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
Liuyingxi Su, Lijun Xu, Jun Tang, Manhua Zuo · 2026
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Description
Liuyingxi Su's team graded answers from five chatbots to forty-two blood-cleaning questions, checking quality, ease of reading, and match with medical guides. Blood-cleaning patients more and more turn to large language models for disease-care information, but the trust in that advice had been little tested.
Two masked raters scored outputs on four quality scales and five ease-of-reading gauges, with a senior kidney doctor settling disputes. Answers were checked against KDIGO and other top guidance. Quality differed clearly across models on every scale. The study documents made-up answers and safety concerns, urging patients and doctors to treat chatbot blood-cleaning advice as a starting point, not an order.
Catalog record
| CREATOR | Liuyingxi Su, Lijun Xu, Jun Tang, Manhua Zuo |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 44 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |