Artificial Intelligence in the NHS: Climate and Emissions✰,✰✰ - 06/12/21
Abstract |
Healthcare provision has a significant climate impact and, conversely, the climate is a determinant of population health. Research is underway to quantify the emissions from healthcare systems, which helps with reducing and offsetting them. Artificial intelligence (AI) is a rapidly developing field contributing to the English National Health Service (NHS) goals of more efficient care and reduced climate impact. There are concerns about the detrimental carbon emissions from training and deploying AI models. Conversely, AI could potentially reduce emissions through process optimisation and changing models of care.
In this narrative scoping review using the NHS as a case study we consider: AI in healthcare, methodologies for quantifying AI associated emissions, and opportunities for using AI to support NHS emission reduction efforts. We present the metrics and approaches commonly used to quantify climate impact in the field of AI and interpret them alongside healthcare AI.
While the NHS, and other health systems, are investing in the potential of AI technologies to improve health services, more should be done to quantify the climate impact of AI tools. Standardised measures are lacking, thereby limiting the ability to reduce and offset the climate impact of AI. We provide recommendations for policymakers, climate researchers, and AI developers to consider as part of achieving a net zero NHS by 2040.
El texto completo de este artículo está disponible en PDF.Keywords : Artificial intelligence, Carbon footprint, Healthcare, NHS, digital health
Esquema
✰Funding This work was supported by NHSX. The NHSX sponsor, Kassandra Karpathakis, Head of AI Strategy, was involved in study design, report writing and decision to submit for publication. |
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✰✰Credit statement All authors (PSB, PCB, EP, JM, KK) conceptualized the research and contributed to the methodology. The literature searchers were conducted by PSB (detrimental impacts of AI), PCB (climate research and use of AI), and EP (clinical domain AI). PSB wrote the first draft of the manuscript. JM critiqued the findings for bias. All authors reviewed and edited several iterations of the manuscript. KK provided supervision and acquired funding. |
Vol 4
Artículo 100056- octobre 2021 Regresar al númeroBienvenido a EM-consulte, la referencia de los profesionales de la salud.