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
Sex
ALL
Min age
18 Years
Max age
—
Healthy volunteers
No
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.
Primary outcome measure(s)
Refractive prediction error for sphere — At index examination Mean absolute error (MAE, diopters) of model-predicted sphere versus clinical reference
Cohen's kappa with 95% CIs between model — At index examination (single time point) Cohen's kappa with 95% CIs between model outputs and clinician-validated reference for per-parameter
This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.
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