AI and Sustainable Healthcare: Aligning Innovation With Environmental Responsibility

Presentation Type: Workshop - Collaborative
Presentation Time: 08:30 AM - 12:00 PM

Abstract Keywords: Artificial Intelligence, Large Language Models (LLMs), Environmental Health and Climate Informatics, Informatics Implementation, Health Equity
Programmatic Theme: Clinical Informatics

Artificial intelligence (AI) is increasingly positioned as a means to improve healthcare quality, efficiency, and access, yet its environmental implications remain insufficiently assessed. This gap is consequential: healthcare accounts for 8.5% of U.S. greenhouse gas emissions, and the life-cycle emissions associated with training a single 176-billion-parameter language model have been estimated at 50.5 metric tons of CO2. Despite this, most health system informatics leaders have limited understanding of how AI implementation interfaces with their institutions’ environmental footprint and sustainability objectives. This collaborative workshop will equip attendees to evaluate when AI can advance both better care and environmental sustainability—and when it may deepen healthcare’s carbon footprint.
Through brief presentations, structured discussion, and interactive group exercises, participants will examine two complementary questions: (1) how AI can support more sustainable care by improving clinical workflows, reducing waste, and strengthening resource allocation; and (2) how AI can also create environmental burdens through energy-intensive model training and inference, increased computing infrastructure, electronic waste, and rebound effects from expanded digital workflows. Drawing on the organizers’ recent publication in NEJM Catalyst and invited presentations at the intersection of AI and sustainable healthcare, the session will introduce practical approaches for evaluating these trade-offs across domains such as radiology, clinical documentation, and predictive analytics. It is designed for informaticians, implementers, researchers, and health system leaders shaping AI adoption in practice.
The workshop will use an inclusive, participatory format grounded in design thinking to engage attendees in developing implementation principles for environmentally responsible AI in healthcare. By the end of the session, participants will leave with a shared vocabulary, a practical framework for assessing environmental and clinical value, and actionable strategies to align AI deployment with organizational and sector-wide decarbonization goals.

Speaker(s):
Anu Ramachandran, MD MPH
Stanford University, VA Palo Alto

Manijeh Berenji, MD MPH
UC Irvine School of Medicine/Joe C Wen School of Population and Public Health (at UC Irvine); VA Long Beach Healthcare System

Jason Lau, MD, MS
University of Washington

Author(s):
Anu Ramachandran, MD MPH - Stanford University, VA Palo Alto; Manijeh Berenji, MD MPH - UC Irvine School of Medicine/Joe C Wen School of Population and Public Health (at UC Irvine); VA Long Beach Healthcare System; Jason Lau, MD, MS - University of Washington;