ArticleiScience2025
ChatMyopia: An AI agent for myopia-related consultation in primary eye care settings.
Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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Who cites it
8 citing papers in PubMed.
- An autonomous multimodal AI agent for evidence-grounded ophthalmic diagnosis.Cell reports. Medicine · 2026Article
- Evaluation Methods for Inference-Time Retrieval-Augmented and Graph Retrieval-Augmented Large Language Models in Health Care: Scoping Review.Journal of medical Internet research · 2026Article
- AI agents: a game changer in ophthalmology?Eye (London, England) · 2026Article
- Review
- Accuracy and Effectiveness of AI-Powered Systems in Patient Counseling, Education, and Management in Optometry and Related Eye-Care Settings: A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Evaluation of large language models and retrieval-augmented generation for clinical reasoning in pediatric myopia: a 50-case real-world study.Scientific reports · 2026Article
- Evaluating large language models in specialized myopia knowledge and clinical reasoning.Frontiers in medicine · 2026Article
- Advances in the application of artificial intelligence in ophthalmic education and clinical training.Frontiers in medicine · 2025Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large language models (LLMs) show promise for tailored healthcare communication but face challenges in interpretability and multi-task integration, particularly for domain-specific needs such as myopia, and their real-world effectiveness as patient education tools has yet to be demonstrated. Here, we introduce ChatMyopia, an LLM-based AI agent to address text- and image-based inquiries related to myopia. ChatMyopia integrates an image classification tool and a retrieval-augmented knowledge base built from literature, expert consensus, and clinical guidelines. Myopic maculopathy grading task, single question examination, and human evaluations validated its ability to deliver accurate and safe responses with high scalability and interpretability. In a randomized controlled trial, it significantly improved patient satisfaction compared to traditional leaflets, enhancing patient education in accuracy, empathy, disease awareness, and communication with eye care practitioners. These findings highlight ChatMyopia's potential as a valuable supplement to enhance patient education and improve satisfaction with medical services in primary eye care settings.
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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.