Journal of Clinical Medicine
Retrospective Assessment of LLM Decision-Making for IOL Power Selection in Cataract Surgery in Comparison with Surgeon Performance
By Randle M. Uyeda, Josh Zhe, Mina M. Sitto and 4 others.
Abstract
Objectives
To evaluate a general-purpose large language model (LLM)’s ability to perform surgeon-level decision-making in choosing an intraocular lens (IOL) power when provided the same preoperative data.
Methods
This study included 293 eyes (174 patients) that underwent monofocal or toric cataract surgery performed by a single surgeon. GPT-5.2 analyzed the same biometric printouts, topographic displays, and preoperative data used in surgical planning. Postoperative refraction for GPT-5.2-selected IOLs was simulated from the measured postoperative refraction using a fixed conversion factor. The mean refractive deviation from target, mean absolute error (MAE), and number of eyes within ±0.25 D and ±0.50 D of target refraction were calculated. A subgroup analysis evaluated toric IOL cylinder magnitude and axis.
