Condition: Cataract · Sponsor: Jin Yang
We conducted a single-center, retrospective observational study to evaluate large language models (ChatGPT 4o, GPT-5, DeepSeek) for automated interpretation of de-identified IOLMaster 700 reports provided as raster images. Models produced structured biometric extraction, toric IOL recommendation, and refractive predictions (sphere, cylinder, axis). Primary outcomes included parameter-level agreement and refractive error metrics; secondary outcomes included decision-support performance for toric IOL selection and agreement on ordered T-codes. No clinical intervention was performed.
This description comes directly from the study's public registry record.
Xuanqiao Lin · +8615088920668 · 1532483480@qq.com
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| Eye and ENT hospital of Fudan University | Shanghai, Shanghai Municipality, China | Recruiting |
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Source record: clinicaltrials.gov/study/NCT07183891