This paper addresses the MultiClinAI challenge, subtask MultiClinNER, which focuses on clinical Named Entity Recognition (NER) across seven languages: Czech, Dutch, English, Italian, Romanian, Spanish, and Swedish. The main goal of MultiClinNER is to identify and extract clinical terms specifically related to diseases, procedures, and symptoms from discharge summaries. The paper explores a variety of state-of-the-art methods, both monolingual and multilingual, ranging from pretrained, zero-shot, domain-adapted transformers to fine-tuned transformer models, and demonstrates the benefits of ensemble modeling. Data augmentation through external resources significantly enhanced the models’ ability to recognize clinical entities. Both monolingual and multilingual approaches showed complementary strengths depending on the language and entity type. The average F1 score achieved across the best models for each language and category is 0.6502.