To take a different approach: Can large language models provide knowledge related to respiratory aspiration?
LLMs demonstrated promising performance in delivering respiratory aspiration-related knowledge and showed promise as supportive tools in training, particularly when their limitations were well understood.
Authors
- Zhifang Ren (ORCID: https://orcid.org/0000-0001-9460-0063)
- Shuojin Fu (ORCID: https://orcid.org/0000-0003-3176-1139)
- Yanling Wang (ORCID: https://orcid.org/0000-0002-1295-4245)
- Qian Xiao (ORCID: https://orcid.org/0000-0002-5657-2863)
- Shuai Jin (ORCID: https://orcid.org/0000-0003-2469-0563)
- Zehui Xuan (ORCID: https://orcid.org/0009-0006-5898-9415)
- Ruifu Kang (ORCID: https://orcid.org/0000-0002-4566-683X)
- Yirou Niu (ORCID: https://orcid.org/0000-0002-8439-8057)
Institutions
- Capital Medical University (CN)
Publication Details
- Journal
- Digital Health
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1177/20552076251349616
- Primary Topic
- Tracheal and airway disorders
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China