This paper describes the BIT.UA system for the MultiClinNER shared task at #SMM4H–HeaRD 2026, targeting multilingual clinical named entity recognition across seven languages for three entity types (Disease, Procedure, Symptom). We extend the Multi-Head CRF architecture, originally developed for multi-class NER on Spanish clinical text, to the multilingual setting. To enable joint multi-entity training despite per-entity text variations in the dataset, we develop an adaptive text consolidation pipeline that preserves over 94% of annotations. Our central finding is that a single xlm-roberta-large model, trained jointly on all seven languages and three entity types, achieves competition rank 2 for five of seven languages, outperforming dedicated monolingual models by up to +6.94 F1 points, while requiring only a single set of weights. Ensembling multiple seeds of this model achieves rank 1 for those five languages, and combining it with monolingual models yields rank 1 for the remaining two. Code and models are publicly available at https://github.com/ieeta-pt/Multi-Head-CRF/tree/MultiClinNER and https://huggingface.co/collections/IEETA/multiclinner-models.

This paper presents the joint participation of the BIT.UA and AAUBS groups in the ArchEHR-QA 2026 shared task, which focuses on clinical question answering and evidence grounding in a low-resource setting. Due to the absence of training data and the strict data privacy constraints inherent to the healthcare domain (e.g. GDPR), we investigate the capabilities of Large Language Models (LLMs) without weight updates. We evaluate several state-of-the-art proprietary models and locally deployable open-source alternatives using various prompt engineering strategies, including task decomposition, Chain-of-Thought, and in-context learning. Furthermore, we explore majority voting and LLM-as-a-judge ensembling techniques to maximize predictive robustness. Our results demonstrate that while proprietary models exhibit strong resilience to prompt variations, domain-adapted open-source models (such as MedGemma 3 27B) achieve highly competitive performance when paired with the right prompt. Overall, our prompt-based approach proved highly effective, securing 1st place in Subtask 4 (evidence citation alignment) and 3rd place in Subtask 3 (patient-friendly answer generation). All code, results, and prompts are available on our GitHub repository: https://github.com/bioinformatics-ua/ArchEHR-QA-2026.