| 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. |
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| Challenge: | Existing methods to generate high-quality synthetic corpus from clinical documents require learning from the original clinical documents. |
| Approach: | They propose a method to generate synthetic corpus from clinical documents using a large language model. |
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High-Quality Medical Dialogue Synthesis for Improving EMR Generation (2025.emnlp-industry)
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| Challenge: | Existing methods for generating EMRs from doctor-patient dialogues produce rigid and repetitive dialogues. |
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Synthetic Doctor-Patient Dialogue Generation for Robust Medical ASR: A Scalable Pipeline for Vocabulary Expansion and Privacy Preservation (2026.eacl-industry)
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| Challenge: | Existing ASR models struggle with high word error rates (WER) on clinical vocabulary, especially medication names. |
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Can Synthetic Text Help Clinical Named Entity Recognition? A Study of Electronic Health Records in French (2023.eacl-main)
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| Challenge: | In sensitive domains, the sharing of corpora is restricted due to confidentiality, copyrights or trade secrets. |
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Generating Synthetic Free-text Medical Records with Low Re-identification Risk using Masked Language Modeling (2025.naacl-srw)
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| Challenge: | Existing methods to generate medical records using Causal Language Modelling are limited due to privacy concerns. |
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Two Directions for Clinical Data Generation with Large Language Models: Data-to-Label and Label-to-Data (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) can generate natural language texts for various domains and tasks, but their potential for clinical text mining is under-explored. |
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Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes (2024.findings-acl)
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Sunjun Kweon, Junu Kim, Jiyoun Kim, Sujeong Im, Eunbyeol Cho, Seongsu Bae, Jungwoo Oh, Gyubok Lee, Jong Hak Moon, Seng Chan You, Seungjin Baek, Chang Hoon Han, Yoon Bin Jung, Yohan Jo, Edward Choi
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HealthAlignSumm : Utilizing Alignment for Multimodal Summarization of Code-Mixed Healthcare Dialogues (2024.findings-emnlp)
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| Challenge: | Collaboration between doctors and AI scientists is leading to personalized models to stream-line healthcare tasks and improve productivity. |
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PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt Learning (2022.emnlp-main)
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| Challenge: | Existing methods for generating longitudinal multimodal EHRs are limited due to privacy concerns. |
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Data-Constrained Synthesis of Training Data for De-Identification (2025.acl-long)
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| Challenge: | sensitive domains lack widely available datasets due to privacy risks . recent studies have focused on evaluating the privacy of the synthetic text . |
| Approach: | They domain-adapt LLMs to clinical domain and generate synthetic clinical texts . they then generate NER models that can be annotated with tags for PII . |
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