mlsanityData to 7 Oct 2026

Generating High Quality Synthetic Data for Dutch Medical Conversations

arXiv 2604.09645 ↗First posted 25 Mar 2026cs.CL cs.AIcited by 0

On the map: Synthetic data and self-improvement for LLMs → Medical synthetic data and agents

Abstract (arXiv)

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. Quantitative analysis revealed strong lexical variety and overly regular turn-taking, suggesting scripted rather than natural conversation flow. Qualitative review produced slightly below-average scores, with raters noting issues in domain specificity and natural expression. The limited correlation between quantitative and qualitative results highlights that numerical metrics alone cannot fully capture linguistic quality. Our findings demonstrate that generating synthetic Dutch medical dialogues is feasible but requires domain knowledge and carefully structured prompting to balance naturalness and structure in conversation. This work provides a foundation for expanding Dutch clinical NLP resources through ethically generated synthetic data.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Medical synthetic data and agents, one of 13 directions in Synthetic data and self-improvement for LLMs. Builds on 13 of its 51 references that are in the map. First posted on arXiv 25 Mar 2026.

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13 of 51 references.

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  1. 2023Does Synthetic Data Generation of LLMs Help Clinical Text Mining?8 Mar

Other references · 12

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  4. 2024A Framework for Human Evaluation of Large Language Models in Healthcare Derived from Literature Reviewcited by 65
  5. 2024Synthetic Patient-Physician Dialogue Generation from Clinical Notes Using LLMcited by 15
  6. 2023UMASS_BioNLP at MEDIQA-Chat 2023: Can LLMs generate high-quality synthetic note-oriented doctor-patient conversations?cited by 14
  7. 2024Few-shot clinical entity recognition in English, French and Spanish: masked language models outperform generative model promptingcited by 12
  8. 2024Entropy and type-token ratio in gigaword corporacited by 6
  9. 2024ChocoLlama: Lessons Learned From Teaching Llamas Dutchcited by 5
  10. 2025Generation of Synthetic Clinical Text: A Systematic Reviewcited by 4
  11. 2023Enhancing Summarization Performance through Transformer-Based Prompt Engineering in Automated Medical Reportingcited by 3
  12. 2025MedSynth: Realistic, Synthetic Medical Dialogue-Note Pairscited by 2

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