aceOpenEvidence versus Six Leading Large Language Models in Refractive Surgery: Does Curated Medical Evidence Confer an Advantage?
Purpose To assess the accuracy of OpenEvidence relative to six leading large language models (LLMs) in answering refractive surgery multiple-choice questions, and to compare their performance with users of the American Academy of Ophthalmology (AAO) Basic and Clinical Science Course (BCSC) Self-Assessment Program. Setting Not applicable. Design Randomized questionnaire-based study using 225 questions from the refractive surgery section of the AAO BCSC Program. Methods OpenEvidence and 6 LLMs (ChatGPT-4o, ChatGPT…
The medical-evidence tool OpenEvidence had the lowest accuracy (76%) on 225 refractive surgery questions, below six general-purpose chatbots and the human benchmark of ophthalmology trainees and users.
The authors state the task-specific result should not be interpreted as direct evidence of globally inferior medical knowledge or of performance in open-ended clinical applications.
Evidence
- Peer-reviewedJournal of Cataract & Refractive Surgery2026-10-26
How should this claim be treated?
Truvace Impact Record TRV-2026-2288, v1: “OpenEvidence versus Six Leading Large Language Models in Refractive Surgery: Does Curated Medical Evidence Confer an Advantage?.” Truvace, 2026-10-27. /record/TRV-2026-2288 (accessed at citation time). sha256 08f70fce384cda28…
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