Vietnamese medical research has become an increasingly vital domain, particularly with the rise of intelligent technologies aimed at reducing time and resource burdens in clinical diagnosis. Recent advances in vision-language models (VLMs), such as Gemini and GPT-4V, have sparked a growing interest in applying AI to healthcare. However, most existing VLMs lack exposure to Vietnamese medical data, limiting their ability to generate accurate and contextually appropriate diagnostic outputs for Vietnamese patients. To address this challenge, we introduce ViX-Ray, a novel dataset comprising 5,400 Vietnamese chest X-ray images annotated with expert-written findings and impressions from physicians at a major Vietnamese hospital. We analyze linguistic patterns within the dataset, including the frequency of mentioned body parts and diagnoses, to identify domain-specific linguistic characteristics of Vietnamese radiology reports. Furthermore, we fine-tune five state-of-the-art open-source VLMs on ViX-Ray and compare their performance to leading proprietary models, GPT-4V and Gemini. Our results show that while several models generate outputs partially aligned with clinical ground truths, they often suffer from low precision and excessive hallucination, especially in impression generation. These findings not only demonstrate the complexity and challenge of our dataset but also establish ViX-Ray as a valuable benchmark for evaluating and advancing vision-language models in the Vietnamese clinical domain.

翻译：越南医学研究已成为日益重要的领域，特别是在智能技术兴起、旨在减轻临床诊断时间和资源负担的背景下。视觉语言模型（如Gemini和GPT-4V）的最新进展引发了将人工智能应用于医疗保健领域的广泛兴趣。然而，现有大多数视觉语言模型缺乏对越南医学数据的接触，限制了其为越南患者生成准确且符合语境的诊断输出的能力。为应对这一挑战，我们推出了ViX-Ray——一个包含5,400张越南胸部X光图像的新型数据集，所有图像均附有越南主要医院医师撰写的专家级检查所见与影像印象标注。我们分析了数据集中的语言模式（包括提及的身体部位和诊断频率），以识别越南放射学报告中特定领域的语言特征。此外，我们在ViX-Ray上对五种前沿开源视觉语言模型进行微调，并将其性能与领先的专有模型GPT-4V和Gemini进行比较。实验结果表明，虽然部分模型生成的输出与临床真实情况部分吻合，但其普遍存在精确度低和过度幻觉生成的问题，在影像印象生成任务中尤为明显。这些发现不仅揭示了数据集的复杂性与挑战性，更确立了ViX-Ray作为评估和推进越南临床领域视觉语言模型的重要基准。