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

• URL: http://arxiv.org/abs/2511.11878v1
• Date: Fri, 14 Nov 2025 21:13:28 GMT
• Title: MedPT: A Massive Medical Question Answering Dataset for Brazilian-Portuguese Speakers
• Authors: Fernanda Bufon Färber, Iago Alves Brito, Julia Soares Dollis, Pedro Schindler Freire Brasil Ribeiro, Rafael Teixeira Sousa, Arlindo Rodrigues Galvão Filho,
• Abstract summary: We introduce MedPT, the first large-scale, real-world corpus for Brazilian Portuguese.<n>It comprises 384,095 authentic question-answer pairs from patient-doctor interactions.<n>Our analysis reveals its thematic breadth (3,200 topics) and unique linguistic properties, like the natural asymmetry in patient-doctor communication.
• Score: 35.41469674626373
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While large language models (LLMs) show transformative potential in healthcare, their development remains focused on high-resource languages, creating a critical barrier for others 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 for Brazilian Portuguese, comprising 384,095 authentic question-answer pairs from patient-doctor interactions. The dataset underwent a meticulous multi-stage curation protocol, using a hybrid quantitative-qualitative analysis to filter noise and contextually enrich thousands of ambiguous queries. We further augmented the corpus via LLM-driven annotation, classifying questions into seven semantic types to capture user intent. Our analysis reveals its thematic breadth (3,200 topics) and unique linguistic properties, like the natural asymmetry in patient-doctor communication. To validate its utility, we benchmark a medical specialty routing 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 to foster the development of more equitable, accurate, and culturally-aware medical technologies for the Portuguese-speaking world.

Related papers

• IMB: An Italian Medical Benchmark for Question Answering [11.555285143713315]
  We present two comprehensive Italian medical benchmarks: textbfIMB-QA, containing 782,644 patient-doctor conversations from 77 medical categories, and textbfIMB-MCQA, comprising 25,862 multiple-choice questions from medical specialty examinations.<n>We demonstrate how Large Language Models (LLMs) can be leveraged to improve the clarity and consistency of medical forum data while retaining their original meaning and conversational style.<n>Our experiments with Retrieval Augmented Generation (RAG) and domain-specific fine-tuning reveal that specialized adaptation strategies can outperform larger, general-purpose models in medical question
  arXiv  Detail & Related papers  (2025-10-21T09:45:59Z)
• Arabic Large Language Models for Medical Text Generation [0.5483130283061118]
  This study proposes an approach that fine-tunes large language models (LLMs) for Arabic medical text generation.<n>The system is designed to assist patients by providing accurate medical advice, diagnoses, drug recommendations, and treatment plans based on user input.
  arXiv  Detail & Related papers  (2025-09-12T09:37:26Z)
• MedQARo: A Large-Scale Benchmark for Medical Question Answering in Romanian [50.767415194856135]
  We introduce MedQARo, the first large-scale medical QA benchmark in Romanian.<n>We construct a high-quality and large-scale dataset comprising 102,646 QA pairs related to cancer patients.
  arXiv  Detail & Related papers  (2025-08-22T13:48:37Z)
• Reinforced Correlation Between Vision and Language for Precise Medical AI Assistant [11.187690318227514]
  RCMed is a full-stack AI assistant that improves multimodal alignment in both input and output.<n>It achieves state-of-the-art precision in contextualizing irregular lesions and subtle anatomical boundaries.
  arXiv  Detail & Related papers  (2025-05-06T10:00:08Z)
• Conversation AI Dialog for Medicare powered by Finetuning and Retrieval Augmented Generation [0.0]
  Large language models (LLMs) have shown impressive capabilities in natural language processing tasks, including dialogue generation.<n>This research aims to conduct a novel comparative analysis of two prominent techniques, fine-tuning with LoRA and the Retrieval-Augmented Generation framework.
  arXiv  Detail & Related papers  (2025-02-04T11:50:40Z)
• PMC-LLaMA: Towards Building Open-source Language Models for Medicine [62.39105735933138]
  Large Language Models (LLMs) have showcased remarkable capabilities in natural language understanding. LLMs struggle in domains that require precision, such as medical applications, due to their lack of domain-specific knowledge. We describe the procedure for building a powerful, open-source language model specifically designed for medicine applications, termed as PMC-LLaMA.
  arXiv  Detail & Related papers  (2023-04-27T18:29:05Z)
• Cross-lingual Argument Mining in the Medical Domain [6.0158981171030685]
  We show how to perform Argument Mining (AM) in medical texts for which no annotated data is available. Our work shows that automatically translating and projecting annotations (data-transfer) from English to a given target language is an effective way to generate annotated data. We also show how the automatically generated data in Spanish can also be used to improve results in the original English monolingual setting.
  arXiv  Detail & Related papers  (2023-01-25T11:21:12Z)
• Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of Code-Mixed Clinical Texts [56.72488923420374]
  Pre-trained language models (LMs) have shown great potential for cross-lingual transfer in low-resource settings. We show the few-shot cross-lingual transfer property of LMs for named recognition (NER) and apply it to solve a low-resource and real-world challenge of code-mixed (Spanish-Catalan) clinical notes de-identification in the stroke.
  arXiv  Detail & Related papers  (2022-04-10T21:46:52Z)
• Self-supervised Answer Retrieval on Clinical Notes [68.87777592015402]
  We introduce CAPR, a rule-based self-supervision objective for training Transformer language models for domain-specific passage matching. We apply our objective in four Transformer-based architectures: Contextual Document Vectors, Bi-, Poly- and Cross-encoders. We report that CAPR outperforms strong baselines in the retrieval of domain-specific passages and effectively generalizes across rule-based and human-labeled passages.
  arXiv  Detail & Related papers  (2021-08-02T10:42:52Z)
• Benchmarking Automated Clinical Language Simplification: Dataset, Algorithm, and Evaluation [48.87254340298189]
  We construct a new dataset named MedLane to support the development and evaluation of automated clinical language simplification approaches. We propose a new model called DECLARE that follows the human annotation procedure and achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-12-04T06:09:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.