{"title":"重点错位的临床大型语言模型","authors":"Zining Luo, Haowei Ma, Zhiwu Li, Yuquan Chen, Yixin Sun, Aimin Hu, Jiang Yu, Yang Qiao, Junxian Gu, Hongying Li, Xuxi Peng, Dunrui Wang, Ying Liu, Zhenglong Liu, Jiebin Xie, Zhen Jiang, Gang Tian","doi":"10.1038/s42256-024-00929-0","DOIUrl":null,"url":null,"abstract":"<p>On 12 September 2024, OpenAI released two new large language models (LLMs) — o1-preview and o1-mini — marking an important shift in the competitive landscape of commercial LLMs, particularly concerning their reasoning capabilities. Since the introduction of GPT-3.5, OpenAI has launched 31 LLMs in two years. Researchers are rapidly applying these evolving commercial models in clinical medicine, achieving results that sometimes exceed human performance in specific tasks. Although such success is encouraging, the development of the models used for these tasks may not align with the characteristics and needs of clinical practice.</p><p>LLMs can be categorized as either open-source or closed-source. Open-source models, such as Meta’s Llama, allow developers to access source code, training data and documentation freely. By contrast, closed-source models are accessed only through official channels or application programming interfaces (APIs). Initially, open-source models dominated the LLM landscape, until the release of OpenAI’s GPT-3 in 2020<sup>1</sup>, which attracted considerable commercial interest and shifted focus towards closed-source approaches<sup>2</sup>.</p>","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"18 1","pages":""},"PeriodicalIF":18.8000,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Clinical large language models with misplaced focus\",\"authors\":\"Zining Luo, Haowei Ma, Zhiwu Li, Yuquan Chen, Yixin Sun, Aimin Hu, Jiang Yu, Yang Qiao, Junxian Gu, Hongying Li, Xuxi Peng, Dunrui Wang, Ying Liu, Zhenglong Liu, Jiebin Xie, Zhen Jiang, Gang Tian\",\"doi\":\"10.1038/s42256-024-00929-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>On 12 September 2024, OpenAI released two new large language models (LLMs) — o1-preview and o1-mini — marking an important shift in the competitive landscape of commercial LLMs, particularly concerning their reasoning capabilities. Since the introduction of GPT-3.5, OpenAI has launched 31 LLMs in two years. Researchers are rapidly applying these evolving commercial models in clinical medicine, achieving results that sometimes exceed human performance in specific tasks. Although such success is encouraging, the development of the models used for these tasks may not align with the characteristics and needs of clinical practice.</p><p>LLMs can be categorized as either open-source or closed-source. Open-source models, such as Meta’s Llama, allow developers to access source code, training data and documentation freely. By contrast, closed-source models are accessed only through official channels or application programming interfaces (APIs). Initially, open-source models dominated the LLM landscape, until the release of OpenAI’s GPT-3 in 2020<sup>1</sup>, which attracted considerable commercial interest and shifted focus towards closed-source approaches<sup>2</sup>.</p>\",\"PeriodicalId\":48533,\"journal\":{\"name\":\"Nature Machine Intelligence\",\"volume\":\"18 1\",\"pages\":\"\"},\"PeriodicalIF\":18.8000,\"publicationDate\":\"2024-11-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature Machine Intelligence\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1038/s42256-024-00929-0\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Machine Intelligence","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1038/s42256-024-00929-0","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Clinical large language models with misplaced focus
On 12 September 2024, OpenAI released two new large language models (LLMs) — o1-preview and o1-mini — marking an important shift in the competitive landscape of commercial LLMs, particularly concerning their reasoning capabilities. Since the introduction of GPT-3.5, OpenAI has launched 31 LLMs in two years. Researchers are rapidly applying these evolving commercial models in clinical medicine, achieving results that sometimes exceed human performance in specific tasks. Although such success is encouraging, the development of the models used for these tasks may not align with the characteristics and needs of clinical practice.
LLMs can be categorized as either open-source or closed-source. Open-source models, such as Meta’s Llama, allow developers to access source code, training data and documentation freely. By contrast, closed-source models are accessed only through official channels or application programming interfaces (APIs). Initially, open-source models dominated the LLM landscape, until the release of OpenAI’s GPT-3 in 20201, which attracted considerable commercial interest and shifted focus towards closed-source approaches2.
期刊介绍:
Nature Machine Intelligence is a distinguished publication that presents original research and reviews on various topics in machine learning, robotics, and AI. Our focus extends beyond these fields, exploring their profound impact on other scientific disciplines, as well as societal and industrial aspects. We recognize limitless possibilities wherein machine intelligence can augment human capabilities and knowledge in domains like scientific exploration, healthcare, medical diagnostics, and the creation of safe and sustainable cities, transportation, and agriculture. Simultaneously, we acknowledge the emergence of ethical, social, and legal concerns due to the rapid pace of advancements.
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