{"ID":2861226,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.01558","arxiv_id":"2510.01558","title":"CardioRAG: A Retrieval-Augmented Generation Framework for Multimodal Chagas Disease Detection","abstract":"Chagas disease affects nearly 6 million people worldwide, with Chagas cardiomyopathy representing its most severe complication. In regions where serological testing capacity is limited, AI-enhanced electrocardiogram (ECG) screening provides a critical diagnostic alternative. However, existing machine learning approaches face challenges such as limited accuracy, reliance on large labeled datasets, and more importantly, weak integration with evidence-based clinical diagnostic indicators. We propose a retrieval-augmented generation framework, CardioRAG, integrating large language models with interpretable ECG-based clinical features, including right bundle branch block, left anterior fascicular block, and heart rate variability metrics. The framework uses variational autoencoder-learned representations for semantic case retrieval, providing contextual cases to guide clinical reasoning. Evaluation demonstrated high recall performance of 89.80%, with a maximum F1 score of 0.68 for effective identification of positive cases requiring prioritized serological testing. CardioRAG provides an interpretable, clinical evidence-based approach particularly valuable for resource-limited settings, demonstrating a pathway for embedding clinical indicators into trustworthy medical AI systems.","short_abstract":"Chagas disease affects nearly 6 million people worldwide, with Chagas cardiomyopathy representing its most severe complication. In regions where serological testing capacity is limited, AI-enhanced electrocardiogram (ECG) screening provides a critical diagnostic alternative. However, existing machine learning approache...","url_abs":"https://arxiv.org/abs/2510.01558","url_pdf":"https://arxiv.org/pdf/2510.01558v1","authors":"[\"Zhengyang Shen\",\"Xuehao Zhai\",\"Hua Tu\",\"Mayue Shi\"]","published":"2025-10-02T01:02:04Z","proceeding":"cs.CE","tasks":"[\"cs.CE\",\"cs.LG\",\"eess.SP\"]","methods":"[\"RAG\",\"Language Model\"]","has_code":false}