Food AI can recognize dishes, ingredients, and nutrients. However, these abilities do not show whether a dish is suitable for a person with a specific health condition. They also do not test whether such decisions are grounded in the relevant food evidence. To address this gap, we introduce FAM-Bench, a Food-as-Medicine benchmark comprising 2,500 question instances constructed from 1,811 recipes across 13 diet-related health conditions. Gold annotations are derived from rule-based dietary guidance informed by published sources and nutrition professionals. The benchmark contains two complementary tasks: dish-level suitability assessment and comparative dish analysis. We evaluate open-weight and proprietary vision-language models under four prompting modes. Results reveal a substantial gap between suitability decisions and their supporting rationales, as well as limited performance on comparative ranking. These findings highlight persistent challenges in grounded, condition-aware food reasoning.

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