Oliver Kleinig, Shreyans Sinhal, Rushan Khurram, Christina Gao, Luke Spajic, Andrew Zannettino, Margaret Schnitzler, Christina Guo, Sarah Zaman, Harry Smallbone, Mana Ittimani, Weng Onn Chan, Brandon Stretton, Harry Godber, Justin Chan, Richard C Turner, Leigh R Warren, Jonathan Clarke, Gopal Sivagangabalan, Matthew Marshall-Webb, Genevieve Moseley, Simon Driscoll, Pramesh Kovoor, Clara K Chow, Yuchen Luo, Aravinda Thiagalingam, Ammar Zaka, Paul Gould, Fabio Ramponi, Aashray Gupta, Joshua G Kovoor, Stephen Bacchi
{"title":"大型语言模型对医学环境的影响。","authors":"Oliver Kleinig, Shreyans Sinhal, Rushan Khurram, Christina Gao, Luke Spajic, Andrew Zannettino, Margaret Schnitzler, Christina Guo, Sarah Zaman, Harry Smallbone, Mana Ittimani, Weng Onn Chan, Brandon Stretton, Harry Godber, Justin Chan, Richard C Turner, Leigh R Warren, Jonathan Clarke, Gopal Sivagangabalan, Matthew Marshall-Webb, Genevieve Moseley, Simon Driscoll, Pramesh Kovoor, Clara K Chow, Yuchen Luo, Aravinda Thiagalingam, Ammar Zaka, Paul Gould, Fabio Ramponi, Aashray Gupta, Joshua G Kovoor, Stephen Bacchi","doi":"10.1111/imj.16549","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":13625,"journal":{"name":"Internal Medicine Journal","volume":" ","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Environmental impact of large language models in medicine.\",\"authors\":\"Oliver Kleinig, Shreyans Sinhal, Rushan Khurram, Christina Gao, Luke Spajic, Andrew Zannettino, Margaret Schnitzler, Christina Guo, Sarah Zaman, Harry Smallbone, Mana Ittimani, Weng Onn Chan, Brandon Stretton, Harry Godber, Justin Chan, Richard C Turner, Leigh R Warren, Jonathan Clarke, Gopal Sivagangabalan, Matthew Marshall-Webb, Genevieve Moseley, Simon Driscoll, Pramesh Kovoor, Clara K Chow, Yuchen Luo, Aravinda Thiagalingam, Ammar Zaka, Paul Gould, Fabio Ramponi, Aashray Gupta, Joshua G Kovoor, Stephen Bacchi\",\"doi\":\"10.1111/imj.16549\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":13625,\"journal\":{\"name\":\"Internal Medicine Journal\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2024-11-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Internal Medicine Journal\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1111/imj.16549\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MEDICINE, GENERAL & INTERNAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Internal Medicine Journal","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/imj.16549","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
Environmental impact of large language models in medicine.
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.
期刊介绍：
The Internal Medicine Journal is the official journal of the Adult Medicine Division of The Royal Australasian College of Physicians (RACP). Its purpose is to publish high-quality internationally competitive peer-reviewed original medical research, both laboratory and clinical, relating to the study and research of human disease. Papers will be considered from all areas of medical practice and science. The Journal also has a major role in continuing medical education and publishes review articles relevant to physician education.
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