Importance: Large language models (LLMs) recently developed an unprecedented ability to answer questions. Studies of LLMs from other fields may not generalize to medical oncology, a high-stakes clinical setting requiring rapid integration of new information.
Objective: To evaluate the accuracy and safety of LLM answers on medical oncology examination questions.
Purpose: To validate the quantitative assessment of metamorphopsia in rhegmatogenous retinal detachment (RRD) using M-CHARTS by determining its correlation with subjective reporting of metamorphopsia with a validated metamorphopsia questionnaire (modified MeMoQ).
Methods: The Research Ethics Board approved a prospective observational study carried out at St. Michael's Hospital, Toronto, Canada.
Purpose: Manual extraction of spectral domain optical coherence tomography (SD-OCT) reports is time and resource intensive. This study aimed to develop an optical character recognition (OCR) algorithm for automated data extraction from Cirrus SD-OCT macular cube reports.
Methods: SD-OCT monocular macular cube reports (n = 675) were randomly selected from a single-center database of patients from 2020 to 2023.