Operational greenhouse-gas emissions of deep learning in digital pathology: a modelling study.
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ABSTRACT:
SUBMITTER: Vafaei Sadr A
PROVIDER: S-EPMC10728828 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
Publications
Operational greenhouse-gas emissions of deep learning in digital pathology: a modelling study.
Vafaei Sadr Alireza A Bülow Roman R von Stillfried Saskia S Schmitz Nikolas E J NEJ Pilva Pourya P Hölscher David L DL Ha Peiman Pilehchi PP Schweiker Marcel M Boor Peter P
The Lancet. Digital health 20231122 1
<h4>Background</h4>Deep 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 ...[more]
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