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Evaluating and Mitigating Carbon Dioxide Equivalent Emissions in Stroke Management: A Modeling Study
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- TRV-2026-2607raw data (JSON)
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- v1Published
- First published
- Evidence type
- Observational study
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- 1 sourcePeer-reviewed
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Background Artificial intelligence (AI) can improve stroke imaging workflows, but its computational carbon footprint remains poorly quantified. We estimated carbon dioxide equivalent (CO 2 eq) emissions from AI use in US stroke management and evaluated carbon‐aware mitigation. Methods We 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 scena…
The problem
A modeling study of US stroke care estimated that a full AI imaging workflow would emit about 16,375 metric tons of CO2 equivalent a year, more than double the 7,693 tons of a minimal setup.
What this doesn’t fix
The estimates rest on 2018 to 2019 data and modeling assumptions, such as uniform application of AI, and exclude large language models, other greenhouse gases, water use, and cloud capacity and latency constraints for relocating processing.
Sources
1 on file- Journal of the American Heart Associationdoi:10.1161/jaha.125.047556
Cite this record
v1Truvace Impact Record TRV-2026-2607, v1: “Evaluating and Mitigating Carbon Dioxide Equivalent Emissions in Stroke Management: A Modeling Study.” Truvace, 2026-11-12. /record/TRV-2026-2607 (accessed on the day you copy it). sha256 91378da1aab8f73a…
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