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SynthMedic: Utilizing large language models for synthetic discharge summary generation, correction and validation.

Georgi Grazhdanski¹, Vasil Vasilev², Sylvia Vassileva¹

¹Faculty of Mathematics and Informatics, Sofia University St. Kliment Ohridski, 5 James Bourchier Blvd., Sofia, 1164, Bulgaria.

Journal of Biomedical Informatics

|September 17, 2025

Summary

Researchers developed a method to create synthetic clinical discharge summaries using large language models (LLMs). This approach ensures data privacy and can be used to train AI without real patient information.

Keywords:

Artificial intelligence Clinical text Human evaluation Knowledge graphs Large language models Synthetic data

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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Published on: June 13, 2025

Area of Science:

Medical Informatics
Natural Language Processing
Artificial Intelligence in Healthcare

Background:

Synthetic clinical texts offer a privacy-preserving alternative to real patient data for training AI models.
Current methods for generating synthetic medical data face challenges in ensuring factual accuracy and clinical relevance.
The use of synthetic data can enhance transparency, reduce bias, and lower costs in medical AI development.

Purpose of the Study:

To develop and validate a methodology for generating, validating, and correcting synthetic discharge summaries using LLMs.
To create a high-quality, publicly available corpus of synthetic discharge summaries for training machine learning models.
To ensure the medical factual correctness and clinical credibility of synthetic discharge summaries.

Main Methods:

Utilized a large language model (LLM) to generate synthetic discharge summaries based on specific diseases and medical references (Merck Manuals).
Employed both LLM-based and human expert validation to assess the quality and accuracy of the generated summaries.
Implemented a Knowledge Graph-based approach for automatic correction to ensure medical factual accuracy.

Main Results:

Human expert evaluation confirmed the credibility and factual accuracy of the synthetic discharge summaries when grounded with medical references.
Achieved a System Usability Score of 94.35% from medical professionals and a 93.65% Faithfulness score from an LLM.
The generated corpus includes 900 synthetic discharge summaries for nine significant diseases.

Conclusions:

The proposed methodology effectively generates high-quality synthetic discharge summaries suitable for training AI models.
The publicly available corpus and methodology empower the research community to develop advanced medical AI without using sensitive patient data.
This work facilitates the development of AI tools to support healthcare professionals in their daily tasks.