EMGLLM: Data-to-Text Alignment for Electromyogram Diagnosis Generation with Medical Numerical Data Encoding (2025.findings-acl)
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| Challenge: | Existing Large Language Models struggle to interpret EMG tables . EMGLLM is a data-to-text model for medical examination tables based on electrical signals . |
| Approach: | They propose a data-to-text model that aligns EMG data into word embeddings that reflect health degree. |
| Outcome: | The proposed model outperforms baseline models in understanding EMG tables and generating high-quality diagnoses. |
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| Challenge: | Existing medical report generation efforts focus on producing human-readable reports, yet the generated text may not be well aligned to the clinical facts. |
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