# Synthetic Patient-Physician Dialogue Generation from Clinical Notes Using LLM

arXiv: https://arxiv.org/abs/2408.06285 · HTML: https://mlsanity.com/paper/2408.06285/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 12 Aug 2024.

## Builds on (most cited first, 4 of 4)

- [Texygen: A Benchmarking Platform for Text Generation Models](https://mlsanity.com/paper/1802.01886/) (2018)
- [End-to-End Knowledge-Routed Relational Dialogue System for Automatic Diagnosis](https://mlsanity.com/paper/1901.10623/) (2019)
- [NoteChat: A Dataset of Synthetic Doctor-Patient Conversations Conditioned on Clinical Notes](https://mlsanity.com/paper/2310.15959/) (2023)
- [Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation](https://mlsanity.com/paper/2012.11988/) (2020)

## Built on by (most cited first, 15 of 15)

- [A Survey on LLM-based Conversational User Simulation](https://mlsanity.com/paper/2604.24977/) (2026)
- [Large Language Model Benchmarks in Medical Tasks](https://mlsanity.com/paper/2410.21348/) (2024)
- [Enhancing the Medical Context-Awareness Ability of LLMs via Multifaceted Self-Refinement Learning](https://mlsanity.com/paper/2511.10067/) (2025)
- [Synthesis and Evaluation of Long-term History-aware Medical Dialogue](https://mlsanity.com/paper/2605.19766/) (2026)
- [ConvoGen: Enhancing Conversational AI with Synthetic Data: A Multi-Agent Approach](https://mlsanity.com/paper/2503.17460/) (2025)
- [EMSDialog: Synthetic Multi-person Emergency Medical Service Dialogue Generation from Electronic Patient Care Reports via Multi-LLM Agents](https://mlsanity.com/paper/2604.07549/) (2026)
- [Why Synthetic Isn't Real Yet: A Diagnostic Framework for Contact Center Dialogue Generation](https://mlsanity.com/paper/2508.18210/) (2025)
- [LLM-Based Data Generation and Clinical Skills Evaluation for Low-Resource French OSCEs](https://mlsanity.com/paper/2604.08126/) (2026)
- [Generating High Quality Synthetic Data for Dutch Medical Conversations](https://mlsanity.com/paper/2604.09645/) (2026)
- [Are LLMs Ready to Assist Physicians? PhysAssistBench for Interactive Doctor-Patient-EHR Assistance](https://mlsanity.com/paper/2606.18613/) (2026)
- [A French OSCE Dialogue Dataset and Controllable Virtual Patient System for Clinical Training](https://mlsanity.com/paper/2606.28526/) (2026)
- [Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies](https://mlsanity.com/paper/2608.05993/) (2026)
- [ELICITED: EHR-grounded Longitudinal Interactive Conversations for Information-seeking Triage Evaluation and Decision-making](https://mlsanity.com/paper/2608.09024/) (2026)
- [AnchorSIPS: A Synthetic Dataset and Evaluation Resource for Evidence-Supported Psychosis-Risk Symptom Measurement](https://mlsanity.com/paper/2608.12329/) (2026)
- [Measuring Cultural Alignment Beyond the Average: A Framework for Evaluating Maternal-Health LLM Interactions in Indian Contexts](https://mlsanity.com/paper/2610.11586/) (2026)

## Abstract (arXiv)

Medical dialogue systems (MDS) enhance patient-physician communication, improve healthcare accessibility, and reduce costs. However, acquiring suitable data to train these systems poses significant challenges. Privacy concerns prevent the use of real conversations, necessitating synthetic alternatives. Synthetic dialogue generation from publicly available clinical notes offers a promising solution to this issue, providing realistic data while safeguarding privacy. Our approach, SynDial, uses a single LLM iteratively with zero-shot prompting and a feedback loop to generate and refine high-quality synthetic dialogues. The feedback consists of weighted evaluation scores for similarity and extractiveness. The iterative process ensures dialogues meet predefined thresholds, achieving superior extractiveness as a result of the feedback loop. Additionally, evaluation shows that the generated dialogues excel in factuality metric compared to the baselines and has comparable diversity scores with GPT4.