ArticleInternational journal of ophthalmology2025
Large language models in neuro-ophthalmology diseases: ChatGPT
Article in International journal of ophthalmology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Current Applications of Artificial Intelligence in Neuro-Ophthalmic Imaging: A Narrative Approach.Medical sciences (Basel, Switzerland) · 2026Review
- Comparing the performance of four mainstream large language models on medical literature review generation: a human expert evaluation in SMILE surgery.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimTo 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.
methodsEach 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.
resultsIn terms of diagnostic success rate for the described cases, Bard was unable to provide a diagnosis. The success rates for the described cases increased in the order of Bing, ChatGPT-3.5, and ChatGPT-4.0. Further, ChatGPT-4.0 and -3.5 provided the most satisfactory answer quality for judgment by neuro-ophthalmologists, with their sets of answers resembling the sample set most. Bard was only able to provide ten differential diagnoses in three trials. Bing scored the lowest for the satisfactory standard. A Mann-Whitney test indicated that Bard was significantly faster than ChatGPT-4.0 (
conclusionChatGPT-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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.