Comparative evaluation of information quality, readability, and guideline consistency of large language model–generated educational content on hemodialysis complications: a cross-sectional assessment of generative AI-driven chatbots
- DOI
- 10.1186/s12882-026-05048-z
- Published
- 2026-05-25
- Container
- BMC Nephrology
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1186/s12882-026-05048-z,
title = {Comparative evaluation of information quality, readability, and guideline consistency of large language model–generated educational content on hemodialysis complications: a cross-sectional assessment of generative AI-driven chatbots},
author = {Liuyingxi Su and Lijun Xu and Jun Tang and Manhua Zuo},
year = {2026},
journal = {BMC Nephrology},
doi = {10.1186/s12882-026-05048-z},
url = {https://doi.org/10.1186/s12882-026-05048-z}
}RIS
TY - JOUR TI - Comparative evaluation of information quality, readability, and guideline consistency of large language model–generated educational content on hemodialysis complications: a cross-sectional assessment of generative AI-driven chatbots AU - Liuyingxi Su AU - Lijun Xu AU - Jun Tang AU - Manhua Zuo PY - 2026 JO - BMC Nephrology DO - 10.1186/s12882-026-05048-z UR - https://doi.org/10.1186/s12882-026-05048-z ER -
APA
Su, L., Xu, L., Tang, J., & Zuo, M. (2026). Comparative evaluation of information quality, readability, and guideline consistency of large language model–generated educational content on hemodialysis complications: a cross-sectional assessment of generative AI-driven chatbots. BMC Nephrology. https://doi.org/10.1186/s12882-026-05048-z
Source records
- crossref · retrieved 2026-09-26T23:16:35.928Z