Papers with generating high-utility synthetic clinical text
DualAlign: Generating Clinically Grounded Synthetic Data (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) can generate fluent clinical text, but ensuring that such outputs are clinically grounded and useful for downstream modeling remains challenging. |
| Approach: | They propose a disease-agnostic framework for generating privacy-preserving, clinically faithful synthetic EHR narratives. |
| Outcome: | The proposed framework produces context-aware, symptom-rich sentences that more closely reflect real-world clinical documentation. |