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Climate change and artificial intelligence in healthcare: Review and recommendations towards a sustainable future.
Ueda, Daiju; Walston, Shannon L; Fujita, Shohei; Fushimi, Yasutaka; Tsuboyama, Takahiro; Kamagata, Koji; Yamada, Akira; Yanagawa, Masahiro; Ito, Rintaro; Fujima, Noriyuki; Kawamura, Mariko; Nakaura, Takeshi; Matsui, Yusuke; Tatsugami, Fuminari; Fujioka, Tomoyuki; Nozaki, Taiki; Hirata, Kenji; Naganawa, Shinji.
Afiliación
  • 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:
  • Walston SL; Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Abeno-ku, Osaka 545-8585, Japan.
  • Fujita S; Department of Radiology, University of Tokyo, Bunkyo-ku, Tokyo 113-8655, Japan.
  • Fushimi Y; Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Sakyoku, Kyoto 606-8507, Japan.
  • Tsuboyama T; Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo 650-0017, Japan.
  • Kamagata K; Department of Radiology, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo 113-8421, Japan.
  • Yamada A; Medical Data Science Course, Shinshu University School of Medicine, Matsumoto, Nagano 390-8621, Japan.
  • Yanagawa M; Department of Radiology, Graduate School of Medicine, Osaka University, Suita-city, Osaka 565-0871, Japan.
  • Ito R; Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan.
  • Fujima N; Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Hokkaido 060-8648, Japan.
  • Kawamura M; Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan.
  • Nakaura T; Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, Chuo-ku, Kumamoto 860-8556, Japan.
  • Matsui Y; Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Kita-ku, Okayama 700-8558, Japan.
  • Tatsugami F; Department of Diagnostic Radiology, Hiroshima University, Minami-ku, Hiroshima City, Hiroshima 734-8551, Japan.
  • Fujioka T; Department of Diagnostic Radiology, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo 113-8510, Japan.
  • Nozaki T; Department of Radiology, Keio University School of Medicine, Shinjuku-ku, Tokyo 160-8582, Japan.
  • Hirata K; Department of Diagnostic Imaging, Faculty of Medicine, Hokkaido University, Sapporo, Hokkaido 060-8638, Japan.
  • Naganawa S; Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan.
Diagn Interv Imaging ; 2024 Jun 24.
Article en En | MEDLINE | ID: mdl-38918123
ABSTRACT
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Diagn Interv Imaging Año: 2024 Tipo del documento: Article Pais de publicación: Francia
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Diagn Interv Imaging Año: 2024 Tipo del documento: Article Pais de publicación: Francia