ArticleInternational journal of retina and vitreous2025
Automated and code-free development of a risk calculator using ChatGPT-4 for predicting diabetic retinopathy and macular edema without retinal imaging.
Article in International journal of retina and vitreous, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Large language models for primary care ophthalmic education: a systematic review.Frontiers in medicine · 2026Pooled it
- Prevalence and risk factors associated with diabetic retinopathy: a cross-sectional study in northern Xinjiang.Scientific reports · 2026Article
- How Far Have Large Language Models Advanced in Ophthalmology? A Systematic Review of Their Development, Evaluation, and Readiness for Clinical Use.Research square · 2026Article
- Artificial Intelligence in Diabetic Retinopathy and Diabetic Macular Edema: A Narrative Review.Bioengineering (Basel, Switzerland) · 2025Review
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Abstract
backgroundDiabetic retinopathy (DR) and macular edema (DME) are critical causes of vision loss in patients with diabetes. In many communities, access to ophthalmologists and retinal imaging equipment is limited, making screening for diabetic retinal complications difficult in primary health care centers. We investigated whether ChatGPT-4, an advanced large-language-model chatbot, can develop risk calculators for DR and DME using health check-up tabular data without the need for retinal imaging or coding experience.
methodsData-driven prediction models were developed using medical history and laboratory blood test data from diabetic patients in the Korea National Health and Nutrition Examination Surveys (KNHANES). The dataset was divided into training (KNHANES 2017-2020) and validation (KNHANES 2021) datasets. ChatGPT-4 was used to build prediction formulas for DR and DME and developed a web-based risk calculator tool. Logistic regression analysis was performed by ChatGPT-4 to predict DR and DME, followed by the automatic generation of Hypertext Markup Language (HTML) code for the web-based tool. The performance of the models was evaluated using areas under the curves of receiver operating characteristic curve (ROC-AUCs).
resultsChatGPT-4 successfully developed a risk calculator for DR and DME, operational on a web browser without any coding experience. The validation set showed ROC-AUCs of 0.786 and 0.835 for predicting DR and DME, respectively. The performance of the ChatGPT-4 developed models was comparable to those created using various machine-learning tools.
conclusionBy utilizing ChatGPT-4 with code-free prompts, we overcame the technical barriers associated with using coding skills for developing prediction models, making it feasible to build a risk calculator for DR and DME prediction. Our approach offers an easily accessible tool for the risk prediction of DM and DME in diabetic patients during health check-ups, without the need for retinal imaging. Based on this automatically developed risk calculator using ChatGPT-4, health care workers will be able to effectively screen patients who require retinal examinations using only medical history and laboratory data. Future research should focus on validating this approach in diverse populations and exploring the integration of more comprehensive clinical data to enhance predictive performance.
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