TY - JOUR
T1 - Environmental impact of large language models in medicine
AU - Kleinig, Oliver
AU - Sinhal, Shreyans
AU - Khurram, Rushan
AU - Gao, Christina
AU - Spajic, Luke
AU - Zannettino, Andrew
AU - Schnitzler, Margaret
AU - Guo, Christina
AU - Zaman, Sarah
AU - Smallbone, Harry
AU - Ittimani, Mana
AU - Chan, Weng Onn
AU - Stretton, Brandon
AU - Godber, Harry
AU - Chan, Justin
AU - Turner, Richard C.
AU - Warren, Leigh R.
AU - Clarke, Jonathan
AU - Sivagangabalan, Gopal
AU - Marshall-Webb, Matthew
AU - Moseley, Genevieve
AU - Driscoll, Simon
AU - Kovoor, Pramesh
AU - Chow, Clara K.
AU - Luo, Yuchen
AU - Thiagalingam, Aravinda
AU - Zaka, Ammar
AU - Gould, Paul
AU - Ramponi, Fabio
AU - Gupta, Aashray
AU - Kovoor, Joshua G.
AU - Bacchi, Stephen
N1 - Publisher Copyright:
© 2024 Royal Australasian College of Physicians.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - environment
KW - large language model
KW - water use
UR - http://www.scopus.com/inward/record.url?scp=85209064179&partnerID=8YFLogxK
U2 - 10.1111/imj.16549
DO - 10.1111/imj.16549
M3 - Article
AN - SCOPUS:85209064179
SN - 1444-0903
JO - Internal Medicine Journal
JF - Internal Medicine Journal
ER -