Guardians of the data: NER and LLMs for effective medical record anonymization in Brazilian Portuguese

To the best of our knowledge, <i>AnonyMed-BR</i> is the first manually annotated anonymization dataset for Brazilian Portuguese medical texts, enabling systematic evaluation of both extractive and generative models. The dataset and methodology establish a foundation for privacy-preserving NLP research in the Brazilian healthcare context and the good performance achieved by all models demonstrates the feasibility of developing reliable anonymization systems for Brazilian clinical data. Importantly, the ability to anonymize sensitive information opens opportunities to create new datasets and train models for a variety of downstream tasks in the medical domain, such as clinical outcome prediction, medical entity recognition, diagnostic support, and patient stratification, fostering the growth of NLP research for Brazilian Portuguese healthcare texts. Motivated by our findings, future work includes a deeper exploration of synthetic data generation and utilization. Additionally, we plan to evaluate the models across different languages and textual domains, and to expand the dataset to cover these new languages and domains. These efforts aim to develop more complex anonymization systems with higher generalization capability, ultimately enabling broader applications and safer sharing of data in diverse research and operational settings. All resources are publicly available at https://github.com/venturusbr/AnonyMED-BR.