Does Language Shift Break Medical Vision-Language Models? Indonesian Radiology Visual Question Answering Case Study
arXiv:2606.03693v1 Announce Type: new Abstract: 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 equ
The proliferation of AI models globally is forcing a confrontation with linguistic diversity, highlighting the current English-centric bias of most advanced systems and their potential fragility when confronted with non-English data.
This study underscores a critical blind spot in current AI development, revealing that models robust in one language may fail severely in another, directly impacting the global applicability and trust in AI systems, especially in sensitive domains like medicine.
The understanding of AI robustness is shifting from a purely technical performance metric to one that explicitly includes multilingual and multicultural contexts, necessitating new benchmarks and development strategies.
- · Non-English speaking AI developers
- · Multilingual AI research platforms
- · Countries investing in localized AI data and models
- · Healthcare providers in diverse linguistic regions
- · Developers relying solely on English benchmarks
- · AI models without multilingual robustness
- · Cloud providers without diverse language support
Medical AI models will face increased scrutiny for cross-linguistic and cross-cultural generalization.
There will be accelerated investment in creating diverse linguistic datasets and benchmarks across various critical AI applications, not just healthcare.
This could fuel the development of a more fragmented but globally tailored AI ecosystem, where localized models outperform generalized ones in specific regions or language groups.
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Read at arXiv cs.CL