MedSynth: Realistic, Synthetic Medical Dialogue-Note Pairs

Abstract: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 this https URL and the dataset is available at this https URL.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2508.01401 [cs.CL]

(or arXiv:2508.01401v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2508.01401 Focus to learn more arXiv-issued DOI via DataCite

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