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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.03781 |
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| _version_ | 1866913823730958336 |
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| author | Yu, Jin Park, JaeHo Park, TaeJun Kim, Gyurin Lee, JiHyun Lee, Min Sung Kwon, Joon-myoung Son, Jeong Min Jo, Yong-Yeon |
| author_facet | Yu, Jin Park, JaeHo Park, TaeJun Kim, Gyurin Lee, JiHyun Lee, Min Sung Kwon, Joon-myoung Son, Jeong Min Jo, Yong-Yeon |
| contents | Leveraging Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) for analyzing medical data, particularly Electrocardiogram (ECG), offers high accuracy and convenience. However, generating reliable, evidence-based results in specialized fields like healthcare remains a challenge, as RAG alone may not suffice. We propose a Zero-shot ECG diagnosis framework based on RAG for ECG analysis that incorporates expert-curated knowledge to enhance diagnostic accuracy and explainability. Evaluation on the PTB-XL dataset demonstrates the framework's effectiveness, highlighting the value of structured domain expertise in automated ECG interpretation. Our framework is designed to support comprehensive ECG analysis, addressing diverse diagnostic needs with potential applications beyond the tested dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_03781 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ALFRED: Ask a Large-language model For Reliable ECG Diagnosis Yu, Jin Park, JaeHo Park, TaeJun Kim, Gyurin Lee, JiHyun Lee, Min Sung Kwon, Joon-myoung Son, Jeong Min Jo, Yong-Yeon Machine Learning Leveraging Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) for analyzing medical data, particularly Electrocardiogram (ECG), offers high accuracy and convenience. However, generating reliable, evidence-based results in specialized fields like healthcare remains a challenge, as RAG alone may not suffice. We propose a Zero-shot ECG diagnosis framework based on RAG for ECG analysis that incorporates expert-curated knowledge to enhance diagnostic accuracy and explainability. Evaluation on the PTB-XL dataset demonstrates the framework's effectiveness, highlighting the value of structured domain expertise in automated ECG interpretation. Our framework is designed to support comprehensive ECG analysis, addressing diverse diagnostic needs with potential applications beyond the tested dataset. |
| title | ALFRED: Ask a Large-language model For Reliable ECG Diagnosis |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.03781 |