Use of Online Large Language Model Chatbots in Cornea Clinics
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.
Authors
- Sara Alshaker (ORCID: https://orcid.org/0000-0001-9033-7966)
- Joshua C. Teichman (ORCID: https://orcid.org/0000-0002-0170-7177)
- Prem Nichani (ORCID: https://orcid.org/0000-0001-6641-1345)
- Clara C. Chan (ORCID: https://orcid.org/0000-0002-2886-2011)
- Stephan Ong Tone (ORCID: https://orcid.org/0000-0001-8619-1490)
Institutions
- Sunnybrook Health Science Centre (CA)
- University Health Network (CA)
- University of Toronto (CA)
- Toronto Western Hospital (CA)
- Trillium Health Centre (CA)
- King Saud University (SA)
- Health Sciences Centre (CA)
- Kensington Health (CA)
Publication Details
- Journal
- Cornea
- Published
- 2024-12-03
- DOI
- https://doi.org/10.1097/ico.0000000000003747
- Citations
- 2
- Primary Topic
- Mobile Health and mHealth Applications
- Type
- article
- Field-Weighted Citation Impact
- 1.70