The abundance of medical records holds great promise for enhancing healthcare and advancing biomedical research. However, due to privacy constraints, access to such data is typically limited to internal use.Recent studies have attempted to overcome this challenge by generating synthetic data through Causal Language Modelling. Yet, this approach often fails to ensure patient anonymity and offers limited control over output diversity—unless additional computational cost is introduced.In response, we propose a method for generating synthetic free-text medical records based on Masked Language Modelling. Our approach retains key medical details while introducing variability in the generated texts and reducing the risk of patient re-identification. With a relatively lightweight architecture of approximately 120 million parameters, the system ensures low inference costs.Experimental results show that our method produces high-quality synthetic data, achieving a HIPAA-compliant PHI recall of 96% and a re-identification risk of only 3.5%. Furthermore, downstream evaluations reveal that models trained on the synthetic data perform comparably to those trained on real-world data. Our trained models are publicly available on Github as SynDeidMLM (at https://github.com/SamySam0/SynDeidMLM) (meaning synthetic and de-identified data generation using MLM).

Due to the sensitive nature of clinical letters, their use in model training, medical research, and education is limited. This work aims to generate diverse, de-identified, and high-quality synthetic clinical letters to enhance privacy protection. This study explores various pre-trained language models (PLMs) for text masking and generation, employing various masking strategies with a focus on Bio_ClinicalBERT. Both qualitative and quantitative methods are used for evaluation, supplemented by a downstream Named Entity Recognition (NER) task. Our results indicate that encoder-only models outperform encoder-decoder models. General-domain and clinical-domain PLMs exhibit comparable performance when clinical information is preserved. Preserving clinical entities and document structure yields better performance than fine-tuning alone. Masking stopwords enhances text quality, whereas masking nouns or verbs has a negative impact. BERTScore proves to be the most reliable quantitative evaluation metric in our task. Contextual information has minimal impact, indicating that synthetic letters can effectively replace original ones in downstream tasks. Unlike previous studies that focus primarily on reconstructing original letters or training a privacy-detection and substitution model, this project provides a framework for generating diverse clinical letters while embedding privacy detection, enabling sensitive dataset expansion and facilitating the use of real-world clinical data. Our codes and trained models will be publicly available at https://github.com/HECTA-UoM/Synthetic4Health.