ArticleGraefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie2024
Diagnostic capabilities of ChatGPT in ophthalmology.
Article in Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 3 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 3 syntheses or guidelines pooled it, 39 citations in OpenAlex.
- Evaluating Large Language Models in Ophthalmology: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Accuracy of Large Language Models When Answering Clinical Research Questions: Systematic Review and Network Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Comparing Diagnostic Accuracy of Clinical Professionals and Large Language Models: Systematic Review and Meta-Analysis.JMIR medical informatics · 2025Pooled it
- Independent and collaborative performance of large language models and healthcare professionals in diagnosis and triage.NPJ digital medicine · 2026Article
- Article
- A Comparative Analysis of AI-Language Models' MCQ Performance versus Medical Students Across Different Pediatric Topics.Advances in medical education and practice · 2026Article
- Ophtimus-V2-Tx: a compact domain-specific LLM for ophthalmic diagnosis and treatment planning.Scientific reports · 2025Article
- Evaluating the clinical utility of multimodal large language models in rare maculopathy.Scientific reports · 2025Article
- Evolution of AI in anatomy education study based on comparison of current large language models against historical ChatGPT performance.Scientific reports · 2025Article
- Evaluation of DeepSeek-R1 for Ophthalmic Diagnosis and Reasoning: A Comparison with OpenAI o1 and o3.Journal of medical systems · 2025Article
- Performance of ChatGPT-4 Omni and Gemini 1.5 Pro on Ophthalmology-Related Questions in the Turkish Medical Specialty Exam.Turkish journal of ophthalmology · 2025Article
- Evaluating ChatGPT-4 Plus in Ophthalmology: Effect of Image Recognition and Domain-Specific Pretraining on Diagnostic Performance.Diagnostics (Basel, Switzerland) · 2025Article
- Multimodal machine learning enables AI chatbot to diagnose ophthalmic diseases and provide high-quality medical responses.NPJ digital medicine · 2025Article
- Clinical decision-making for uveal melanoma radiotherapy: comparative performance of experienced radiation oncologists and leading generative AI models.Frontiers in oncology · 2025Article
- Opportunities and Challenges of Chatbots in Ophthalmology: A Narrative Review.Journal of personalized medicine · 2024Review
- Artificial Intelligence Diagnosing of Oral Lichen Planus: A Comparative Study.Bioengineering (Basel, Switzerland) · 2024Article
- Performance of ChatGPT in Ophthalmic Registration and Clinical Diagnosis: Cross-Sectional Study.Journal of medical Internet research · 2024Article
- Applications of ChatGPT in the diagnosis, management, education, and research of retinal diseases: a scoping review.International journal of retina and vitreous · 2024Article
- Utilizing Large Language Models in Ophthalmology: The Current Landscape and Challenges.Ophthalmology and therapy · 2024Review
- Predicting Glaucoma Before Onset Using a Large Language Model Chatbot.American journal of ophthalmology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe purpose of this study is to assess the diagnostic accuracy of ChatGPT in the field of ophthalmology.
methodsThis is a retrospective cohort study conducted in one academic tertiary medical center. We reviewed data of patients admitted to the ophthalmology department from 06/2022 to 01/2023. We then created two clinical cases for each patient. The first case is according to the medical history alone (Hx). The second case includes an addition of the clinical examination (Hx and Ex). For each case, we asked for the three most likely diagnoses from ChatGPT, residents, and attendings. Then, we compared the accuracy rates (at least one correct diagnosis) of all groups. Additionally, we evaluated the total duration for completing the assignment between the groups.
resultsChatGPT, residents, and attendings evaluated 126 cases from 63 patients (history only or history and exam findings for each patient). ChatGPT achieved a significantly lower accurate diagnosis rate (54%) in the Hx, as compared to the residents (75%; p < 0.01) and attendings (71%; p < 0.01). After adding the clinical examination findings, the diagnosis rate of ChatGPT was 68%, whereas for the residents and the attendings, it increased to 94% (p < 0.01) and 86% (p < 0.01), respectively. ChatGPT was 4 to 5 times faster than the attendings and residents. CONCLUSIONS AND RELEVANCE: ChatGPT showed low diagnostic rates in ophthalmology cases compared to residents and attendings based on patient history alone or with additional clinical examination findings. However, ChatGPT completed the task faster than the physicians.
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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.