Patient education is a major NLP application, but current evaluation largely focuses on text-only assistance. In radiology, this overlooks an important part of clinical communication: patients often need both a plain-language explanation and a visual aid showing where to look and what it refers to. We introduce MediSketch, a multimodal patient-education task in which an agent must produce an image–text bundle: an educational image and a grounded plain-language explanation. We instantiate this setting with a clinician-curated radiology testbed, a structured multimodal rubric, and a rubric-following judge that evaluates bundle quality rather than text-only correctness. Under a matched image-generation budget, we compare three test-time scaling strategies: parallel sampling, sequential revision, and hybrid search. Across five backbone models, the no-scaling baseline remains weak, while naive parallel search often provides little gain despite using the full budget. Hybrid performs best on 4 of 5 backbones, suggesting that test-time compute works best when exploration and revision are combined. Further analysis identifies weak multimodal self-critique as a major bottleneck: many models stop revising too early and overestimate the quality of their own image–text outputs. MediSketch exposes both an evaluation gap in multimodal patient education and a concrete failure mode in test-time scaling.

A benchmark score credits final answers, but not the route by which an item can be answered. In medical multimodal multiple-choice questions (MCQs), this distinction matters because a correct answer can be supported by the intended image finding or by benchmark-preserved cues in the wording of answers, non-visual clinical text, visible image text, artificial annotations, or device/context artifacts. We call the resulting score-level overinterpretation reasoning inflation. Here, a route is an observable input path that can support answer selection, not a claim about the model’s hidden cognition. Across six medical multimodal MCQ datasets, we separate candidate cues from behavioral evidence through prompt- and image-side audits, modality ablations, and matched repairs that preserve the medical target and answer key. In a 13-configuration open-model panel, full-input accuracy is 62.63%, while text-only and options-only settings achieve 53.96% and 29.71%, respectively. Removing length-gap, absolute/conspicuous, and spatial/prepositional cues lowers accuracy by 6.58, 3.50, and 4.77 percentage points. We also construct MedQA-MM, a 1,000-item shortcut-mitigated subset, where text-only and options-only accuracy fall to 5.21% and 12.33%. This does not imply that models never use images; it shows that medical image-reasoning claims require route-level evidence.