Performance evaluation of mainstream large language models in cataract science popularization Q&A: a comparative study of readability, quality, and educational suitability
Yunqing Hou, Mingyue Liu, Zhuowei Wang, Xiaohui Jiang · 2026
Read Performance evaluation of mainstream large language models in cataract science popularization Q&A: a comparative study of readability, quality, and educational suitability by Yunqing Hou, Mingyue Liu, Zhuowei Wang, Xiaohui Jiang (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/PMC13645593.txt.
Description
Yunqing Hou and three colleagues compare five large language models on how well they answer common patient questions about cataracts. The condition clouds the eye lens and are a leading cause of reversible blindness, mainly in older people. The team put twenty frequent questions to each model. Answers were scored for reading ease, information quality, and fitness for patient teaching.
Clear differences appeared between models on every measure the team used. One model earned the highest quality and teaching scores, though its answers were also harder to read. Topics differed too: aftercare and prevention answers taught well, while surgery topics read harder. The authors note that easy reading and high quality did not always go together. Their results can help clinics pick and supervise chat tools for patient education with more care.
Catalog record
| CREATOR | Yunqing Hou, Mingyue Liu, Zhuowei Wang, Xiaohui Jiang |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 33 KB |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |