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 , Clara Boyer , Louise Olgiati , Clément Brutti Mairesse , Daphné Morel , 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":null,"pages":null},"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 , Clara Boyer , Louise Olgiati , Clément Brutti Mairesse , Daphné Morel , 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\":null,\"pages\":null},\"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}
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.
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.