Georgi Grazhdanski , Vasil Vasilev , Sylvia Vassileva , Dimitar Taskov , Izabel Antova , Ivan Koychev , Svetla Boytcheva
{"title":"SynthMedic: Utilizing large language models for synthetic discharge summary generation, correction and validation","authors":"Georgi Grazhdanski , Vasil Vasilev , Sylvia Vassileva , Dimitar Taskov , Izabel Antova , Ivan Koychev , Svetla Boytcheva","doi":"10.1016/j.jbi.2025.104906","DOIUrl":null,"url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Synthetic clinical texts can improve transparency and reduce bias and costs when training and evaluating specialized language models in the medical domain. Synthetic texts are freely shareable, as they contain no real patient information, and can be customized for a specific task. The objective of this study is to develop a methodology for generating, validating, and correcting synthetic discharge summaries using LLMs without requiring any real patient data.</div></div><div><h3>Methods:</h3><div>The proposed approach uses an LLM to generate synthetic discharge summaries for specific diseases and standard medical references from Merck Manuals to ground the generation in internationally accepted medical practices. We validate the generated summaries using LLMs as well as by human expert validation. In addition, we propose a method for automatic correction of the generated discharge summaries using Knowledge Graphs to ensure medical factual correctness.</div></div><div><h3>Results:</h3><div>The conducted human expert evaluation shows that the generated synthetic discharge summaries are credible and factually accurate when provided with the medical reference context. The generated summaries achieve a System Usability Score of 94.35% based on a comprehensive rubric evaluated by medical professionals and a score of 93.65% on the Faithfulness metric evaluated by an LLM.</div></div><div><h3>Conclusions:</h3><div>The proposed methodology can be utilized to generate high-quality synthetic discharge summaries for various diseases. The generated synthetic corpus consists of 900 discharge summaries in English representing nine socially significant diseases and is publicly available under an open license. The community can take advantage of the corpus and proposed methodology to train complex machine learning models, helping medical professionals in their daily work without using real patient data.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"170 ","pages":"Article 104906"},"PeriodicalIF":4.5000,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Biomedical Informatics","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1532046425001352","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Background and Objective:
Synthetic clinical texts can improve transparency and reduce bias and costs when training and evaluating specialized language models in the medical domain. Synthetic texts are freely shareable, as they contain no real patient information, and can be customized for a specific task. The objective of this study is to develop a methodology for generating, validating, and correcting synthetic discharge summaries using LLMs without requiring any real patient data.
Methods:
The proposed approach uses an LLM to generate synthetic discharge summaries for specific diseases and standard medical references from Merck Manuals to ground the generation in internationally accepted medical practices. We validate the generated summaries using LLMs as well as by human expert validation. In addition, we propose a method for automatic correction of the generated discharge summaries using Knowledge Graphs to ensure medical factual correctness.
Results:
The conducted human expert evaluation shows that the generated synthetic discharge summaries are credible and factually accurate when provided with the medical reference context. The generated summaries achieve a System Usability Score of 94.35% based on a comprehensive rubric evaluated by medical professionals and a score of 93.65% on the Faithfulness metric evaluated by an LLM.
Conclusions:
The proposed methodology can be utilized to generate high-quality synthetic discharge summaries for various diseases. The generated synthetic corpus consists of 900 discharge summaries in English representing nine socially significant diseases and is publicly available under an open license. The community can take advantage of the corpus and proposed methodology to train complex machine learning models, helping medical professionals in their daily work without using real patient data.
期刊介绍:
The Journal of Biomedical Informatics reflects a commitment to high-quality original research papers, reviews, and commentaries in the area of biomedical informatics methodology. Although we publish articles motivated by applications in the biomedical sciences (for example, clinical medicine, health care, population health, and translational bioinformatics), the journal emphasizes reports of new methodologies and techniques that have general applicability and that form the basis for the evolving science of biomedical informatics. Articles on medical devices; evaluations of implemented systems (including clinical trials of information technologies); or papers that provide insight into a biological process, a specific disease, or treatment options would generally be more suitable for publication in other venues. Papers on applications of signal processing and image analysis are often more suitable for biomedical engineering journals or other informatics journals, although we do publish papers that emphasize the information management and knowledge representation/modeling issues that arise in the storage and use of biological signals and images. System descriptions are welcome if they illustrate and substantiate the underlying methodology that is the principal focus of the report and an effort is made to address the generalizability and/or range of application of that methodology. Note also that, given the international nature of JBI, papers that deal with specific languages other than English, or with country-specific health systems or approaches, are acceptable for JBI only if they offer generalizable lessons that are relevant to the broad JBI readership, regardless of their country, language, culture, or health system.