MedSynth: Realistic, Synthetic Medical Dialogue-Note Pairs

TL;DR

MedSynth is a new synthetic dataset of over 10,000 realistic medical dialogue-note pairs designed to improve automated medical documentation tools, covering extensive disease categories and enhancing dialogue-note generation tasks.

Contribution

The paper introduces MedSynth, a large, diverse, and privacy-compliant synthetic dataset for medical dialogue and note generation, advancing research in automated clinical documentation.

Findings

Dataset improves model performance in dialogue-to-note tasks

Enhances note-to-dialogue generation accuracy

Provides a valuable resource for privacy-sensitive medical AI research

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 https://github.com/ahmadrezarm/MedSynth/tree/main and the dataset is available at https://huggingface.co/datasets/Ahmad0067/MedSynth.