Abstract
Large language models (LLMs) have been shown to answer patient questions in ophthalmology similar to human experts. However, concerns remain regarding their use, particularly related to patient privacy and potential inaccuracies that could compromise patient safety.
This study aimed to compare the performance of an LLM in answering frequently asked patient questions about glaucoma with that of a small language model (SLM) trained locally on ophthalmology-specific literature.
We compiled 35 frequently asked questions on glaucoma, categorized into 6 domains, including pathogenesis, risk factors, clinical manifestations, diagnosis, treatment and prevention, and prognosis. Each question was posed to both a SLM using a retrieval-augmented generation framework, trained on ophthalmology-specific literature, and to a LLM (ChatGPT 4.0, OpenAI). Three glaucoma specialists from a single institution independently assessed the answers using a 3-tier accuracy rating scale: poor (score=1), borderline (score=2), and good (score=3). Each answer received a quality score ranging from 3 to 9 points based on the sum of ratings from the 3 graders. Readability grade level was assessed using 4 formulas, such as the Flesch-Kincaid Level, the Gunning Fog Index, the Coleman-Liau Index, and the Simple Measure of Gobbledygook Index.
The answers from the SLM demonstrated comparable quality with ChatGPT 4.0, scoring mean 7.9 (SD 1.2) and mean 7.4 (SD 1.5), respectively, out of a total of 9 points (P=.13). The accuracy rating was consistent overall and across all 6 glaucoma care domains. Both models provided answers considered unsuitable for health care-related information, as they were difficult for the average layperson to read.
Both models generated accurate content, but the answers were considered challenging for the average layperson to understand, making them unsuitable for health care-related information. Given the specialized SLM's comparable performance to the LLM, its high customization potential, lower cost, and ability to operate locally, it presents a viable option for deploying natural language processing in real-world ophthalmology clinical settings.