From static guidelines to dynamic personalization: a novel AI-driven evidence-based paradigm for low-concentration...

The rapid increase in the global incidence of myopia poses challenges for prevention and management. Evidence-based low-concentration atropine eye drops can effectively slow down the progression of myopia. However the massive and constantly updated research data place a burden on clinicians, especially in primary care or regions with limited resources, hindering knowledge integration and evidence-based decision-making. There is still a gap in patients' access to reliable and personalized guidance. To address this issue, we developed MyoATP-AI-powered evidence based decision support system specifically designed for low-concentration atropine myopia control, built upon a GraphRAG and chain-of-thought reasoning architecture.

We systematically searched PubMed (2000-2025) for literature on "atropine, myopia", screening 104 relevant clinical articles and constructing a specialized dataset. Based on this, we developed MyoATP-AI, an AI-assisted diagnostic system combining GraphRAG and chain-of-thought reasoning. The knowledge graph structure was optimized through community detection and graph embedding. System performance was evaluated using the BLEU, ROUGE, and RAGAS frameworks. Five ophthalmologists rated the system's safety, professionalism, fluency, comprehensiveness, and satisfaction. Finally, we compared the 2024 consensus opinion with MyoATP-AI.

MyoATP-AI demonstrated excellent performance in fidelity, relevance, and context recall. The automated scoring was high: BLEU (0.9479), ROUGE-1 (0.969), ROUGE-L (0.9677), fidelity (0.947), answer relevance (0.9587), and context recall rate (0.925). Experts gave highly positive evaluations of the system across all dimensions, indicating its great potential in clinical decision support and patient education, as it can provide safe, professional, fluent, and comprehensive responses.

MyoATP-AI is a knowledge graph-based question-answering system that integrates GraphRAG and CoT technology for low-concentration atropine control of myopia. It can provide accurate and useful clinical advice, which, on the one hand, can assist doctors in making evidence-based decisions, and on the other hand, can offer reliable and easily understandable medical guidance to patients. Future work will expand the dataset to enhance the system's applicability in different scenarios and help with the prevention and control of myopia.