Xu et al. present a study which identifies the capabilities of GPT 4o in increasing the cost-effectiveness of implementing AI to audit errors in fields which utilize ocular B-scan reports and fundus fluorescein angiography (FFA). The research was performed by Xu et al. via the aid of 180 original ophthalmic imaging reports from Eye Center, The Second Affiliated Hospital, School of Medicine, Zhajiang University, Hangzhou. The team then created a division of reports into a correct and incorrect dataset, with 200 total errors being induced, focusing on eye laterality, lesion, phase, and location. After that, the team utilized GPT-4o to identify the potential errors in each report without prompting. The findings of Xu et al. were collected in a comprehensive report, displaying a 79.0% detection rate performed by GPT-4o, which was compared to the performance of experts, technicians, and ophthalmologists, each presenting accuracy rates of 84%, 86%, and 78% respectively. Subgroup analysis subsequently displayed how GPT-4o was not significantly different from specialists, however was less effective than a top-performing expert in omission errors. Consequently Xu et al. found that due to this insignificant difference in error detection accuracy of GPT-4o and specialists, the subsequent low cost creates an effective and efficient reading operation.