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.

翻译：医生花费大量时间记录临床诊疗过程，这一负担加剧了职业倦怠问题。为解决此问题，医疗文档自动化工具至关重要。我们提出MedSynth——一个由合成医疗对话与病历构成的新型数据集，旨在推动对话到病历（Dial-2-Note）和病历到对话（Note-2-Dial）两项任务。该数据集基于对疾病分布的系统性分析，包含覆盖2000余个ICD-10代码的超过1万对对话-病历对。我们证明，该数据集能显著提升模型从对话生成病历以及从病历生成对话的性能。在开放获取、隐私合规且多样化训练数据稀缺的领域，该数据集提供了宝贵资源。代码见https://github.com/ahmadrezarm/MedSynth/tree/main，数据集见https://huggingface.co/datasets/Ahmad0067/MedSynth。