VLMs provide visual information alongside their predictions, but it remains unclear whether consistency in such information implies consistent decisions. We study this question in a controlled medical-imaging setting using brain MRI with pathology-confirmed labels and expert lesion annotations. For each human subject and modality, we construct configurations that retain the lesion content while varying surrounding context and scale and measure decision flips together with consistency in model-reported influential slices. Across four diverse VLMs (including proprietary, open-source, and domain-specific models), flip rates reach up to 75% across lesion-containing presentations, often despite high overlap in reported evidence. When lesion-related content is removed, proprietary models rarely produce a categorical diagnosis, with abstention rates ranging from 63% to 99%. These results reveal a mismatch between reported evidence and decisions, motivating evaluation beyond accuracy. Our evaluation dataset is publicly available on Hugging Face.

We present PerMed-MM, the first multimodal benchmark for Persian medical question answering. The dataset comprises 733 expert-authored multiple-choice questions from Iranian National Medical Board Exams, each paired with one to five clinically relevant images, spanning 46 medical specialties and diverse visual modalities. We evaluate five open-source and five proprietary vision language models, and find that reasoning supervision and domain-specific fine-tuning yield performance gains. Our cross-lingual analysis reveals significant unpredictability in translation-based pipelines, motivating the need for benchmarks that support direct, native-language evaluation. Additionally, domain- and modality-level analysis uncovers meaningful variation in model behavior often masked by aggregate metrics. PerMed-MM is publicly available on Hugging Face.