ArticleDigital health
Performance analysis of an emergency triage system in ophthalmology using a customized CHATBOT.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Real-World Performance of a New Online Eye Symptom Triage Tool (Eye+Dot) in an Emergency Eye Clinic: Mixed Methods Evaluation Study.JMIR human factors · 2026Article
- Comparison of ChatGPT-4o and DeepSeek R1 in the Management of Ophthalmological Emergencies-An Analysis of Ten Fictional Case Vignettes.Journal of clinical medicine · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To evaluate the performance of a custom ChatGPT-based chatbot in triaging ophthalmic emergencies compared to trained ophthalmologists. Methods: One hundred hypothetical ophthalmic cases were created based on actual patient data from an ophthalmic emergency department, including details such as age, symptoms and medical history. Three experienced ophthalmologists independently graded these cases using a four-tier severity scale, ranging from Grade 1 (immediate care required) to Grade 4 (non-urgent care). A customized version of ChatGPT was developed to perform the same grading task. Inter-rater agreement was measured between the chatbot and the ophthalmologists, as well as among all human graders. Results: The chatbot demonstrated substantial agreement with the ophthalmologists, achieving Cohen's kappa scores of 0.737, 0.749 and 0.751, respectively. The highest agreement was between ophthalmologist 3 and the chatbot (κ = 0.751). Fleiss' kappa for overall agreement among all graders was 0.79, indicating substantial agreement. The Kruskal-Wallis test showed no statistically significant differences in the distribution of grades assigned by the chatbot and the ophthalmologists ( Conclusions: The study demonstrates that a customized chatbot can perform ophthalmic triage with a level of accuracy comparable to that of trained ophthalmologists. This suggests that AI-assisted triage could be a valuable tool in emergency departments, potentially enhancing clinical workflows and reducing waiting times while maintaining high standards of patient care.
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