ECG-Agent: On-Device Tool-Calling Agent for ECG Multi-Turn Dialogue
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arXiv
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| Format: | Preprint |
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2026
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| author | Chung, Hyunseung Oh, Jungwoo Kyung, Daeun Kim, Jiho Kwon, Yeonsu Kim, Min-Gyu Choi, Edward |
| author_facet | Chung, Hyunseung Oh, Jungwoo Kyung, Daeun Kim, Jiho Kwon, Yeonsu Kim, Min-Gyu Choi, Edward |
| contents | Recent advances in Multimodal Large Language Models have rapidly expanded to electrocardiograms, focusing on classification, report generation, and single-turn QA tasks. However, these models fall short in real-world scenarios, lacking multi-turn conversational ability, on-device efficiency, and precise understanding of ECG measurements such as the PQRST intervals. To address these limitations, we introduce ECG-Agent, the first LLM-based tool-calling agent for multi-turn ECG dialogue. To facilitate its development and evaluation, we also present ECG-Multi-Turn-Dialogue (ECG-MTD) dataset, a collection of realistic user-assistant multi-turn dialogues for diverse ECG lead configurations. We develop ECG-Agents in various sizes, from on-device capable to larger agents. Experimental results show that ECG-Agents outperform baseline ECG-LLMs in response accuracy. Furthermore, on-device agents achieve comparable performance to larger agents in various evaluations that assess response accuracy, tool-calling ability, and hallucinations, demonstrating their viability for real-world applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_20323 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ECG-Agent: On-Device Tool-Calling Agent for ECG Multi-Turn Dialogue Chung, Hyunseung Oh, Jungwoo Kyung, Daeun Kim, Jiho Kwon, Yeonsu Kim, Min-Gyu Choi, Edward Artificial Intelligence Recent advances in Multimodal Large Language Models have rapidly expanded to electrocardiograms, focusing on classification, report generation, and single-turn QA tasks. However, these models fall short in real-world scenarios, lacking multi-turn conversational ability, on-device efficiency, and precise understanding of ECG measurements such as the PQRST intervals. To address these limitations, we introduce ECG-Agent, the first LLM-based tool-calling agent for multi-turn ECG dialogue. To facilitate its development and evaluation, we also present ECG-Multi-Turn-Dialogue (ECG-MTD) dataset, a collection of realistic user-assistant multi-turn dialogues for diverse ECG lead configurations. We develop ECG-Agents in various sizes, from on-device capable to larger agents. Experimental results show that ECG-Agents outperform baseline ECG-LLMs in response accuracy. Furthermore, on-device agents achieve comparable performance to larger agents in various evaluations that assess response accuracy, tool-calling ability, and hallucinations, demonstrating their viability for real-world applications. |
| title | ECG-Agent: On-Device Tool-Calling Agent for ECG Multi-Turn Dialogue |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2601.20323 |