We present the SuSh system for Subtask 1 of the MultiClinAI shared task at the 11th SMM4H and HeaRD Workshop (ACL 2026), which addresses multilingual clinical named entity recognition (NER) across seven languages. Our system adopts a fully zero-shot approach using GLiNER-biomed-large-v1.0, a span-based NER model pre-trained on biomedical text, requiring no task-specific fine-tuning or labeled data in target languages. We apply a character-level sliding window strategy to handle long clinical documents that exceed the model’s token limit and incorporate a post processing pipeline including threshold optimization via F1-max sweep, entity-specific gazetteer lookup derived from DisTEMIST and SympTEMIST terminology lists, span boundary correction, and negation filtering. Our official submission achieves a Strict F1 of 0.5175, Strict Precision of 0.5536, Strict Recall of 0.4859, and CHR F1 of 0.6130 on the English disease subtask, demonstrating that domain adapted zero-shot biomedical NER models can serve as competitive baselines for multilingual026 clinical entity recognition without any task specific training data.