ArticleJournal of the American Heart Association2026
Evaluating and Mitigating Carbon Dioxide Equivalent Emissions in Stroke Management: A Modeling Study.
Article in Journal of the American Heart Association, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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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Authors and funding
7 authors.
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Abstract
backgroundArtificial intelligence (AI) can improve stroke imaging workflows, but its computational carbon footprint remains poorly quantified. We estimated carbon dioxide equivalent (CO
methodsWe combined TriNetX neuroimaging utilization data with Global Burden of Disease stroke burden estimates for 2018 to 2019. Imaging utilization rates were aligned by state, year, stroke type, sex, and age group. We modeled 2 AI scenarios: Minimal AI, defined as one 2-dimensional classification model and one 2-dimensional segmentation model; and Ideal AI, defined as two 2-dimensional classification models, two 3-dimensional segmentation models, and one 3-dimensional classification model. Model energy use was converted to CO
resultsIdeal AI generated 16 375.09 metric tons CO
conclusionsAI-assisted stroke imaging has a measurable carbon footprint that depends on model intensity, imaging modality, and grid carbon intensity. Carbon-aware routing of computation offers an immediate mitigation strategy while preserving clinical AI use.
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