This study presents a fine-tuning of the BERTimbau model for Named Entity Recognition (NER) in Electronic Health Records (EHR), focusing on oncology data in Brazilian Portuguese. The proposed approach employs an annotated dataset formatted in the Inside-Outside-Beginning (IOB) scheme, enabling precise extraction of clinical entities. The model was trained using the BERTimbau Base Tokenizer, with hyperparameters optimized through the Hugging Face library. Experimental results demonstrate robust performance, achieving F1scores between 0.83 and 0.85 across disease, procedure, and medication categories. When applied to a dataset of 125,825 new clinical records, the model achieved a correct identification rate exceeding 92%, underscoring its potential for medical decision support. Key challenges include enhancing classification accuracy for less frequent entities and adapting the model to diverse clinical contexts. Future directions involve dataset expansion, data augmentation techniques, exploration of more advanced transformer-based models, and integration into hospital decision support systems to further improve healthcare analytics.

The increasing volume of electronic health records (EHRs) in oncology presents a challenge for medical professionals who need to quickly and efficiently extract relevant patient information. Large Language Models (LLMs) have demonstrated potential in automating this process, reducing the cognitive load on healthcare providers while improving the quality and speed of clinical decision-making. This study presents an automatic summarization system for oncology patients’ EHRs, leveraging LLaMA 3 to generate concise and coherent medical summaries. By employing prompt engineering techniques, our approach structures the extraction of key clinical insights into an easily interpretable format, such as diagnoses, treatments, and test results. The system was validated on a dataset of anonymized medical records, demonstrating its capability to improve consultation efficiency and reduce redundancy in manual reviews.

In recent years, artificial intelligence (AI) has enabled continuous advances in medicine, playing a crucial role in the evolution of cancer research. One of the AI techniques widely used in this context is Natural Language Processing (NLP) due to its ability to extract information from unstructured documents such as clinical notes and progress reports. These notes can contain valuable data about the type of cancer and information that can be essential for improving the quality of patient treatment, providing data for research, and offering meaningful figures for health service managers, among other uses. This study aims to present a systematic literature review to investigate and synthesize technological advances in applying NLP in medicine, particularly in cancer diagnosis. It identifies gaps, challenges, and potential strategies to accelerate the diagnosis and treatment of cancer disease using AI.