ArticleiScience2026
Comparison of the performance of large language models in answering patient questions related to cataract.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study evaluated the performance of four popular large-scale language models (ChatGPT o3-mini, Gemini 2.0 pro experimental, Deep Seek Thinking R1, and Kimi Thinking K1.5) in addressing frequently asked patient questions about cataracts and cataract surgery in Chinese. DeepSeek Thinking R1 performed comparably to Gemini 2.0 pro experimental in accuracy, while outperforming both ChatGPT o3-mini and Kimi Thinking K1.5. In terms of completeness and consistency, DeepSeek Thinking R1 showed superior performance over the other three LLMs. Regarding legibility and safety, DeepSeek Thinking R1, Gemini 2.0 pro experimental, and ChatGPT o3-mini exhibited comparable results, all performing better than Kimi Thinking K1.5. Deep Seek Thinking R1 demonstrated the strongest overall performance among the four LLMs in this comparative evaluation. The modern LLMs are promising tools for public education in ophthalmology while human oversight is still required.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.