Green artificial intelligence in health applications

Clinical Snapshot

Key Findings

• Effect Size: Not reported — no quantitative effect sizes are presented; findings are thematic and descriptive

• Primary Outcome: Identification and thematic synthesis of Green AI applications in healthcare, including energy-efficient AI models, green computing practices, renewable energy use, and telemedicine as environmentally friendly care modalities

• Nnt Or Sensitivity: Not applicable — no clinical efficacy, diagnostic accuracy, or prognostic metrics are reported; the review does not address patient outcomes in quantitative terms

• Confidence Interval: Not applicable — no statistical synthesis performed

Clinical Application

The conceptual recommendations (adopting energy-efficient AI, integrating renewable energy, expanding telemedicine) are broadly feasible in well-resourced healthcare systems but face significant implementation barriers in resource-limited settings. The review acknowledges barriers to adoption but does not quantify implementation costs, infrastructure requirements, or return on investment. Australia's National Digital Health Strategy and the Australian Digital Health Agency's AI governance work are relevant contexts. The Australian Government's commitment to net-zero emissions by 2050 aligns with Green AI principles. The TGA does not currently regulate AI sustainability metrics, though AI as a medical device (AIaMD) regulation is evolving. The RACGP has not issued specific guidance on Green AI. Telemedicine, highlighted as a Green AI enabler, is well-established in Australia post-COVID-19, with MBS telehealth items providing a reimbursement pathway. The PBS is not directly relevant to this review. Australian health services pursuing sustainability certifications (e.g., under the National Health Sustainability Framework) may find the policy discussion directionally useful, though the lack of quantitative evidence limits actionable guidance. Healthcare organisations, health informaticians, clinical informaticists, hospital administrators, and health policy makers considering AI adoption or sustainability strategies in clinical settings. Not directly applicable to individual patient care decisions.

Abstract

As the healthcare sector increasingly integrates Artificial Intelligence (AI) technologies to improve operational effectiveness, diagnosis, and therapy, the environmental footprint of these innovations has become a growing concern. High energy consumption, electronic waste, and carbon emissions associated with the deployment and training of AI models pose sustainability challenges that must be addressed. This paper investigates the concept and application of Green AI in healthcare, aiming to balance technological advancement with environmental responsibility. This systematic review explores key themes, including green computing practices, the adoption of energy-efficient AI models, and the use of renewable energy sources within healthcare settings. It identifies a range of healthcare applications employing Green AI, highlights emerging trends, and emphasizes the growing importance of environmental awareness in AI development. Furthermore, the study examines enabling tools and techniques, outlines barriers to adoption, and highlights how Green AI can help streamline processes, reduce resource waste, and promote environmentally friendly medical procedures like telemedicine. The review also discusses the significance of policy frameworks, international initiatives, and cross-sector collaboration in promoting environmentally responsible AI deployment. Finally, the paper presents practical implications and outlines future research directions to guide the sustainable evolution of AI in healthcare.

References

1. 1.Alzoubi, Y. I., & Mishra, A. (2026). Green artificial intelligence in health applications. Artificial Intelligence in Medicine. https://doi.org/10.1016/j.artmed.2026.103442