Performance of Large Language Models for Structured Recognition and Refractive Prediction
Trial Parameters
Brief Summary
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.
Eligibility Criteria
Inclusion Criteria: -postoperative corrected distance visual acuity (CDVA) of 0.10 logMAR or better -an absolute IOL rotational stability of less than 10∘ at the 1-month follow-up examination Exclusion Criteria: * incomplete biometric data on the examination report; * a history of previous ocular surgery or ocular trauma * the occurrence of intraoperative complications, such as an anterior capsular tear or posterior capsular rupture * the development of significant postoperative complications, including but not limited to severe intraocular infection or inadequate pupillary dilation.