A team of researchers from Osaka Metropolitan University have recently evaluated the environmental costs of artificial intelligence (AI), revealing the requirement for global sustainability initiatives.
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As AI rapidly evolves, its integration into healthcare, especially radiology, has accelerated. Hospitals are increasingly adopting AI-based diagnostic systems to aid radiologists, but this growth has raised concerns about the environmental impact of advanced AI models and the need for more sustainable practices.
Associate Professor Daiju Ueda from Osaka Metropolitan University’s Graduate School of Medicine, a member of the Japan Radiological Society, led a team to investigate the environmental costs of AI. Their review, involving leading figures from the Japan Radiological Society and other experts, examined the energy consumption of AI systems, data center carbon emissions, and electronic waste issues. The study proposed several solutions, including the development of energy-efficient AI models, the adoption of green computing, and the use of renewable energy.
Additionally, the review recommends strategies for the sustainable deployment of AI in healthcare. These guidelines aim to assist medical institutions, policymakers, and AI developers in implementing AI systems in an environmentally responsible manner.
AI has the potential to improve the quality of healthcare, but at the same time its environmental impact cannot be ignored. The best practices we have recommended are the first steps toward balancing these two factors. The challenge for the future will be to verify and further elaborate these recommendations in actual medical practice. They are also expected to contribute to the standardization of methods for assessing AI's environmental impact and the development of an international regulatory framework.
Daiju Ueda, Associate Professor, Osaka Metropolitan University
Journal Reference:
Ueda, D., et. al. (2024) Climate change and artificial intelligence in healthcare: Review and recommendations towards a sustainable future. Diagnostic and Interventional Imaging. doi:10.1016/j.diii.2024.06.002