1. Environmental impact of large language models in medicine.
- Author
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Kleinig O, Sinhal S, Khurram R, Gao C, Spajic L, Zannettino A, Schnitzler M, Guo C, Zaman S, Smallbone H, Ittimani M, Chan WO, Stretton B, Godber H, Chan J, Turner RC, Warren LR, Clarke J, Sivagangabalan G, Marshall-Webb M, Moseley G, Driscoll S, Kovoor P, Chow CK, Luo Y, Thiagalingam A, Zaka A, Gould P, Ramponi F, Gupta A, Kovoor JG, and Bacchi S
- Subjects
- Humans, Algorithms, Natural Language Processing, Environment
- 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., (© 2024 Royal Australasian College of Physicians.)
- Published
- 2024
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