Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Apr 17;16(1):88.
doi: 10.1186/s13244-025-01962-2.

Radiology AI and sustainability paradox: environmental, economic, and social dimensions

Burak Kocak  1 , Andrea Ponsiglione  2 , Valeria Romeo  2 , Lorenzo Ugga  3 , Merel Huisman  4 , Renato Cuocolo  5
Affiliations

Radiology AI and sustainability paradox: environmental, economic, and social dimensions

Burak Kocak et al. Insights Imaging. .

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

Keywords: Artificial intelligence; Environmental health; Health equity; Radiology; Sustainability.

PubMed Disclaimer

Conflict of interest statement

Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: A.P. is a member of the Scientific Editorial Board of Insights into Imaging; he did not take part in the review or selection processes of this article. M.H.: unrelated to this manuscript: Speakers honoraria from industry (DeepC, Bayer, Canon, Sonoskills); paid consulting (Capvision); support for attending meetings and/or travel from scientific societies; EuSoMII Board member, ESR eHealth & Informatics Subcommittee member, ECR Imaging Informatics/Artificial Intelligence and Machine Learning Chairperson 2025, committee member with FMS (Dutch), and Radiology: Artificial Intelligence associate editor and trainee editorial board advisory panel (all unpaid). The other authors have nothing to disclose.

Figures

Fig. 1
Fig. 1
AI and sustainability paradox
Fig. 2
Fig. 2
Three pillars of AI sustainability
Fig. 3
Fig. 3
Carbon emissions of AI systems and relevant comparisons. Data compiled from multiple sources with necessary conversions applied [–22, 98]. CO2-eq (t), carbon dioxide equivalent (metric tons)
Fig. 4
Fig. 4
Environmental impact and challenges of AI
Fig. 5
Fig. 5
Strategies to mitigate environmental impact and challenges of AI. TPU, tensor processing unit; ALTAI, assessment list for trustworthy AI
Fig. 6
Fig. 6
Economic impact and challenges of AI
Fig. 7
Fig. 7
Mitigation strategies for the economic implications of AI. QALYs, quality-adjusted life years; EBITDA, earnings before interest, taxes, depreciation, and amortization
Fig. 8
Fig. 8
Social impact and challenges of AI
Fig. 9
Fig. 9
Strategies to mitigate social impact and challenges of AI
Fig. 10
Fig. 10
Key opportunities for utilizing AI to achieve sustainability in radiology
Fig. 11
Fig. 11
Potential roles of radiologists in sustainability of radiology AI
Fig. 12
Fig. 12
Ecolabel mock draft for AI tools. CO2-eq, carbon dioxide equivalent
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Khalifa M, Albadawy M (2024) AI in diagnostic imaging: revolutionising accuracy and efficiency. Comput Methods Prog Biomed Update 5:100146. 10.1016/j.cmpbup.2024.100146
    1. Kocak B, Baessler B, Cuocolo R et al (2023) Trends and statistics of artificial intelligence and radiomics research in radiology, nuclear medicine, and medical imaging: bibliometric analysis. Eur Radiol 33:7542–7555. 10.1007/s00330-023-09772-0 - PubMed
    1. Joshi G, Jain A, Araveeti SR et al (2024) FDA-approved artificial intelligence and machine learning (AI/ML)-enabled medical devices: an updated landscape. Electronics 13:498. 10.3390/electronics13030498
    1. Liu Y, Yu W, Dillon T (2024) Regulatory responses and approval status of artificial intelligence medical devices with a focus on China. NPJ Digit Med 7:255. 10.1038/s41746-024-01254-x - PMC - PubMed
    1. Muehlematter UJ, Bluethgen C, Vokinger KN (2023) FDA-cleared artificial intelligence and machine learning-based medical devices and their 510(k) predicate networks. Lancet Digit Health 5:e618–e626. 10.1016/S2589-7500(23)00126-7 - PubMed

LinkOut - more resources