Use of Online Large Language Model Chatbots in Cornea Clinics.

Purpose: Online large language model (LLM) chatbots have garnered attention for their potential in enhancing efficiency, providing education, and advancing research. This study evaluated the performance of LLM chatbots-Chat Generative Pre-Trained Transformer (ChatGPT), Writesonic, Google Bard, and Bing Chat-in responding to cornea-related scenarios.

Methods: Prompts covering clinic administration, patient counselling, treatment algorithms, surgical management, and research were devised. Responses from LLMs were assessed by 3 fellowship-trained cornea specialists, blinded to the LLM used, using a standardized rubric evaluating accuracy, comprehension, compassion, professionalism, humanness, comprehensiveness, and overall quality. In addition, 12 readability metrics were used to further evaluate responses. Scores were averaged and ranked; subgroup analyses were performed to identify the best-performing LLM for each rubric criterion.

Results: Sixty-six responses were generated from 11 prompts. ChatGPT outperformed the other LLMs across all rubric criteria, scoring an overall response score of 3.35 ± 0.42 (83.8%). However, Google Bard excelled in readability, leading in 75% of the metrics assessed. Importantly, no responses were found to pose risks to patients, ensuring the safety and reliability of the information provided.

Conclusions: ChatGPT demonstrated superior accuracy and comprehensiveness in responding to cornea-related prompts, whereas Google Bard stood out for its readability. The study highlights the potential of LLMs in streamlining various clinical, administrative, and research tasks in ophthalmology. Future research should incorporate patient feedback and ongoing data collection to monitor LLM performance over time. Despite their promise, LLMs should be used with caution, necessitating continuous oversight by medical professionals and standardized evaluations to ensure patient safety and maximize benefits.