{"id":"W4414160008","doi":"10.1167/tvst.14.9.18","title":"Advancing Question-Answering in Ophthalmology With Retrieval-Augmented Generation: Benchmarking Open-Source and Proprietary Large Language Models","year":2025,"lang":"en","type":"article","venue":"Translational Vision Science & Technology","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":true,"has_abstract":true,"ca_institutions":"","funders":"Medical Research Council; Moorfields Eye Hospital NHS Foundation Trust; Retina UK; Moorfields Eye Charity; Sight Research UK; Department of Health and Social Care; Canadian Institute of Steel Construction; UK Research and Innovation; National Institute for Health and Care Research; Amazon Web Services","keywords":"Benchmarking; MEDLINE; Language model; Comprehension","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0154630639637406,"score_gpt":0.3199486013467062,"score_spread":0.3044855373829656,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}