肿瘤学中的人工智能:确保在临床实践中安全有效地整合语言模型

IF 13.6 Q1 HEALTH CARE SCIENCES & SERVICES
Loïc Verlingue , Clara Boyer , Louise Olgiati , Clément Brutti Mairesse , Daphné Morel , Jean-Yves Blay
{"title":"肿瘤学中的人工智能:确保在临床实践中安全有效地整合语言模型","authors":"Loïc Verlingue ,&nbsp;Clara Boyer ,&nbsp;Louise Olgiati ,&nbsp;Clément Brutti Mairesse ,&nbsp;Daphné Morel ,&nbsp;Jean-Yves Blay","doi":"10.1016/j.lanepe.2024.101064","DOIUrl":null,"url":null,"abstract":"<div><p>In this Personal View, we address the latest advancements in automatic text analysis with artificial intelligence (AI) in medicine, with a focus on its implications in aiding treatment decisions in medical oncology. Acknowledging that a majority of hospital medical content is embedded in narrative format, natural language processing has become one of the most dynamic research fields for developing clinical decision support tools. In addition, large language models have recently reached unprecedented performance, notably when answering medical questions. Emerging applications include prognosis estimation, treatment recommendations, multidisciplinary tumor board recommendations and matching patients to recruiting clinical trials. Altogether, we advocate for a forward-looking approach in which the community efficiently initiates global prospective clinical evaluations of promising AI-based decision support systems. Such assessments will be essential to validate and evaluate potential biases, ensuring these innovations can be effectively and safely translated into practical tools for oncological practice. We are at a pivotal moment, where continued advancements in patient care must be pursued with scientific rigor.</p></div>","PeriodicalId":53223,"journal":{"name":"Lancet Regional Health-Europe","volume":"46 ","pages":"Article 101064"},"PeriodicalIF":13.6000,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266677622400231X/pdfft?md5=a6c9bd6accbe3b29497493a777676280&pid=1-s2.0-S266677622400231X-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence in oncology: ensuring safe and effective integration of language models in clinical practice\",\"authors\":\"Loïc Verlingue ,&nbsp;Clara Boyer ,&nbsp;Louise Olgiati ,&nbsp;Clément Brutti Mairesse ,&nbsp;Daphné Morel ,&nbsp;Jean-Yves Blay\",\"doi\":\"10.1016/j.lanepe.2024.101064\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In this Personal View, we address the latest advancements in automatic text analysis with artificial intelligence (AI) in medicine, with a focus on its implications in aiding treatment decisions in medical oncology. Acknowledging that a majority of hospital medical content is embedded in narrative format, natural language processing has become one of the most dynamic research fields for developing clinical decision support tools. In addition, large language models have recently reached unprecedented performance, notably when answering medical questions. Emerging applications include prognosis estimation, treatment recommendations, multidisciplinary tumor board recommendations and matching patients to recruiting clinical trials. Altogether, we advocate for a forward-looking approach in which the community efficiently initiates global prospective clinical evaluations of promising AI-based decision support systems. Such assessments will be essential to validate and evaluate potential biases, ensuring these innovations can be effectively and safely translated into practical tools for oncological practice. We are at a pivotal moment, where continued advancements in patient care must be pursued with scientific rigor.</p></div>\",\"PeriodicalId\":53223,\"journal\":{\"name\":\"Lancet Regional Health-Europe\",\"volume\":\"46 \",\"pages\":\"Article 101064\"},\"PeriodicalIF\":13.6000,\"publicationDate\":\"2024-09-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S266677622400231X/pdfft?md5=a6c9bd6accbe3b29497493a777676280&pid=1-s2.0-S266677622400231X-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Lancet Regional Health-Europe\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S266677622400231X\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Lancet Regional Health-Europe","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S266677622400231X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0

摘要

在这篇 "个人观点 "中,我们将探讨人工智能(AI)在医学自动文本分析方面的最新进展,重点关注其对肿瘤内科治疗决策的影响。由于医院的大部分医疗内容都是以叙事格式嵌入的,自然语言处理已成为开发临床决策支持工具的最具活力的研究领域之一。此外,大型语言模型最近也达到了前所未有的性能,尤其是在回答医疗问题时。新出现的应用包括预后评估、治疗建议、多学科肿瘤委员会建议以及将患者与招募的临床试验相匹配。总之,我们主张采取一种前瞻性的方法,让社会各界高效地对有前景的人工智能决策支持系统进行全球前瞻性临床评估。此类评估对于验证和评估潜在偏差至关重要,可确保这些创新能有效、安全地转化为肿瘤实践的实用工具。我们正处于一个关键时刻,必须以严谨的科学态度来追求患者护理的持续进步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial intelligence in oncology: ensuring safe and effective integration of language models in clinical practice

In this Personal View, we address the latest advancements in automatic text analysis with artificial intelligence (AI) in medicine, with a focus on its implications in aiding treatment decisions in medical oncology. Acknowledging that a majority of hospital medical content is embedded in narrative format, natural language processing has become one of the most dynamic research fields for developing clinical decision support tools. In addition, large language models have recently reached unprecedented performance, notably when answering medical questions. Emerging applications include prognosis estimation, treatment recommendations, multidisciplinary tumor board recommendations and matching patients to recruiting clinical trials. Altogether, we advocate for a forward-looking approach in which the community efficiently initiates global prospective clinical evaluations of promising AI-based decision support systems. Such assessments will be essential to validate and evaluate potential biases, ensuring these innovations can be effectively and safely translated into practical tools for oncological practice. We are at a pivotal moment, where continued advancements in patient care must be pursued with scientific rigor.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
19.90
自引率
1.40%
发文量
260
审稿时长
9 weeks
期刊介绍: The Lancet Regional Health – Europe, a gold open access journal, is part of The Lancet's global effort to promote healthcare quality and accessibility worldwide. It focuses on advancing clinical practice and health policy in the European region to enhance health outcomes. The journal publishes high-quality original research advocating changes in clinical practice and health policy. It also includes reviews, commentaries, and opinion pieces on regional health topics, such as infection and disease prevention, healthy aging, and reducing health disparities.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信