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Corneal Tissue Engineering: An In Vitro Model of the Stromal-nerve Interactions of the Human Cornea

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Published on: January 24, 2018

Use of Online Large Language Model Chatbots in Cornea Clinics.

Prem A H Nichani¹, Stephan Ong Tone^1,2, Sara M AlShaker^1,3

¹Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, Ontario, Canada.

|December 3, 2024

Summary

ChatGPT and Google Bard show promise in ophthalmology. ChatGPT excels in accuracy for cornea-related queries, while Google Bard leads in readability, with no patient safety risks identified.

Keywords:

artificial intelligence clinic efficiency cornea large language models patient counselling

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Area of Science:

Ophthalmology
Artificial Intelligence
Medical Informatics

Background:

Large language model (LLM) chatbots offer potential for efficiency, education, and research in healthcare.
Evaluating LLM performance in specialized medical fields like ophthalmology is crucial.

Purpose of the Study:

To assess the performance of various LLM chatbots in responding to cornea-related scenarios.
To compare ChatGPT, Writesonic, Google Bard, and Bing Chat for clinical and research applications in ophthalmology.

Main Methods:

Development of prompts covering clinical administration, patient counseling, treatment, surgery, and research.
Evaluation of LLM responses by cornea specialists using a rubric (accuracy, comprehension, compassion, professionalism, humanness, comprehensiveness, quality).
Assessment of response readability using 12 metrics; subgroup analyses for criterion-specific performance.

Main Results:

ChatGPT outperformed other LLMs across all rubric criteria, achieving the highest overall score (83.8%).
Google Bard demonstrated superior readability, excelling in 75% of readability metrics.
All LLM responses were deemed safe, posing no risk to patients.

Conclusions:

ChatGPT offers superior accuracy and comprehensiveness for ophthalmology-related prompts.
Google Bard provides enhanced readability, beneficial for patient communication.
LLMs show potential for streamlining ophthalmology tasks but require medical professional oversight and ongoing evaluation.