# MedPT: A Massive Medical Question Answering Dataset for Brazilian-Portuguese Speakers

arXiv: https://arxiv.org/abs/2511.11878 · HTML: https://mlsanity.com/paper/2511.11878/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 41 references that are in the map.
- First posted on arXiv 14 Nov 2025.

## Builds on (most cited first, 13 of 13)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [gpt-oss-120b & gpt-oss-20b Model Card](https://mlsanity.com/paper/2508.10925/) (2025)
- [PubMedQA: A Dataset for Biomedical Research Question Answering](https://mlsanity.com/paper/1909.06146/) (2019)
- [Large language models in healthcare and medical domain: A review](https://mlsanity.com/paper/2401.06775/) (2023)
- [BioRED: A Rich Biomedical Relation Extraction Dataset](https://mlsanity.com/paper/2204.04263/) (2022)
- [MT-Clinical BERT: Scaling Clinical Information Extraction with Multitask Learning](https://mlsanity.com/paper/2004.10220/) (2020)
- [SemClinBr -- a multi institutional and multi specialty semantically annotated corpus for Portuguese clinical NLP tasks](https://mlsanity.com/paper/2001.10071/) (2020)
- [SPBERTQA: A Two-Stage Question Answering System Based on Sentence Transformers for Medical Texts](https://mlsanity.com/paper/2206.09600/) (2022)
- [Leveraging Online Data to Enhance Medical Knowledge in a Small Persian Language Model](https://mlsanity.com/paper/2505.16000/) (2025)
- [Adapting LLMs for the Medical Domain in Portuguese: A Study on Fine-Tuning and Model Evaluation](https://mlsanity.com/paper/2410.00163/) (2024)
- [A Unified Framework of Medical Information Annotation and Extraction for Chinese Clinical Text](https://mlsanity.com/paper/2203.03823/) (2022)

## Built on by (most cited first, 2 of 2)

- [Teaching LLMs Brazilian Healthcare: Injecting Knowledge from Official Clinical Guidelines](https://mlsanity.com/paper/2605.01077/) (2026)
- [MTEB-BR: A Text Embedding Benchmark for Brazilian Portuguese](https://mlsanity.com/paper/2607.04581/) (2026)

## Abstract (arXiv)

While large language models (LLMs) show transformative potential in healthcare, their development remains focused on high-resource languages. This creates a critical barrier for other languages, as simple translation fails to capture unique clinical and cultural nuances, such as endemic diseases. To address this, we introduce MedPT, the first large-scale, real-world corpus of patient-doctor interactions for the Brazilian Portuguese medical domain. Comprising 384,095 authentic question-answer pairs and covering over 3,200 distinct health-related conditions, the dataset was refined through a rigorous multi-stage curation protocol that employed a hybrid quantitative-qualitative analysis to filter noise and contextually enrich thousands of ambiguous queries, resulting in a corpus of approximately 57 million tokens. We further utilize of LLM-driven annotation to classify queries into seven semantic types to capture user intent. To validate MedPT's utility, we benchmark it in a medical specialty classification task: fine-tuning a 1.7B parameter model achieves an outstanding 94% F1-score on a 20-class setup. Furthermore, our qualitative error analysis shows misclassifications are not random but reflect genuine clinical ambiguities (e.g., between comorbid conditions), proving the dataset's deep semantic richness. We publicly release MedPT on Hugging Face to support the development of more equitable, accurate, and culturally-aware medical technologies for the Portuguese-speaking world.