ArticleNature medicine2026
Evaluating the robustness and readiness of large frontier models in health AI applications.
Article in Nature medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- MAGIC: an international network for evaluating generative artificial intelligence in global health.Nature medicine · 2026Article
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Authors and funding
32 authors.
Funding
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Abstract
Large frontier models such as GPT-5 and Gemini have demonstrated remarkable performance in a wide range of health application benchmarks. However, underneath the seemingly promising results lie salient growth areas, especially in cutting-edge frontiers such as multimodal reasoning. Here we systematically apply and integrate a series of adversarial stress tests to assess the robustness of flagship models and health benchmarks. Our study reveals prevalent brittleness in the presence of simple adversarial transformations: leading systems can guess the correct answer even with key inputs removed yet may get confused by the slightest prompt alterations while fabricating convincing but flawed reasoning traces. Using clinician-guided rubrics, we demonstrate that popular health benchmarks vary widely in what they truly measure. Our study reveals considerable gaps between benchmark performance and the robustness evidence needed to support claims about multimodal medical reasoning in health applications.
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Registered trials
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