) and assess within-mo...

Yong-Wen Cao, Zhen-Dong Liu, Ning-Ning Wang et al. · 0 citations
Open access Aug 2026

Association between cumulative intraoperative hypotension and postoperative ICU admission after gastrointestinal lumen cancer surgery: a retrospective cohort study using the INSPIRE database

Background Intraoperative hypotension is associated with postoperative organ injury, but its relationship with postoperative intensive care unit (ICU) admission after gastrointestinal lumen cancer surgery remains uncertain. Methods We conducted a retrospective cohort study using the INSPIRE perioperative database. Adul...

Zhen-Dong Liu, Ning-Ning Wang, Min Chen et al. · 0 citations
Open access Aug 2026

Same child, different risk: demographic bias in childhood obesity attribution by large language models

Publicly accessible English-language web-interface outputs from current LLMs showed systematic demographic patterns in pediatric obesity risk attribution, supporting the need for pre-deployment and post-deployment bias auditing before clinical or consumer health use.

Can Wang, Zhen-Dong Liu, Yan-Yu Jiang et al. · 0 citations
Open access Jul 2026

Profile-associated financial and access-related framing in LLM-generated pediatric asthma referral plans: a factorial audit of seven large language models

LLM-generated pediatric asthma referral plans varied in financial-access, geographic-access, navigation, SDOH-recognition, and selected tone-related framing, which support evaluating structural and access-related framing alongside biomedical content in clinical LLM audits.

Zhendong Liu, Xiaoping Yang, Yu Zhang et al. · 0 citations

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