语言转换会破坏医学视觉语言模型吗？印度尼西亚放射学视觉问答案例研究
Does Language Shift Break Medical Vision-Language Models? Indonesian Radiology Visual Question Answering Case Study
AI总结：
本研究通过构建印尼语放射学VQA数据集IndoRad-VQA，评估医学视觉语言模型在非英语临床语言下的鲁棒性，发现英语与印尼语设置间存在8-25%的性能差距，表明需要更包容的多语言评估。
摘要：
医学视觉语言模型（VLM）通常在英语放射学视觉问答基准上进行评估，其在非英语临床语言下的鲁棒性很大程度上未被探索。我们引入了IndoRad-VQA，这是VQA-RAD的印尼语改编版，以评估当问题以印尼语提出时，医学VLM是否保留放射学推理能力。放射学问答对被翻译成印尼语，并通过基于自我评估的质量控制来保持临床意义、术语一致性和答案等价性。我们在英语和印尼语提示设置下评估了通用、东南亚多语言和医学专用VLM。除了准确性，我们量化了英语和印尼语输入之间的语言鲁棒性差距。我们还进行了错误分析，以识别问答的失败模式，例如是/否翻转、侧向性错误和输出语言不匹配。我们的发现表明，在英语医学VQA基准上的强性能并不一定转化为印尼语临床环境中的鲁棒行为。我们观察到英语和印尼语设置之间的性能差距为8%到25%，具体取决于评估指标。这些结果突显了对医学多模态基础模型进行更包容的多语言评估的必要性。数据集可在以下网址获取：此 https URL。
英文摘要：
Medical Vision-Language Models (VLMs) are typically evaluated on English radiology visual question answering benchmarks, leaving their robustness under non-English clinical language largely unexplored. We introduce IndoRad-VQA, an Indonesian adaptation of VQA-RAD, to assess whether medical VLMs retain radiology reasoning ability when questions are asked in Bahasa Indonesia. Radiology question-answer pairs are translated into Indonesian with self-evaluation-based quality control to preserve clinical meaning, terminology consistency, and answer equivalence. We evaluate general-purpose, Southeast Asian multilingual, and medical-specific VLMs under English and Indonesian prompting settings. Beyond accuracy, we quantify the language robustness gap between English and Indonesian inputs. We also conduct an error analysis to identify failure modes of question answering, such as yes/no flips, laterality errors, and output-language mismatches. Our findings show that strong performance on English medical VQA benchmarks does not necessarily translate to robust behavior in Indonesian clinical contexts. We observe a performance gap of 8 to 25 percent between the English and Indonesian settings, depending on the evaluation metric. These results highlight the need for more inclusive multilingual evaluation of medical multimodal foundation models. The dataset is available at https://huggingface.co/datasets/Lab-IS/IndoRad-VQA.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
