ArticleThe Lancet. Digital health2024
Operational greenhouse-gas emissions of deep learning in digital pathology: a modelling study.
Article in The Lancet. Digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Evaluating and Mitigating Carbon Dioxide Equivalent Emissions in Stroke Management: A Modeling Study.Journal of the American Heart Association · 2026Article
- The impact of tissue detection on diagnostic artificial intelligence algorithms in prostate digital pathology.Scientific reports · 2026Article
- Applying Abstract Text Mining as a Complement to PRISMA in Reviewing the Scope of Healthcare's Circular Economy Comment on "A Review of the Applicability of Current Green Practices in Healthcare Facilities".International journal of health policy and management · 2026Article
- Training a high-performance retinal foundation model with half-the-data and 400 times less compute.Nature communications · 2025Article
- Patients' Perceptions of Artificial Intelligence Acceptance, Challenges, and Use in Medical Care: Qualitative Study.Journal of medical Internet research · 2025Article
- Decoding pathology: the role of computational pathology in research and diagnostics.Pflugers Archiv : European journal of physiology · 2025Review
- [Ecological sustainability of deep learning in pathology : A modeling study].Pathologie (Heidelberg, Germany) · 2025Article
- Ecologically sustainable benchmarking of AI models for histopathology.NPJ digital medicine · 2024Article
- Net Zero is not enough: ratcheting ambition for sustainable health systems through Reduce and Support.BMJ global health · 2024Review
- Joining forces for pathology diagnostics with AI assistance: The EMPAIA initiative.Journal of pathology informatics · 2024Review
- Deep learning applications in digital pathology.Nature reviews. Nephrology · 2024Article
- [Artificial intelligence in kidney transplant pathology].Pathologie (Heidelberg, Germany) · 2024Review
- Why digital innovation may not reduce healthcare's environmental footprint.BMJ (Clinical research ed.) · 2024Article
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9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDeep learning is a promising way to improve health care. Image-processing medical disciplines, such as pathology, are expected to be transformed by deep learning. The first clinically applicable deep-learning diagnostic support tools are already available in cancer pathology, and their number is increasing. However, data on the environmental sustainability of these tools are scarce. We aimed to conduct an environmental-sustainability analysis of a theoretical implementation of deep learning in patient-care pathology.
methodsFor this modelling study, we first assembled and calculated relevant data and parameters of a digital-pathology workflow. Data were breast and prostate specimens from the university clinic at the Institute of Pathology of the Rheinisch-Westfälische Technische Hochschule Aachen (Aachen, Germany), for which commercially available deep learning was already available. Only specimens collected between Jan 1 and Dec 31, 2019 were used, to omit potential biases due to the COVID-19 pandemic. Our final selection was based on 2 representative weeks outside holidays, covering different types of specimens. To calculate carbon dioxide (CO
findingsThe pathology database contained 35 552 specimens (237 179 slides), 6420 of which were prostate specimens (10 115 slides) and 11 801 of which were breast specimens (19 763 slides). We selected and subsequently digitised 140 slides from eight breast-cancer cases and 223 slides from five prostate-cancer cases. Applying large deep-learning models on all WSI tiles of prostate and breast pathology cases would result in yearly CO
interpretationOur findings suggest that widespread use of deep learning in pathology might have considerable global-warming potential. The medical community, policy decision makers, and the public should be aware of this potential and encourage the use of CO
fundingGerman Research Foundation, European Research Council, German Federal Ministry of Education and Research, Health, Economic Affairs and Climate Action, and the Innovation Fund of the Federal Joint Committee.
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