The Comparison of ChatGPT 3.5, Microsoft Bing, and Google Gemini for Diagnosing Cases of Neuro-Ophthalmology.

OBJECTIVE

We aim to compare the capabilities of ChatGPT 3.5, Microsoft Bing, and Google Gemini in handling neuro-ophthalmological case scenarios.

METHODS

Ten randomly chosen neuro-ophthalmological cases from a publicly accessible database were used to test the accuracy and suitability of all three models, and the case details were followed by the following query: "What is the most probable diagnosis?"

RESULTS

On the basis of the accuracy of diagnosis, all three chat boxes (ChatGPT 3.5, Microsoft Bing, and Google Gemini) gave the correct diagnosis in four (40%) out of 10 cases, whereas in terms of suitability, ChatGPT 3.5, Microsoft Bing, and Google Gemini gave six (60%), five (50%), and five (50%) out of 10 case scenarios, respectively.

CONCLUSION

ChatGPT 3.5 performs better than the other two when it comes to handling neuro-ophthalmological case difficulties. These results highlight the potential benefits of developing artificial intelligence (AI) models for improving medical education and ocular diagnostics.