AccScience Publishing / GHES / Online First / DOI: 10.36922/ghes.8359
ORIGINAL RESEARCH ARTICLE

Healthcare, smartphones, and the carbon footprint

Gloria Wu1* Sahil Saini1 Ethan Pan1 Ivan Chim2 Brian Hoang3 Samson Nguyen4 Mary Nguyen5 Hrishi Paliath-Pathiyal6
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1 Department of Ophthalmology, School of Medicine, University of California, San Francisco, California, United States of America
2 Department of Biology, School of Biological Sciences, University of California San Diego, San Diego, California, United States of America
3 Department of Computer Science, School of Engineering, University of California Davis, Davis, California, United States of America
4 Department of Biology, College of Science, San Jose State University, San Jose, California, United States of America
5 Department of Biology, Charlie Dunlop School of Biological Sciences, University of California Irvine, Irvine, California, United States of America
6 Department of Biological Sciences, Halmos College of Arts and Sciences, Nova Southeastern University, Fort Lauderdale, Florida, United States of America
Received: 1 January 2025 | Revised: 31 May 2025 | Accepted: 9 June 2025 | Published online: 24 June 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Abstract

Smartphones are widely used by physicians and patients. The carbon footprint of healthcare devices is poorly documented. Physicians report an average daily smartphone usage of 1 – 5 h for activities, such as reviewing diagnostic information, capturing patient photographs, conducting telehealth consultations, and advancing their medical education. Meanwhile, patients generate billions of daily queries on Google and millions on ChatGPT, trends likely to increase as artificial intelligence (AI)-driven search engines and large language models (LLMs) become more sophisticated and accessible. To explore the associated environmental impact, we evaluated the average lifetime carbon emissions linked to smartphone usage and the energy costs of manufacturing selected smartphone models. Our data were sourced from publicly accessible databases, corporate 10-K statements, and corporate social responsibility reports available on company websites. We then validated these findings by using four types of LLMs, including ChatGPT, Gemini, Claude.ai, and Meta AI. We found that all LLMs produced carbon emission estimates that differed from those reported in the companies’ official corporate literature. In an era of rapid AI adoption, establishing reliable environmental metrics is essential for informed decision-making and responsible technology use.

Keywords
Carbon footprint
Smartphone
Large language models
Environmental sustainability
ChatGPT
Digital healthcare
Artificial intelligence
Funding
None.
Conflict of interest
The authors declare they have no competing interests.
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