ArticleInternational journal of retina and vitreous2024
Recommendations for diabetic macular edema management by retina specialists and large language model-based artificial intelligence platforms.
Article in International journal of retina and vitreous, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.
- Evaluating Large Language Models in Ophthalmology: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Applications of Large Language Models in Glaucoma: A Scoping Review.Vision (Basel, Switzerland) · 2026Review
- Large language models in ophthalmology: a scoping review on their utility for clinicians, researchers, patients, and educators.Eye (London, England) · 2025Article
- Article
- Evolutionary patterns and research frontiers of artificial intelligence in age-related macular degeneration: a bibliometric analysis.Quantitative imaging in medicine and surgery · 2025Article
- Applications of ChatGPT in the diagnosis, management, education, and research of retinal diseases: a scoping review.International journal of retina and vitreous · 2024Article
- Discriminative, generative artificial intelligence, and foundation models in retina imaging.Taiwan journal of ophthalmologyReview
Corrections and comments
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Authors and funding
14 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo study the role of artificial intelligence (AI) in developing diabetic macular edema (DME) management recommendations by creating and comparing responses to clinicians in hypothetical AI-generated case scenarios. The study also examined whether its joint recommendations followed national DME management guidelines.
methodsThe AI hypothetically generated 50 ocular case scenarios from 25 patients using keywords like age, gender, type, duration and control of diabetes, visual acuity, lens status, retinopathy stage, coexisting ocular and systemic co-morbidities, and DME-related retinal imaging findings. For DME and ocular co-morbidity management, we calculated inter-rater agreements (kappa analysis) separately for clinician responses, AI-platforms, and the "majority clinician response" (the maximum number of identical clinician responses) and "majority AI-platform" (the maximum number of identical AI responses). Treatment recommendations for various situations were compared to the Indian national guidelines.
resultsFor DME management, clinicians (ĸ=0.6), AI platforms (ĸ=0.58), and the 'majority clinician response' and 'majority AI response' (ĸ=0.69) had moderate to substantial inter-rate agreement. The study showed fair to substantial agreement for ocular co-morbidity management between clinicians (ĸ=0.8), AI platforms (ĸ=0.36), and the 'majority clinician response' and 'majority AI response' (ĸ=0.49). Many of the current study's recommendations and national clinical guidelines agreed and disagreed. When treating center-involving DME with very good visual acuity, lattice degeneration, renal disease, anaemia, and a recent history of cardiovascular disease, there were clear disagreements.
conclusionFor the first time, this study recommends DME management using large language model-based generative AI. The study's findings could guide in revising the global DME management guidelines.
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