LLMs generate privacy-safe synthetic clinical reports for data augmentation

Researchers have developed a new evaluation framework to assess the quality of synthetic clinical data generated by Large Language Models (LLMs). The framework measures semantic fidelity, lexical diversity, and privacy to ensure generated reports are clinically coherent, varied, and do not risk patient confidentiality. Experiments using models like DeepSeek-R1, OpenBioLLM-Llama3, and Qwen 3.5 demonstrated their capability to produce safe and useful synthetic mental health evaluation reports, thereby expanding training data for clinical NLP tasks. AI

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IMPACT Provides a robust method for generating privacy-preserving synthetic clinical data, potentially accelerating research and development in healthcare AI.

RANK_REASON Academic paper introducing a new evaluation framework for LLM-generated clinical data.

Read on arXiv cs.LG →

• DeepSeek-R1
• OpenBioLLM-Llama3
• Qwen 3.5
• LLM
• ICD-10

• paper
• safety
• model release