Climate change and artificial intelligence in healthcare: Review and recommendations towards a sustainable future.

Artificial intelligence Climate change Green computing Sustainable AI Sustainable development goals

Journal

Diagnostic and interventional imaging
ISSN: 2211-5684
Titre abrégé: Diagn Interv Imaging
Pays: France
ID NLM: 101568499

Informations de publication

Date de publication:
24 Jun 2024
Historique:
received: 29 05 2024
revised: 03 06 2024
accepted: 03 06 2024
medline: 26 6 2024
pubmed: 26 6 2024
entrez: 25 6 2024
Statut: aheadofprint

Résumé

The rapid advancement of artificial intelligence (AI) in healthcare has revolutionized the industry, offering significant improvements in diagnostic accuracy, efficiency, and patient outcomes. However, the increasing adoption of AI systems also raises concerns about their environmental impact, particularly in the context of climate change. This review explores the intersection of climate change and AI in healthcare, examining the challenges posed by the energy consumption and carbon footprint of AI systems, as well as the potential solutions to mitigate their environmental impact. The review highlights the energy-intensive nature of AI model training and deployment, the contribution of data centers to greenhouse gas emissions, and the generation of electronic waste. To address these challenges, the development of energy-efficient AI models, the adoption of green computing practices, and the integration of renewable energy sources are discussed as potential solutions. The review also emphasizes the role of AI in optimizing healthcare workflows, reducing resource waste, and facilitating sustainable practices such as telemedicine. Furthermore, the importance of policy and governance frameworks, global initiatives, and collaborative efforts in promoting sustainable AI practices in healthcare is explored. The review concludes by outlining best practices for sustainable AI deployment, including eco-design, lifecycle assessment, responsible data management, and continuous monitoring and improvement. As the healthcare industry continues to embrace AI technologies, prioritizing sustainability and environmental responsibility is crucial to ensure that the benefits of AI are realized while actively contributing to the preservation of our planet.

Identifiants

pubmed: 38918123
pii: S2211-5684(24)00138-4
doi: 10.1016/j.diii.2024.06.002
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2024 The Author(s). Published by Elsevier Masson SAS.. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors have no competing interests to disclose in relation with this article.

Auteurs

Daiju Ueda (D)

Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Abeno-ku, Osaka 545-8585, Japan; Department of Artificial Intelligence, Graduate School of Medicine, Osaka Metropolitan University, Abeno-ku, Osaka 545-8585, Japan. Electronic address: ai.labo.ocu@gmail.com.

Shannon L Walston (SL)

Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Abeno-ku, Osaka 545-8585, Japan.

Shohei Fujita (S)

Department of Radiology, University of Tokyo, Bunkyo-ku, Tokyo 113-8655, Japan.

Yasutaka Fushimi (Y)

Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Sakyoku, Kyoto 606-8507, Japan.

Takahiro Tsuboyama (T)

Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo 650-0017, Japan.

Koji Kamagata (K)

Department of Radiology, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo 113-8421, Japan.

Akira Yamada (A)

Medical Data Science Course, Shinshu University School of Medicine, Matsumoto, Nagano 390-8621, Japan.

Masahiro Yanagawa (M)

Department of Radiology, Graduate School of Medicine, Osaka University, Suita-city, Osaka 565-0871, Japan.

Rintaro Ito (R)

Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan.

Noriyuki Fujima (N)

Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Hokkaido 060-8648, Japan.

Mariko Kawamura (M)

Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan.

Takeshi Nakaura (T)

Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, Chuo-ku, Kumamoto 860-8556, Japan.

Yusuke Matsui (Y)

Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Kita-ku, Okayama 700-8558, Japan.

Fuminari Tatsugami (F)

Department of Diagnostic Radiology, Hiroshima University, Minami-ku, Hiroshima City, Hiroshima 734-8551, Japan.

Tomoyuki Fujioka (T)

Department of Diagnostic Radiology, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo 113-8510, Japan.

Taiki Nozaki (T)

Department of Radiology, Keio University School of Medicine, Shinjuku-ku, Tokyo 160-8582, Japan.

Kenji Hirata (K)

Department of Diagnostic Imaging, Faculty of Medicine, Hokkaido University, Sapporo, Hokkaido 060-8638, Japan.

Shinji Naganawa (S)

Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan.

Classifications MeSH