Energy Sustainability in Artificial Intelligence for Nursing Practice: Addressing the Hidden Cost
DOI:
https://doi.org/10.35960/vm.v19i1.2249Keywords:
Artificial Intelligence, Energy, Sustainability, Nursing, PracticeAbstract
The adoption of artificial intelligence in nursing practice has accelerated rapidly and offers substantial benefits in terms of efficiency, predictive accuracy, and clinical workflow optimization. Applications such as automated documentation, natural language processing of clinical notes, and decision support systems are increasingly embedded in routine nursing activities. While these technologies enhance performance and productivity, growing evidence indicates that artificial intelligence systems are associated with significant energy consumption during model training, data storage, and operational deployment. The healthcare sector already contributes a measurable proportion of global greenhouse gas emissions, and energy intensive digital infrastructures further amplify this burden. Training advanced artificial intelligence models may generate substantial carbon emissions, and repeated inference processes in daily clinical use accumulate additional energy demand.Despite these concerns, current evaluation frameworks for artificial intelligence in nursing remain primarily centered on clinical effectiveness, usability, safety, and organizational readiness. Energy consumption, carbon footprint, and broader ecological implications are rarely incorporated into technology assessment processes. This omission creates a critical gap between digital innovation and environmental responsibility within nursing informatics. This short communication synthesizes available evidence on the hidden energy costs of artificial intelligence in healthcare and nursing contexts, identifies structural gaps in prevailing evaluation paradigms, and proposes the integration of standardized sustainability metrics. The proposed framework emphasizes explicit reporting of energy consumption, carbon emissions, and life cycle environmental impacts alongside traditional clinical and operational indicators. By reframing artificial intelligence evaluation through a sustainability lens, nursing can contribute to advancing digital transformation that is not only safe and effective but also environmentally responsible
References
Aggarwal, T., Kumar, S., Singh, S. K., Subba, C. K., Upadhyaya, B., Arya, V., Ratnaparkhi, A., & Sharma, S. K. (2025). Reducing the Carbon Footprint in Machine Learning With Eco-Friendly AI Training (pp. 201–214). https://doi.org/10.4018/979-8-3693-8034-5.ch010
Alahmari, S., & Alghamdi, I. (2025). A comprehensive survey on energy-efficient and privacy-preserving federated learning for edge intelligence and IoT. Results in Engineering, 28, 107849. https://doi.org/10.1016/j.rineng.2025.107849
Almagharbeh, W. T., Alfanash, H. A., Alnawafleh, K. A., Alasmari, A. A., Alsaraireh, F. A., Dreidi, M. M., & Nashwan, A. J. (2025). Application of artificial intelligence in nursing practice: a qualitative study of Jordanian nurses’ perspectives. BMC Nursing, 24(1), 92. https://doi.org/10.1186/s12912-024-02658-6
Almagharbeh, W. T., Alharrasi, M., Rony, M. K. K., Kabir, S., Ahmed, S. K., & Alrazeeni, D. M. (2025). Ethical and Institutional Readiness for Artificial Intelligence in Nursing: An Umbrella Review. International Nursing Review, 72(4). https://doi.org/10.1111/inr.70111
Arcadi, P. (2025). Nursing leadership and artificial intelligence ethics: Safeguarding relationships and values. Nursing Ethics, 32(8), 2468–2476. https://doi.org/10.1177/09697330251366599
Balpande, V., Rewatkar, P., Dhole, P., Alwadkar, I., & Gomase, K. (2025). Artificial intelligence transforming healthcare and nursing: A comprehensive bibliometric analysis. Multidisciplinary Reviews, 8(9), 2025267. https://doi.org/10.31893/multirev.2025267
Bozkurt, S. A., Aydoğan, S., Dursun Ergezen, F., & Türkoğlu, A. (2025). A systematic review and sequential explanatory synthesis: Artificial intelligence in healthcare education, a case of nursing. International Nursing Review, 72(2). https://doi.org/10.1111/inr.70018
Bratan, T., Heyen, N. B., Hüsing, B., Marscheider-Weidemann, F., & Thomann, J. (2024). Hypotheses on environmental impacts of AI use in healthcare. The Journal of Climate Change and Health, 16, 100299. https://doi.org/10.1016/j.joclim.2024.100299
Champendal, M., Lokaj, B., de Gevigney, V. D., Brulé, G., Zaghir, J., Boiko, P., Lovis, C., Müller, H., Schmid, J., & Ribeiro, R. T. (2026). Exploring environmental sustainability of artificial intelligence in radiology: A scoping review. European Journal of Radiology, 194, 112558. https://doi.org/10.1016/j.ejrad.2025.112558
Chen, L.-Y. A., Yeh, E.-H., Lin, P., Hsieh, M.-Y., Lin, C.-P., & Liao, Z.-Y. (2026). Interpreting free-text cardiac catheterisation reports: A machine learning approach informed by focused ethnography. Nurse Education in Practice, 91, 104715. https://doi.org/10.1016/j.nepr.2026.104715
Dave, B., Martin, P., David, S. S., Kumar, S., & Chakraborty, T. (2026). Enhancing healthcare worker mental health via artificial intelligence-driven work process improvements: a scoping review. International Journal of Medical Informatics, 205, 106122. https://doi.org/10.1016/j.ijmedinf.2025.106122
Duane, B., Ashley, P., & Larkin, J. (2026). Prompt-Driven ChatGPT Carbon Calculator for Dental Practices: Estimation and Tailored Improvement Strategies. International Dental Journal, 76(1), 103979. https://doi.org/10.1016/j.identj.2025.103979
Fajkis-Zajączkowska, N., Zawada, A., Bojko, M., & Kolasa, K. (2025). Comprehensive analysis of frameworks for evaluating artificial intelligence solutions in Healthcare: A descriptive review. Computers in Biology and Medicine, 196, 110750. https://doi.org/10.1016/j.compbiomed.2025.110750
J., J., Mahalingam, N., Wang, B., & Yeo, K. S. (2025). Federated learning for sustainable intrusion detection systems: A review of green computing strategies and future directions. Internet of Things, 34, 101730. https://doi.org/10.1016/j.iot.2025.101730
Jacob, C., Brasier, N., Laurenzi, E., Heuss, S., Mougiakakou, S.-G., Cöltekin, A., & Peter, M. K. (2025). AI for IMPACTS Framework for Evaluating the Long-Term Real-World Impacts of AI-Powered Clinician Tools: Systematic Review and Narrative Synthesis. Journal of Medical Internet Research, 27, e67485. https://doi.org/10.2196/67485
Keil, M., Frehse, L., Hagemeister, M., Knieß, M., Lange, O., Kronenberg, T., & Rogowski, W. (2024). Carbon footprint of healthcare systems: a systematic review of evidence and methods. BMJ Open, 14(4), e078464. https://doi.org/10.1136/bmjopen-2023-078464
Kommusaar, J., Elunurm, S., Chomutare, T., Kangasniemi, M., Salanterä, S., & Peltonen, L.-M. (2026). A roadmap for federated learning projects using health data to guide sustainable artificial intelligence development in the European Union. International Journal of Medical Informatics, 208, 106242. https://doi.org/10.1016/j.ijmedinf.2025.106242
Mikhailova, A. A., & Sharova, D. E. (2023). Artificial intelligence ethics code in healthcare. Sustainability of artificial intelligence systems: Why do we talk about their impact on the environment? Digital Diagnostics, 4(1S), 93–95. https://doi.org/10.17816/DD430356
Mohammed, S. A. A. Q., Osman, Y. M. M., Ibrahim, A. M., & Shaban, M. (2025). Ethical and regulatory considerations in the use of AI and machine learning in nursing: A systematic review. International Nursing Review, 72(1). https://doi.org/10.1111/inr.70010
Moltó-Puigmartí, C., Trias, S. A., Vallejo, M. B., Domínguez Herrera, D., & Vivanco-Hidalgo, R. M. (2026). The Assessment of AI-Based Digital Health Technologies from the Perspective of HTA Bodies. The Case of AQuAS’ AI Assessment Guide (pp. 90–96). https://doi.org/10.1007/978-3-032-10661-2_7
Ramachandran, A., Sarabu, C., Gupta, U., Ghose, S., & Lee, V. S. (2025b). Sustainably Advancing Health AI: A Decision Framework to Mitigate the Energy, Emissions, and Cost of AI Implementation. NEJM Catalyst, 6(10). https://doi.org/10.1056/CAT.25.0125
Ramadan, O. M. E., Alruwaili, M. M., Alruwaili, A. N., Elsehrawy, M. G., & Alanazi, S. (2024). Facilitators and barriers to AI adoption in nursing practice: a qualitative study of registered nurses’ perspectives. BMC Nursing, 23(1), 891. https://doi.org/10.1186/s12912-024-02571-y
Reading Turchioe, M., Pepingco, C., Ronquillo, C., Ferrara, S. A., Topaz, M., Austin, R., & Lytle, K. (2025). Education, empowerment, and elevating nursing voices: Nursing informatics leaders’ perspectives on the path forward with artificial intelligence in nursing. Nursing Outlook, 73(5), 102484. https://doi.org/10.1016/j.outlook.2025.102484
Reddy, S., Rogers, W., Makinen, V.-P., Coiera, E., Brown, P., Wenzel, M., Weicken, E., Ansari, S., Mathur, P., Casey, A., & Kelly, B. (2021). Evaluation framework to guide implementation of AI systems into healthcare settings. BMJ Health & Care Informatics, 28(1), e100444. https://doi.org/10.1136/bmjhci-2021-100444
Rehman, Z. U., Hassan, U., Islam, S. U., Gallos, P., & Boudjadar, J. (2025). Energy-Efficient AI for Medical Diagnostics: Performance and Sustainability Analysis of ResNet and MobileNet. https://doi.org/10.3233/SHTI250585
Richie, C., Hinrichs-Krapels, S., Dobbe, R., French, P., Wei, J. C. J., Diehl, J. C., & Kong, R. (2025). Environmental impacts of artificial intelligence in health care: considerations and recommendations. Health and Technology, 15(6), 1087–1093. https://doi.org/10.1007/s12553-025-01003-4
Rojek, I., Prokopowicz, P., Piechowiak, M., Kotlarz, P., Náprstková, N., & Mikołajewski, D. (2025). The Impact of Data Analytics Based on Internet of Things, Edge Computing, and Artificial Intelligence on Energy Efficiency in Smart Environment. Applied Sciences, 16(1), 225. https://doi.org/10.3390/app16010225
Ruksakulpiwat, S., Thorngthip, S., Niyomyart, A., Benjasirisan, C., Phianhasin, L., Aldossary, H., Ahmed, B., & Samai, T. (2024). A Systematic Review of the Application of Artificial Intelligence in Nursing Care: Where are We, and What’s Next? Journal of Multidisciplinary Healthcare, Volume 17, 1603–1616. https://doi.org/10.2147/JMDH.S459946
Thakur, D., Guzzo, A., & Fortino, G. (2024). Hardware-algorithm co-design of Energy Efficient Federated Learning in Quantized Neural Network. Internet of Things, 26, 101223. https://doi.org/10.1016/j.iot.2024.101223
Tomlinson, E., Schoch, M., & McDonall, J. (2026). A curriculum framework for embedding artificial intelligence literacies in pre-registration nursing education. Nurse Education Today, 158, 106928. https://doi.org/10.1016/j.nedt.2025.106928
Ueda, D., Walston, S. L., Fujita, S., Fushimi, Y., Tsuboyama, T., Kamagata, K., Yamada, A., Yanagawa, M., Ito, R., Fujima, N., Kawamura, M., Nakaura, T., Matsui, Y., Tatsugami, F., Fujioka, T., Nozaki, T., Hirata, K., & Naganawa, S. (2024). Climate change and artificial intelligence in healthcare: Review and recommendations towards a sustainable future. Diagnostic and Interventional Imaging, 105(11), 453–459. https://doi.org/10.1016/j.diii.2024.06.002
von Gerich, H., Moen, H., Block, L. J., Chu, C. H., DeForest, H., Hobensack, M., Michalowski, M., Mitchell, J., Nibber, R., Olalia, M. A., Pruinelli, L., Ronquillo, C. E., Topaz, M., & Peltonen, L.-M. (2022a). Artificial Intelligence -based technologies in nursing: A scoping literature review of the evidence. International Journal of Nursing Studies, 127, 104153. https://doi.org/10.1016/j.ijnurstu.2021.104153
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Yen-Ching Chang, Agung Budi Prasetio (Author)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Submitted paper will be firstly reviewed by the editors to determine whether the paper meet the edition theme and submission guidelines. Papers which meet the theme and the guidelines will be assigned to selected reviewers for peer-reviews. Viva Medika: Jurnal Kesehatan, Kebidanan dan Keperawatan is a double blind peer-reviewed journal which involves reviewers based on their experties relevant to the topic of the paper. Final decision of paper acceptance is solely decided by the editors according to reviewers' comment.
Plagiarism and self-plagiarism are prohibited. Viva Medika: Jurnal Kesehatan, Kebidanan dan Keperawatan uses Turnitin, PlagiarismCheckerX or iThenticate to scan papers for detecting plagiarism. Thus, Appropriate citation and quotation should be used
.png)
