ArticleJournal of neuro-ophthalmology : the official journal of the North American Neuro-Ophthalmology Society2024
ChatGPT Assisting Diagnosis of Neuro-Ophthalmology Diseases Based on Case Reports.
Article in Journal of neuro-ophthalmology : the official journal of the North American Neuro-Ophthalmology Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluating Large Language Models in Ophthalmology: Systematic Review.Journal of medical Internet research · 2025Pooled it
- ChatGPT-5 vs oral medicine experts for rank-based differential diagnosis of oral lesions: a prospective, biopsy-validated comparison.Odontology · 2026Article
- ChatGPT in the diagnosis and management of complex polyneuropathies: comparative analysis with neurologists using real-world cases.NPJ digital medicine · 2026Article
- Large Language Models in Ophthalmology: A Bibliographic Analysis.Turkish journal of ophthalmology · 2026Article
- Comparative performance of GPT-4, GPT-o3, GPT-5, Gemini-3-Flash, and DeepSeek-R1 in ophthalmology question answering.Frontiers in cell and developmental biology · 2026Article
- Evaluating the diagnostic reasoning of large language models in complex neuro-ophthalmological cases: a comparative analysis of GPT-o1 Pro, GPT-4o, Gemini, Grok 2 and DeepSeek.BMJ open ophthalmology · 2025Article
- Review
- Differentiation of optic disc edema and pseudopapilledema with deep learning on near-infrared reflectance images.BMC ophthalmology · 2025Article
- Large Language Models in Neurological Practice: Real-World Study.Journal of medical Internet research · 2025Article
- Evaluating ChatGPT-4 Plus in Ophthalmology: Effect of Image Recognition and Domain-Specific Pretraining on Diagnostic Performance.Diagnostics (Basel, Switzerland) · 2025Article
- GlaucoRAG: A Retrieval-Augmented Large Language Model for Expert-Level Glaucoma Assessment.medRxiv : the preprint server for health sciences · 2025Article
- Reply: Diagnosing Neuro-Ophthalmology Diseases Based on Case Reports: DeepSeek vs ChatGPT.Journal of neuro-ophthalmology : the official journal of the North American Neuro-Ophthalmology Society · 2025Article
- Large Language Models: Pioneering New Educational Frontiers in Childhood Myopia.Ophthalmology and therapy · 2025Article
- Evaluating large language model workflows in clinical decision support for triage and referral and diagnosis.NPJ digital medicine · 2025Article
- A systematic review and meta-analysis of diagnostic performance comparison between generative AI and physicians.NPJ digital medicine · 2025Article
- Accuracy of Artificial Intelligence Versus Clinicians in Real-Life Case Scenarios of Retinopathy of Prematurity.Cureus · 2025Article
- Evaluation and comparison of large language models' responses to questions related optic neuritis.Frontiers in medicine · 2025Article
- Application prospect of large language model represented by ChatGPT in ophthalmology.International journal of ophthalmology · 2025Review
- Exploring the Potentials of Artificial Intelligence in Sepsis Management in the Intensive Care Unit.Critical care research and practice · 2025Review
- Evaluating the competence of large language models in ophthalmology clinical practice: a multi-scenario quantitative study.Frontiers in cell and developmental biology · 2025Article
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Abstract
backgroundTo evaluate the accuracy of Chat Generative Pre-Trained Transformer (ChatGPT), a large language model (LLM), to assist in diagnosing neuro-ophthalmic diseases based on case reports.
methodsWe selected 22 different case reports of neuro-ophthalmic disorders from a publicly available online database. These cases included a wide range of chronic and acute diseases commonly seen by neuro-ophthalmologists. We inserted each case as a new prompt into ChatGPTs (GPT-3.5 and GPT-4) and asked for the most likely diagnosis. We then presented the exact information to 2 neuro-ophthalmologists and recorded their diagnoses, followed by comparing responses from both versions of ChatGPT.
resultsGPT-3.5 and GPT-4 and the 2 neuro-ophthalmologists were correct in 13 (59%), 18 (82%), 19 (86%), and 19 (86%) out of 22 cases, respectively. The agreements between the various diagnostic sources were as follows: GPT-3.5 and GPT-4, 13 (59%); GPT-3.5 and the first neuro-ophthalmologist, 12 (55%); GPT-3.5 and the second neuro-ophthalmologist, 12 (55%); GPT-4 and the first neuro-ophthalmologist, 17 (77%); GPT-4 and the second neuro-ophthalmologist, 16 (73%); and first and second neuro-ophthalmologists 17 (77%).
conclusionsThe accuracy of GPT-3.5 and GPT-4 in diagnosing patients with neuro-ophthalmic disorders was 59% and 82%, respectively. With further development, GPT-4 may have the potential to be used in clinical care settings to assist clinicians in providing accurate diagnoses. The applicability of using LLMs like ChatGPT in clinical settings that lack access to subspeciality trained neuro-ophthalmologists deserves further research.
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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.