Potential reduction in healthcare carbon footprint by autonomous artificial intelligence
Open accessAbstract
There are efforts worldwide to reduce greenhouse gas (GHG) emissions, as they adversely affect the environment and human health 1 . Based on present, real world, mix of energy production methods (i.e., lacking renewables, nuclear, or other low or zero-carbon energy production), up to 10% of all annual GHG emissions may be due to healthcare processes and services 2 . The combined emissions of healthcare in the US, Canada, Australia, and the UK have been estimated to be ~7.5 × 10 14 grams (g) of carbon dioxide equivalents (CO 2 -eq) annually 3 . These include energy use at the healthcare facility, including heating, ventilation and air conditioning, as well as its construction, provider, personnel and patient travel, training of physicians and other healthcare workers, healthcare technology and systems usage, pharmaceuticals and medical devices, and cleaning and disposables 3 .
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Eyenuk's commercial launch of EyeArt began in the European Union in 2016 after receiving a Conformité Européenne Mark, when the system was only approved for investigational use in the United States.
“Eyenuk’s commercial launch of EyeArt began in the European Union in 2016 after receiving a Conformité Européenne Mark, when the system was only approved for investigational use in the United States.”
From Autonomous Artificial Intelligence in Diabetic Retinopathy Testing—Lessons Learned on Successful Health System Adoption (2026) · cites this paper as [29]
The section “Example of an autonomous AI: IDx-DR” discusses FDA clearance in 2018 for IDx-DR, a different system. Searches for EyeArt, Eyenuk, the 2016 EU launch, CE marking, and investigational use in the United States found no passage addressing the claim in our copy. (LLM, conf=0.76)
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References 33 · Cited by 63
Research works printed but not linked: 24
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- Authors
- Wolf, R. M., Abramoff, M. D., Channa, R., Tava, C., Clarida, W., & Lehmann, H. P.
- Affiliations
- Johns Hopkins University (US)
- University of Wisconsin–Madison (US)
- Venue
- npj Digital Medicine
- Published
- 2022-05-12
- Volume
- 5
- Issue
- 1
- Pages
- 62-62
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC9098499
- OpenAlex
- W4280598544
- Cited by
- 63
- References
- 33
- Citations by year
- 2026: 14 · 2025: 22 · 2024: 11 · 2023: 12 · 2022: 4
- FWCI
- 3.71
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Eye Institute (R01EY033233)
- Topics
- Physical Sciences › Environmental Science › Environmental Engineering › Environmental Impact and Sustainability
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Green IT and Sustainability
- Keywords
- carbon footprint of healthcare, greenhouse gas emissions, artificial intelligence, autonomous systems, CO2 emissions, emission reduction
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