# MedSyn: LLM-based Synthetic Medical Text Generation Framework

arXiv: https://arxiv.org/abs/2408.02056 · HTML: https://mlsanity.com/paper/2408.02056/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 47 references that are in the map.
- First posted on arXiv 4 Aug 2024.

## Builds on (most cited first, 24 of 24)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [The Internal State of an LLM Knows When It's Lying](https://mlsanity.com/paper/2304.13734/) (2023)
- [BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining](https://mlsanity.com/paper/2210.10341/) (2022)
- [ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission](https://mlsanity.com/paper/1904.05342/) (2019)
- [HuatuoGPT, towards Taming Language Model to Be a Doctor](https://mlsanity.com/paper/2305.15075/) (2023)
- [Generating Multi-label Discrete Patient Records using Generative Adversarial Networks](https://mlsanity.com/paper/1703.06490/) (2017)
- [A Study of Generative Large Language Model for Medical Research and Healthcare](https://mlsanity.com/paper/2305.13523/) (2023)
- [Does Synthetic Data Generation of LLMs Help Clinical Text Mining?](https://mlsanity.com/paper/2303.04360/) (2023)
- [Lessons from Natural Language Inference in the Clinical Domain](https://mlsanity.com/paper/1808.06752/) (2018)
- [emrQA: A Large Corpus for Question Answering on Electronic Medical Records](https://mlsanity.com/paper/1809.00732/) (2018)
- [DoctorGLM: Fine-tuning your Chinese Doctor is not a Herculean Task](https://mlsanity.com/paper/2304.01097/) (2023)
- [Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language](https://mlsanity.com/paper/1905.07213/) (2019)
- [Summarizing, Simplifying, and Synthesizing Medical Evidence Using GPT-3 (with Varying Success)](https://mlsanity.com/paper/2305.06299/) (2023)
- [Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification](https://mlsanity.com/paper/2304.13998/) (2023)
- [RuMedBench: A Russian Medical Language Understanding Benchmark](https://mlsanity.com/paper/2201.06499/) (2022)
- [Incorporating Domain Knowledge into Medical NLI using Knowledge Graphs](https://mlsanity.com/paper/1909.00160/) (2019)
- [BioLORD-2023: Semantic Textual Representations Fusing LLM and Clinical Knowledge Graph Insights](https://mlsanity.com/paper/2311.16075/) (2023)
- [RuBioRoBERTa: a pre-trained biomedical language model for Russian language biomedical text mining](https://mlsanity.com/paper/2204.03951/) (2022)

## Built on by (most cited first, 19 of 19)

- [3MDBench: Medical Multimodal Multi-agent Dialogue Benchmark](https://mlsanity.com/paper/2504.13861/) (2025)
- [A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs](https://mlsanity.com/paper/2504.14657/) (2025)
- [OjaKV: Context-Aware Online Low-Rank KV Cache Compression](https://mlsanity.com/paper/2509.21623/) (2025)
- [Method-Based Reasoning for Large Language Models: Extraction, Reuse, and Continuous Improvement](https://mlsanity.com/paper/2508.04289/) (2025)
- [Enhancing disease detection in radiology reports through fine-tuning lightweight LLM on weak labels](https://mlsanity.com/paper/2409.16563/) (2024)
- [Knowledge Hierarchy Guided Biological-Medical Dataset Distillation for Domain LLM Training](https://mlsanity.com/paper/2501.15108/) (2025)
- [Leveraging LLMs for User Stories in AI Systems: UStAI Dataset](https://mlsanity.com/paper/2504.00513/) (2025)
- [A Severity-Based Curriculum Learning Strategy for Arabic Medical Text Generation](https://mlsanity.com/paper/2604.06365/) (2026)
- [Enhancing Patient-Centric Communication: Leveraging LLMs to Simulate Patient Perspectives](https://mlsanity.com/paper/2501.06964/) (2025)
- [Knowledge-Driven Agentic Scientific Corpus Distillation Framework for Biomedical Large Language Models Training](https://mlsanity.com/paper/2504.19565/) (2025)
- [Evaluating the Pre-Consultation Ability of LLMs using Diagnostic Guidelines](https://mlsanity.com/paper/2601.03627/) (2026)
- [Cleansing the Artificial Mind: A Self-Reflective Detoxification Framework for Large Language Models](https://mlsanity.com/paper/2601.11776/) (2026)
- [An Integrated Approach to AI-Generated Content in e-health](https://mlsanity.com/paper/2501.16348/) (2025)
- [ProKG-Dial: Progressive Multi-Turn Dialogue Construction with Domain Knowledge Graphs](https://mlsanity.com/paper/2508.01869/) (2025)
- [Synthetic medical data generation: state of the art and application to trauma mechanism classification](https://mlsanity.com/paper/2508.02771/) (2025)
- [DP-MGTD: Privacy-Preserving Machine-Generated Text Detection via Adaptive Differentially Private Entity Sanitization](https://mlsanity.com/paper/2601.04641/) (2026)
- [Generalistic or Specific Embeddings, Which is Better? An Empirical Study on Search for Clinical Coding in Non-English Languages](https://mlsanity.com/paper/2605.30529/) (2026)
- [A Self Consistency Based Reranking for Narrative Question Answering](https://mlsanity.com/paper/2606.15741/) (2026)
- [Dissecting Neuro-Symbolic Quality Assurance for Synthetic Oncology Data Generation](https://mlsanity.com/paper/2608.22085/) (2026)

## Abstract (arXiv)

Generating synthetic text addresses the challenge of data availability in privacy-sensitive domains such as healthcare. This study explores the applicability of synthetic data in real-world medical settings. We introduce MedSyn, a novel medical text generation framework that integrates large language models with a Medical Knowledge Graph (MKG). We use MKG to sample prior medical information for the prompt and generate synthetic clinical notes with GPT-4 and fine-tuned LLaMA models. We assess the benefit of synthetic data through application in the ICD code prediction task. Our research indicates that synthetic data can increase the classification accuracy of vital and challenging codes by up to 17.8% compared to settings without synthetic data. Furthermore, to provide new data for further research in the healthcare domain, we present the largest open-source synthetic dataset of clinical notes for the Russian language, comprising over 41k samples covering 219 ICD-10 codes.