The study investigates whether cross‑lingual clinical annotation projection can be treated as a constrained text‑generation task that preserves the original text while inserting entity tags. Using a workflow that embeds tags directly into immutable target‑language text and then validates them deterministically, the authors evaluated this approach against supervised candidate‑span projection and hybrid ML‑LLM refinement across six languages. Results show that direct LLM projection, particularly with GLM 5.2 and Gemma4:31B, achieves the highest strict F1 scores (up to 0.9201) and outperforms previous methods by 0.0564–0.1512, producing over 55,000 grounded mentions with accurate offsets.
By \'Alvaro Rey-Blanes, Francisco J. Moreno-Barea, Francisco J. Veredas
The study investigates whether cross‑lingual clinical annotation can be treated as a constrained text‑generation task that preserves the original text while inserting entity tags. Using a workflow that embeds tags directly into immutable target‑language text and then validates them deterministically, the authors compare this approach to supervised candidate‑span projection and hybrid ML‑LLM refinement across six languages. Results show that direct LLM projection, particularly with GLM 5.2 and Gemma4:31B, achieves the highest strict F1 scores, surpassing previous state‑of‑the‑art by up to 0.15 and producing over 55,000 grounded mentions with accurate offsets.
arXiv:2611.07940v1 Announce Type: cross
Abstract: Background: Cardiovascular care generates large volumes of clinical free-text documentation in local languages. However, clinical natural language pr...
By Manuela-Daniela Danu, Miguel Rodr\'iguez-Ortega, Alberto Becerra-Tome, Pablo Arancibia, Fernando Gallego-Donoso, Lorenzo Pratesi, Xi Bai, Andrei-Damian Dinescu, Salvador Lima-L\'opez, Andrea Guala, Saskia Haitjema, Jonas Mack, Michele Miranda, Wesam Al Nabki, Jan Rodr\'iguez-Miret, Vasile George Marica, Vladim\'ir Kincl, Kate\v{r}ina Dost\'alov\'a, Michael Andrej, Alicia Ujil, Corinne Allaart, Machteld Boonstra, Malin Overmars, Anoop Shah, Giovanna Ceroni, Jonathan Kossoff, Domenico D'Amario, Renzo Laborante, Antonio Iaconelli, Isabella Fumarulo, Jacopo Lenkowicz, Livia Lilli, Emma Weiss, Alex Scafa, Felix Lindberg, Milou Gamage, Eul\`alia Farr\'e-Maduell, S\'ebastien Brati\`eres, Martin Krallinger, Bram van Es
arXiv:2609.12544v1 Announce Type: new
Abstract: Clinical de-identification relies on accurately identifying personally identifiable information (PII). However, manually annotated datasets are costly...
By Linh Uyen Le, Christian Hoang, Huy Hoang Ha
arXiv:2606. 12569v1 Announce Type: cross Abstract: We present EDEN (Emergency Department Electronic Notes), a new and unique large-scale corpus of clinical notes produced in Emergency Departments of Italian hospitals.
By Tiziano Labruna, Guido Bertolini, Pietro Ferrazzi, Bernardo Magnini
arXiv:2606. 19640v1 Announce Type: cross Abstract: AI and large language models (LLMs) have emerged as promising tools to address global mental health challenges.
By Yunkai Xu, Saeed Abdullah
This thesis explores how to select and adapt NLP models for global health literature when annotated data and computational resources are scarce. It compares skip‑gram word2vec models trained on increasingly large specialized corpora with BioWordVec for semantic tag discovery, finding that larger coverage does not always yield more useful domain associations. The study also evaluates convolutional spaCy models versus a RoBERTa transformer for named entity recognition, noting a trade‑off between higher F1 scores and longer inference time, and investigates MiniLM few‑shot versus BART‑MNLI zero‑shot classification for multi‑label topic classification, highlighting practical constraints of inference cost.
"whyItMatters":"The work provides empirical guidance on balancing model accuracy and resource demands for building knowledge systems in low‑resource global health settings."
By Genis Skura, Antoine Geissb\"uhler, Jean-Luc Falcone
This study benchmarks transformer models for Bangla medical named entity recognition (NER), comparing BanglaBERT, multilingual BERT (mBERT), XLM‑RoBERTa, and GPT‑4o mini under zero‑shot and few‑shot prompting. Across a full test set of 3,179 samples, fine‑tuned XLM‑RoBERTa achieves a new state‑of‑the‑art F1‑score of 0.5959, while BanglaBERT lags with 0.4937, suggesting that domain diversity outweighs language specificity. The analysis shows high performance on Medicine and Specialist entities (F1 > 0.83) but lower accuracy on Symptoms (F1 0.4367), and demonstrates that fine‑tuned transformers outperform prompt‑only approaches by a factor of 3.76.
By Rakib Abdullah, Md. Maruful Islam Maruf
The paper introduces a method for generating multilingual reasoning traces for medical question answering using large language models. It creates 500,000 reasoning traces in English, Italian, and Spanish by retrieving medical information from Wikipedia and applies them to MedQA and MedMCQA datasets extended into Italian and Spanish. The approach improves performance in both few‑shot in‑context learning and supervised fine‑tuning, achieving state‑of‑the‑art results for 8B‑parameter LLMs and releasing all resources for further research.
By Pietro Ferrazzi, Aitor Soroa, Rodrigo Agerri
arXiv:2606. 12569v2 Announce Type: replace-cross Abstract: We present eCream-MedCorpus, a new and unique large-scale dataset of clinical notes produced in Emergency Departments of Italian hospitals.
By Tiziano Labruna, Guido Bertolini, Pietro Ferrazzi, Bernardo Magnini
arXiv:2606. 12569v3 Announce Type: replace-cross Abstract: We present eCREAM-MedCorpus, a new and unique large-scale dataset of clinical notes produced in Emergency Departments of Italian hospitals.
By Tiziano Labruna, Guido Bertolini, Pietro Ferrazzi, Bernardo Magnini
arXiv:2608.22363v1 Announce Type: new
Abstract: Medical visual question answering (VQA) is a crucial task in clinical AI, yet its evaluation has so far centered almost exclusively on English, limitin...
By Jingbo Wang, Sendong Zhao, Haochun Wang, Bing Qin, Ting Liu