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论文ICLR 2026 Poster2026 年trustworthy medical AI

Critic-Adviser-Reviser 循环精炼:迈向高质量 EMR 语料生成

ICLR 2026 Poster accepted paper at ICLR 2026. Electronic medical records (EMRs) are vital for healthcare research, but their use is limited by privacy concerns. Synthetic EMR generation offers a promising alternative, yet most existing methods merely imitate real records without adhering to rigorous clinical quality principles. To address this, we introduce LLM-CARe, a stage-wise cyclic refinement framework that progressively improves EMR quality through three stages, each targeting a specific granularity: corpus, section and document. At each stage, a Critic, an Adviser, and a Reviser collaborate iteratively to evaluate, provide feedback, and refine the drafts.

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论文详情

英文标题
Critic–Adviser–Reviser Cyclic Refinement: Towards High-Quality EMR Corpus Generation with LLMs
作者
Chen Ning, Xien Liu, Chenwei Yan, Xiao Zhang, Xinxin You, Yuxuan Zhou, Xiangling Fu, Ji Wu
期刊/会议
ICLR 2026 Poster
发表年份
2026 年
研究方向
trustworthy medical AI