arXiv:2608. 19981v1 Announce Type: new Abstract: We present HealMed, an expert-reviewed benchmark for multilingual evaluation of large language models in medicine.
arXiv:2606. 24200v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) in clinical settings increasingly requires multilingual retrieval against predominantly English evidence corpora.
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...
arXiv:2511.00421v2 Announce Type: replace-cross Abstract: Large language models (LLMs) show promise in medical applications, but their ability to detect and correct errors in clinical texts remains u...
arXiv:2606. 07853v1 Announce Type: cross Abstract: Large Language Models are transforming the support for clinical decision and their application in real scenarios.
The study compared the performance of the AI system Doctorina, eight physicians, and four frontier language models on 150 synthetic Polish primary‑care consultations. Doctorina achieved 82.0% top‑1 concordance, outperforming physicians (57.0%) by 25 percentage points, and also led in diagnostic workup and treatment scores. A second run confirmed these advantages across all measured outcomes.
The paper examines how multilingual medical adaptation affects the internal representations of Whisper ASR models. By comparing various fine‑tuning strategies—zero‑shot decoding, English‑only, German‑only, two‑stage EN→EN+DE, and direct EN+DE fine‑tuning—it shows that fine‑tuning significantly improves performance, with the best model varying by setting. Layer‑wise encoder analysis reveals that English medical fine‑tuning drives the main representation shift, while multilingual continuation largely preserves the adapted space, and that domain and language signals remain recoverable across layers.
The paper examines how multilingual medical adaptation affects the internal representations of Whisper ASR models by performing layer‑wise encoder analysis. It compares several adaptation strategies—zero‑shot decoding, English‑only fine‑tuning, German‑only diagnostic fine‑tuning, two‑stage EN→EN+DE continuation, and direct EN+DE fine‑tuning—across different Whisper sizes, finding that fine‑tuning improves performance but the best model varies by setting. Layer‑wise results show that English medical fine‑tuning drives the main encoder shift, while multilingual continuation largely preserves the adapted representation space, with domain and language information remaining recoverable across layers. "whyItMatters":"The study provides insight into how multilingual medical adaptation reshapes Whisper’s internal representations, guiding the selection of model sizes and fine‑tuning strategies for improved MedASR performance."
arXiv:2606. 01904v1 Announce Type: cross Abstract: The increasing application of Natural Language Processing (NLP) in healthcare demands language models specifically attuned to the complexities of clinical language.
arXiv:2609.14819v1 Announce Type: cross Abstract: Large language models (LLMs) have a growing range of applications in medicine, and their evaluation is critical for ensuring they provide benefit and...
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.
arXiv:2604. 18347v2 Announce Type: replace-cross Abstract: Vision Language Models (VLMs) achieved rapid progress in the recent years.