The article "Environmental Sustainability and AI in Radiology: A Double-Edged Sword" discusses the dual impact of artificial intelligence (AI) on environmental sustainability in the field of radiology. On one hand, AI applications in radiology, particularly in data storage and computational efforts, contribute significantly to greenhouse gas (GHG) emissions due to the energy-intensive nature of AI model development and deployment. On the other hand, AI has the potential to improve environmental sustainability by optimizing imaging protocols, reducing scan times, improving scheduling efficiency, and integrating decision-support tools to reduce low-value imaging. The authors highlight the need for strategies to minimize the environmental impact of AI in radiology, such as adopting energy-efficient configurations, optimizing data storage, and leveraging renewable energy sources. They also emphasize the importance of educational initiatives and research to better understand and mitigate the environmental consequences of AI in radiology. The article concludes by outlining top 10 actions to improve sustainability in radiology AI and addressing knowledge gaps and future research directions.The article "Environmental Sustainability and AI in Radiology: A Double-Edged Sword" discusses the dual impact of artificial intelligence (AI) on environmental sustainability in the field of radiology. On one hand, AI applications in radiology, particularly in data storage and computational efforts, contribute significantly to greenhouse gas (GHG) emissions due to the energy-intensive nature of AI model development and deployment. On the other hand, AI has the potential to improve environmental sustainability by optimizing imaging protocols, reducing scan times, improving scheduling efficiency, and integrating decision-support tools to reduce low-value imaging. The authors highlight the need for strategies to minimize the environmental impact of AI in radiology, such as adopting energy-efficient configurations, optimizing data storage, and leveraging renewable energy sources. They also emphasize the importance of educational initiatives and research to better understand and mitigate the environmental consequences of AI in radiology. The article concludes by outlining top 10 actions to improve sustainability in radiology AI and addressing knowledge gaps and future research directions.