ArticleJournal of medical Internet research2024
Uncovering Language Disparity of ChatGPT on Retinal Vascular Disease Classification: Cross-Sectional Study.
Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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
38 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
- Information Quality and Semantic Stability of AI Chatbot Responses to Complete Denture Questions: A Turkish-English Benchmarking Study.Healthcare (Basel, Switzerland) · 2026Article
- BRIDGE: benchmarking large language models for understanding real-world clinical practice texts.Nature biomedical engineering · 2026Article
- Large Language Models in Ophthalmology: A Bibliographic Analysis.Turkish journal of ophthalmology · 2026Article
- A multicenter multifunctional assessment of large language models in pure-tone audiogram interpretation for patients.NPJ digital medicine · 2026Article
- Evolving Consultation: Enhancing Ophthalmic Diagnostic Performance Using Large Language Model.Ophthalmology science · 2026Article
- Grounded report generation for enhancing ophthalmic ultrasound interpretation using Vision-Language Segmentation models.NPJ digital medicine · 2026Article
- Bilingual evaluation of large language models for patient education in refractive surgery.International journal of ophthalmology · 2026Article
- Harnessing generative artificial intelligence for periodontitis prediction: a machine learning approach integrating systemic health indicators for precision oral health in resource-limited settings.Frontiers in dental medicine · 2026Article
- From accuracy to robustness: a comparative study of five advanced large language models on the Chinese dental licensing examination.BMC oral health · 2025Article
- Leveraging ChatGPT for Report Error Audit: An Accuracy-Driven and Cost-Efficient Solution for Ophthalmic Imaging Reports.Ophthalmology and therapy · 2025Article
- Consistent performance of large language models in rare disease diagnosis across ten languages and 4917 cases.EBioMedicine · 2025Article
- Large language models in ophthalmology: a scoping review on their utility for clinicians, researchers, patients, and educators.Eye (London, England) · 2025Article
- Comprehensive Survey of OCT-Based Disorders Diagnosis: From Feature Extraction Methods to Robust Security Frameworks.Bioengineering (Basel, Switzerland) · 2025Review
- Performance of Open-Source Large Language Models in Psychiatry: Usability Study Through Comparative Analysis of Non-English Records and English Translations.Journal of medical Internet research · 2025Article
- Generative artificial intelligence for fundus fluorescein angiography interpretation and human expert evaluation.NPJ digital medicine · 2025Article
- Comparative performance analysis of global and chinese-domain large language models for myopia.Eye (London, England) · 2025Article
- Performance of ChatGPT-4o and Four Open-Source Large Language Models in Generating Diagnoses Based on China's Rare Disease Catalog: Comparative Study.Journal of medical Internet research · 2025Article
- Article
- Consistent Performance of GPT-4o in Rare Disease Diagnosis Across Nine Languages and 4967 Cases.medRxiv : the preprint server for health sciences · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBenefiting from rich knowledge and the exceptional ability to understand text, large language models like ChatGPT have shown great potential in English clinical environments. However, the performance of ChatGPT in non-English clinical settings, as well as its reasoning, have not been explored in depth.
objectiveThis study aimed to evaluate ChatGPT's diagnostic performance and inference abilities for retinal vascular diseases in a non-English clinical environment.
methodsIn this cross-sectional study, we collected 1226 fundus fluorescein angiography reports and corresponding diagnoses written in Chinese and tested ChatGPT with 4 prompting strategies (direct diagnosis or diagnosis with a step-by-step reasoning process and in Chinese or English).
resultsCompared with ChatGPT using Chinese prompts for direct diagnosis that achieved an F
conclusionsChatGPT can serve as a helpful medical assistant to provide diagnosis in non-English clinical environments, but there are still performance gaps, language disparities, and errors compared to professionals, which demonstrate the potential limitations and the need to continually explore more robust large language models in ophthalmology practice.
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.