4 citations · 6 across the 8 of their papers we have counts for
6 papers · 1 filter
MIRA-Ev:A Benchmark for Granular Evidence Detection and Relational Reasoning in Clinical Exams
Iker De la Iglesia, Johanna Ramirez-Romero, Jose Maria Villa-Gonzalez +3
Clinical NLP evaluation remains dominated by multiple-choice question answering (MCQA), which scores only final-answer accuracy and cannot detect when a model reaches the correct d…
To Adapt or not to Adapt, Rethinking the Value of Medical Knowledge-Aware Large Language Models
Ane G. Domingo-Aldama, Iker De La Iglesia, Maitane Urruela +2
BACKGROUND: Recent studies have shown that domain-adapted large language models (LLMs) do not consistently outperform general-purpose counterparts on standard medical benchmarks, r…
ArgHiTZ at ArchEHR-QA 2025: A Two-Step Divide and Conquer Approach to Patient Question Answering for Top Factuality
Adrián Cuadrón, Aimar Sagasti, Maitane Urruela +5
This work presents three different approaches to address the ArchEHR-QA 2025 Shared Task on automated patient question answering. We introduce an end-to-end prompt-based baseline a…
Explanatory Argument Extraction of Correct Answers in Resident Medical Exams
Iakes Goenaga, Aitziber Atutxa, Koldo Gojenola +2
Developing the required technology to assist medical experts in their everyday activities is currently a hot topic in the Artificial Intelligence research field. Thus, a number of…
EriBERTa: A Bilingual Pre-Trained Language Model for Clinical Natural Language Processing
Iker de la Iglesia, Aitziber Atutxa, Koldo Gojenola +1
The utilization of clinical reports for various secondary purposes, including health research and treatment monitoring, is crucial for enhancing patient care. Natural Language Proc…
HiTZ@Antidote: Argumentation-driven Explainable Artificial Intelligence for Digital Medicine
Rodrigo Agerri, Iñigo Alonso, Aitziber Atutxa +9
Providing high quality explanations for AI predictions based on machine learning is a challenging and complex task. To work well it requires, among other factors: selecting a prope…