Environmental impact of large language models in medicine

Abstract

The environmental impact of large language models (LLMs) in medicine spans carbon emission, water consumption and rare mineral usage. Prior-generation LLMs, such as GPT-3, already have concerning environmental impacts. Next-generation LLMs, such as GPT-4, are more energy intensive and used frequently, posing potentially significant environmental harms. We propose a five-step pathway for clinical researchers to minimise the environmental impact of the natural language algorithms they create.

Ähnliche Arbeiten