Medical English-Vietnamese machine translation (En-Vi MT) is essential for healthcare access and communication in Vietnam, yet Vietnamese remains a low-resource and under-studied language. We systematically evaluate prompting strategies for six multilingual LLMs (0.5B-9B parameters) on the MedEV dataset, comparing zero-shot, few-shot, and dictionary-augmented prompting with Meddict, an English-Vietnamese medical lexicon. Results show that model scale is the primary driver of performance: larger LLMs achieve strong zero-shot results, while few-shot prompting yields only marginal improvements. In contrast, terminology-aware cues and embedding-based example retrieval consistently improve domain-specific translation. These findings underscore both the promise and the current limitations of multilingual LLMs for medical En-Vi MT.

翻译：医学英语-越南语机器翻译（En-Vi MT）对于越南的医疗资源获取与沟通至关重要，然而越南语仍属于资源匮乏且研究不足的语言。本研究基于MedEV数据集，系统评估了六种多语言大语言模型（参数量0.5B-9B）的提示策略，对比了零样本、少样本以及结合医学词典Meddict（英越医学词汇表）的词典增强提示方法。结果表明：模型规模是性能的主要驱动因素——参数量更大的大语言模型在零样本场景下即可取得优异效果，而少样本提示仅带来边际改善。相比之下，术语感知提示与基于嵌入的示例检索能持续提升领域特异性翻译质量。这些发现既揭示了多语言大语言模型在医学英越机器翻译中的应用潜力，也指出了其当前存在的局限性。