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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/2508.01401 |
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| _version_ | 1866915423158534144 |
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| author | Mianroodi, Ahmad Rezaie Rezaie, Amirali Todorov, Niko Grisel Rakovski, Cyril Rudzicz, Frank |
| author_facet | Mianroodi, Ahmad Rezaie Rezaie, Amirali Todorov, Niko Grisel Rakovski, Cyril Rudzicz, Frank |
| contents | Physicians spend significant time documenting clinical encounters, a burden that contributes to professional burnout. To address this, robust automation tools for medical documentation are crucial. We introduce MedSynth -- a novel dataset of synthetic medical dialogues and notes designed to advance the Dialogue-to-Note (Dial-2-Note) and Note-to-Dialogue (Note-2-Dial) tasks. Informed by an extensive analysis of disease distributions, this dataset includes over 10,000 dialogue-note pairs covering over 2000 ICD-10 codes. We demonstrate that our dataset markedly enhances the performance of models in generating medical notes from dialogues, and dialogues from medical notes. The dataset provides a valuable resource in a field where open-access, privacy-compliant, and diverse training data are scarce. Code is available at https://github.com/ahmadrezarm/MedSynth/tree/main and the dataset is available at https://huggingface.co/datasets/Ahmad0067/MedSynth. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_01401 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MedSynth: Realistic, Synthetic Medical Dialogue-Note Pairs Mianroodi, Ahmad Rezaie Rezaie, Amirali Todorov, Niko Grisel Rakovski, Cyril Rudzicz, Frank Computation and Language Artificial Intelligence Physicians spend significant time documenting clinical encounters, a burden that contributes to professional burnout. To address this, robust automation tools for medical documentation are crucial. We introduce MedSynth -- a novel dataset of synthetic medical dialogues and notes designed to advance the Dialogue-to-Note (Dial-2-Note) and Note-to-Dialogue (Note-2-Dial) tasks. Informed by an extensive analysis of disease distributions, this dataset includes over 10,000 dialogue-note pairs covering over 2000 ICD-10 codes. We demonstrate that our dataset markedly enhances the performance of models in generating medical notes from dialogues, and dialogues from medical notes. The dataset provides a valuable resource in a field where open-access, privacy-compliant, and diverse training data are scarce. Code is available at https://github.com/ahmadrezarm/MedSynth/tree/main and the dataset is available at https://huggingface.co/datasets/Ahmad0067/MedSynth. |
| title | MedSynth: Realistic, Synthetic Medical Dialogue-Note Pairs |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2508.01401 |