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.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1111/imj.16549 | DOI Listing |
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!