Large language models in neuro-ophthalmology diseases: ChatGPT vs Bard vs Bing

Abstract

AIM: To investigate the capabilities of large language models (LLM) for providing information and diagnoses in the field of neuro-ophthalmology by comparing the performances of ChatGPT-3.5 and -4.0, Bard, and Bing. METHODS: Each chatbot was evaluated for four criteria, namely diagnostic success rate for the described case, answer quality, response speed, and critical keywords for diagnosis. The selected topics included optic neuritis, non-arteritic anterior ischemic optic neuropathy, and Leber hereditary optic neuropathy. RESULTS: =0.011). ChatGPT-3.5 and -4.0 far exceeded the other two interfaces at providing diagnoses and were thus used to find the critical keywords for diagnosis. CONCLUSION: ChatGPT-3.5 and -4.0 are better than Bard and Bing in terms of answer success rate, answer quality, and critical keywords for diagnosis in ophthalmology. This study has broad implications for the field of ophthalmology, providing further evidence that artificial intelligence LLM can aid clinical decision-making through free-text explanations.

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