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.
@article{arxiv.2508.01401,
title = {MedSynth: Realistic, Synthetic Medical Dialogue-Note Pairs},
author = {Ahmad Rezaie Mianroodi and Amirali Rezaie and Niko Grisel Todorov and Cyril Rakovski and Frank Rudzicz},
journal= {arXiv preprint arXiv:2508.01401},
year = {2025}
}