mlsanityData to 14 Oct 2026

CasiMedicos-Arg: A Medical Question Answering Dataset Annotated with Explanatory Argumentative Structures

arXiv 2410.05235 ↗First posted 7 Oct 2024cs.CL cs.AIcited by 11

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Abstract (arXiv)

Explaining Artificial Intelligence (AI) decisions is a major challenge nowadays in AI, in particular when applied to sensitive scenarios like medicine and law. However, the need to explain the rationale behind decisions is a main issue also for human-based deliberation as it is important to justify textitwhy a certain decision has been taken. Resident medical doctors for instance are required not only to provide a (possibly correct) diagnosis, but also to explain how they reached a certain conclusion. Developing new tools to aid residents to train their explanation skills is therefore a central objective of AI in education. In this paper, we follow this direction, and we present, to the best of our knowledge, the first multilingual dataset for Medical Question Answering where correct and incorrect diagnoses for a clinical case are enriched with a natural language explanation written by doctors. These explanations have been manually annotated with argument components (i.e., premise, claim) and argument relations (i.e., attack, support), resulting in the Multilingual CasiMedicos-Arg dataset which consists of 558 clinical cases in four languages (English, Spanish, French, Italian) with explanations, where we annotated 5021 claims, 2313 premises, 2431 support relations, and 1106 attack relations. We conclude by showing how competitive baselines perform over this challenging dataset for the argument mining task.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 29 of its 41 references that are in the map. First posted on arXiv 7 Oct 2024.

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29 of 41 references.

References · 29

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 5; 2026: 5.

11 papers.

  1. 2024A Benchmark for Long-Form Medical Question Answeringcited by 25
  2. 2025Large Language Models in Argument Mining: A Surveycited by 14
  3. 2025Dynamic Knowledge Integration for Evidence-Driven Counter-Argument Generation with Large Language Modelscited by 3
  4. 2026Small LLMs for Medical NLP: a Systematic Analysis of Few-Shot, Constraint Decoding, Fine-Tuning and Continual Pre-Training in Italiancited by 3
  5. 2025Continually Self-Improving Language Models for Bariatric Surgery Question--Answeringcited by 1
  6. 2026Toward Automatic Filling of Case Report Forms: A Case Study on Data from an Italian Emergency Departmentcited by 1
  7. 2026DiagnosticIQ: A Benchmark for LLM-Based Industrial Maintenance Action Recommendation from Symbolic Rulescited by 1
  8. 2025PerMedCQA: Benchmarking Large Language Models on Medical Consumer Question Answering in Persian Languagecited by 0
  9. 2025Investigating LLM Capabilities on Long Context Comprehension for Medical Question Answeringcited by 0
  10. 2026Evaluating LLMs' Effectiveness on Real-World Consumer Device Repair Questionscited by 0
  11. 2026MIRA-Ev:A Benchmark for Granular Evidence Detection and Relational Reasoning in Clinical Examscited by 0