Comparison of Ophthalmologist and Large Language Model Chatbot Responses to Online Patient Eye Care Questions
Smith-Kettlewell Eye Research Institute · Stanford University · +4 more institutions
Abstract
Large language models (LLMs) like ChatGPT appear capable of performing a variety of tasks, including answering patient eye care questions, but have not yet been evaluated in direct comparison with ophthalmologists. It remains unclear whether LLM-generated advice is accurate, appropriate, and safe for eye patients.
To evaluate the quality of ophthalmology advice generated by an LLM chatbot in comparison with ophthalmologist-written advice. Design, Setting, and Participants: This cross-sectional study used deidentified data from an online medical forum, in which patient questions received responses written by American Academy of Ophthalmology (AAO)-affiliated ophthalmologists. A masked panel of 8 board-certified ophthalmologists were asked to distinguish between answers generated by the ChatGPT chatbot and human answers. Posts were dated between 2007 and 2016; data were accessed January 2023 and analysis was performed between March and May 2023. Main Outcomes and Measures: Identification of chatbot and human answers on a 4-point scale (likely or definitely artificial intelligence [AI] vs likely or definitely human) and evaluation of responses for presence of incorrect information, alignment with perceived consensus in the medical community, likelihood to cause harm, and extent of harm.
Citation impact
- FWCI
- 8.83
- Percentile
- 100%
- References
- 32
Authors
13- IAIsaac A. Bernstein
Smith-Kettlewell Eye Research Institute, Stanford University
- YZY Zhang
Smith-Kettlewell Eye Research Institute, Stanford University
- DGDevendra Govil
Smith-Kettlewell Eye Research Institute, Stanford University
- IMIyad Majid
Smith-Kettlewell Eye Research Institute, Stanford University
- RTRobert T. Chang
Smith-Kettlewell Eye Research Institute, Stanford University
Topics & keywords
- Chatbot
- Harm
- Medicine
- Eye care
- Medical education
- Advice (programming)
- Family medicine
- Psychology