Artificial intelligence promises faster notes, smoother workflows, and smarter clinical decisions. But behind every seamless interaction lies an invisible cost—electricity, water, and carbon emissions that rarely enter the healthcare conversation.

In this episode, we trace what happens after you hit “enter” on a clinical prompt. From power-hungry GPUs to evaporative cooling systems in data centers, we uncover the hidden infrastructure fueling AI and how metrics like PUE translate convenience into environmental impact. A single prompt may only consume “a few drops,” but scaled across a hospital, it becomes a lake.

Blending insights from an AI researcher and an ER physician, we unpack the difference between training and serving costs, the overlooked impact of iterative prompting, and how everyday uses—charting, imaging, messaging—accumulate real-world carbon. Then we shift to what you can do now: swap out large models for leaner alternatives, trim excessive input context, and build smarter prompts that reduce compute without compromising care.

We also explore operational strategies: batch non-urgent tasks during off-peak hours, negotiate SLAs that trade slight latency for sustainability, and push vendors on the things that matter—like data center efficiency, water use, and renewables—not just performance scores.

Sustainable AI isn’t a dream—it’s a design choice. So where will you start: documentation, imaging, or patient messaging?

Reference:

Sustainably Advancing Health AI: A Decision Framework to Mitigate the Energy, Emissions, and Cost of AI Implementation
Anu Ramachandran, et al.
NEJM Catalyst (2025)

Credits:

Theme music: Nowhere Land, Kevin MacLeod (incompetech.com)
Licensed under Creative Commons: By Attribution 4.0
https://creativecommons.org/licenses/by/4.0/