Evaluating multiple large language models on orbital diseases.

ABSTRACT: The avoidance of mistakes by humans is achieved through continuous learning, error correction, and experience accumulation. This process is known to be both time-consuming and laborious, often involving numerous detours. In order to assist humans in their learning endeavors, ChatGPT (Generative Pre-trained Transformer) has been developed as a collection of large language models (LLMs) capable of generating responses that resemble human-like answers to a wide range of problems. In this study, we sought to assess the potential of LLMs as assistants in addressing queries related to orbital diseases. To accomplish this, we gathered a dataset consisting of 100 orbital questions, along with their corresponding answers, sourced from examinations administered to ophthalmologist residents and medic

SUBMITTER: Yang QC 

PROVIDER: S-EPMC12277337 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature