Generating High Quality Synthetic Data for Dutch Medical Conversations
Medical conversations offer insights into clinical communication often absent from Electronic Health Records. However, developing reliable clinical Natural Language Processing (NLP) models is hampered by the scarcity of domain-specific datasets, as clinical data are typically inaccessible due to privacy and ethical constraints. To address these challenges, we present a pipeline for generating synthetic Dutch medical dialogues using a Dutch fine-tuned Large Language Model, with real medical conversations serving as linguistic and structural reference. The generated dialogues were evaluated through quantitative metrics and qualitative review by native speakers and medical practitioners. Qua...
| Authors | Cecilia Kuan, Aditya Kamlesh Parikh, Henk van den Heuvel |
|---|---|
| Primary category | cs.CL |
| Published | 25 March 2026 |
| Last updated at source | 25 March 2026 |
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