Insights into Imaging, Journal Year: 2025, Volume and Issue: 16(1)

Published: April 17, 2025

Abstract Artificial intelligence (AI) is transforming radiology by improving diagnostic accuracy, streamlining workflows, and enhancing operational efficiency. However, these advancements come with significant sustainability challenges across environmental, economic, social dimensions. AI systems, particularly deep learning models, require substantial computational resources, leading to high energy consumption, increased carbon emissions, hardware waste. Data storage cloud computing further exacerbate the environmental impact. Economically, costs of implementing tools often outweigh demonstrated clinical benefits, raising concerns about their long-term viability equity in healthcare systems. Socially, risks perpetuating disparities through biases algorithms unequal access technology. On other hand, has potential improve reducing low-value imaging, optimizing resource allocation, efficiency departments. This review addresses paradox from a radiological perspective, exploring its footprint, economic feasibility, implications. Strategies mitigate are also discussed, alongside call for action directions future research. Critical relevance statement By adopting an informed holistic approach, community can ensure that AI’s benefits realized responsibly, balancing innovation sustainability. effort essential align technological preservation, sustainability, equity. Key Points ambivalent potential, capable both exacerbating global issues offering productivity accessibility. Addressing requires broad perspective accounting impact, embracing duality AI, adopt strategies at individual, institutional, collective levels maximize while minimizing negative impacts. Graphical

Language: Английский