Evaluating Chatbot responses to patient questions in the field of glaucoma
View abstract on PubMed
Summary
This summary is machine-generated.ChatGPT demonstrated good accuracy in glaucoma, improving significantly after self-correction. This large language model shows promise for glaucoma information, but requires further validation.
Area Of Science
- Ophthalmology
- Artificial Intelligence
- Medical Informatics
Background
- Publicly available large language models (LLMs) are increasingly being explored for medical applications.
- Evaluating the accuracy and safety of LLMs in specialized medical domains like glaucoma is crucial.
Purpose Of The Study
- To assess the accuracy, comprehensiveness, and safety of ChatGPT, a large language model, in the sub-domain of glaucoma.
- To evaluate the impact of self-correction on the LLM's responses.
Main Methods
- Twenty-four clinically relevant glaucoma questions across diagnosis, treatment, surgeries, and emergencies were curated.
- Responses from ChatGPT (GPT-3.5) were evaluated by a panel of three glaucoma specialists.
- Poorly performing responses were prompted for self-correction and re-evaluated.
Main Results
- The expert panel's mean score for ChatGPT responses was 3.29 out of 4.
- Seven out of 24 responses (29.2%) initially scored 3 or less.
- After self-correction, the proportion of fully accurate responses increased from 30.6% to 57.1% (p=0.026).
Conclusions
- Large language models like ChatGPT show potential in the field of glaucoma.
- The self-correction capability of LLMs can significantly improve response quality.
- Further research and validation are necessary for the clinical application of LLMs in glaucoma.
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