Sustainably Advancing Health AI: A Decision Framework to Mitigate the Energy, Emissions, and Cost of AI Implementation

With increasing pressures to deliver higher quality, safer, affordable care that is more equitable and accessible, U.S. health systems are looking hopefully at AI tools, including new and emerging generative AI capabilities, as a means of transforming medical care while alleviating workforce stresses. These AI technologies require substantial energy and water consumption as well as other resources to develop, deploy, and maintain. When considered at scale, AI technologies have the potential to impact the energy utilization of health systems and their ability to maintain their sustainability commitments, including the 2022 U.S. Department of Health and Human Services' Health Sector Climate Pledge. Multiple factors determine the energy requirements of a given AI tool, and health system leaders will have a critical window of opportunity to align AI implementation with larger considerations of appropriate resource utilization, sustainability, and cost. In this article, the authors offer a framework — the Sustainably Advancing Health AI (SAHAI) framework — for optimizing AI-related energy consumption and emissions in health care settings. Through an example of a generative AI use case — AI patient messaging — they calculate carbon emissions across various scenarios that could substantially affect the emissions profile of a major health system using such a tool. The authors discuss key takeaways for health systems implementing new AI technologies and offer concrete next steps for a coalition to advance health AI sustainably.