医学人工智能对数据的需求

M. Lingier, N. Naessens, E. Ranschaert, K. Verstraete
{"title":"医学人工智能对数据的需求","authors":"M. Lingier, N. Naessens, E. Ranschaert, K. Verstraete","doi":"10.47671/tvg.79.23.110","DOIUrl":null,"url":null,"abstract":"The need for data for artificial intelligence in medicine In recent decades, there has been a digital revolution in medicine, with an increasing integration of innovative technologies across different disciplines in the medical world. Artificial intelligence (AI), in particular, has the potential to have a groundbreaking impact on the healthcare of the future. However, the core of this promising technology heavily relies on data. Relevant literature was systematically and structurally searched through the databases of PubMed and Embase. Interviews were conducted with experts based on the insights and considerations from the literature. These interviews formed the foundation of this paper. Finally, the interviews were supported by relevant websites and literature found through Google Scholar. To develop a generalizable algorithm, the used data should not only have a high quality, but must also be numerous and diverse. However, there is not necessarily a need for more data, but rather for accessibility of the data. In clinical practice, a standardized format to store data is lacking. Furthermore, the data are scattered across different centres, with data-sharing heavily protected by the GDPR. There is a need for uniform and linkable data that can be collected from multiple healthcare institutions in a structured and protected manner using a centralized data platform. This data should have a high quality and must be sufficient in number to develop a robust and representative algorithm. The entire process must comply with the strict obligations imposed by the GDPR, ensuring the protection of the patients’ privacy.","PeriodicalId":23124,"journal":{"name":"Tijdschrift Voor Geneeskunde","volume":"15 3","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"De noodzaak aan data voor artificiële intelligentie in de geneeskunde\",\"authors\":\"M. Lingier, N. Naessens, E. Ranschaert, K. Verstraete\",\"doi\":\"10.47671/tvg.79.23.110\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The need for data for artificial intelligence in medicine In recent decades, there has been a digital revolution in medicine, with an increasing integration of innovative technologies across different disciplines in the medical world. Artificial intelligence (AI), in particular, has the potential to have a groundbreaking impact on the healthcare of the future. However, the core of this promising technology heavily relies on data. Relevant literature was systematically and structurally searched through the databases of PubMed and Embase. Interviews were conducted with experts based on the insights and considerations from the literature. These interviews formed the foundation of this paper. Finally, the interviews were supported by relevant websites and literature found through Google Scholar. To develop a generalizable algorithm, the used data should not only have a high quality, but must also be numerous and diverse. However, there is not necessarily a need for more data, but rather for accessibility of the data. In clinical practice, a standardized format to store data is lacking. Furthermore, the data are scattered across different centres, with data-sharing heavily protected by the GDPR. There is a need for uniform and linkable data that can be collected from multiple healthcare institutions in a structured and protected manner using a centralized data platform. This data should have a high quality and must be sufficient in number to develop a robust and representative algorithm. The entire process must comply with the strict obligations imposed by the GDPR, ensuring the protection of the patients’ privacy.\",\"PeriodicalId\":23124,\"journal\":{\"name\":\"Tijdschrift Voor Geneeskunde\",\"volume\":\"15 3\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-12-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Tijdschrift Voor Geneeskunde\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.47671/tvg.79.23.110\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Tijdschrift Voor Geneeskunde","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.47671/tvg.79.23.110","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

医学人工智能对数据的需求 近几十年来,医学领域发生了一场数字革命,医学界不同学科之间的创新技术日益融合。尤其是人工智能(AI),有可能对未来的医疗保健产生开创性的影响。然而,这项前景广阔的技术的核心在很大程度上依赖于数据。 我们通过 PubMed 和 Embase 数据库对相关文献进行了系统性和结构性检索。根据文献中的见解和考虑,对专家进行了访谈。这些访谈构成了本文的基础。最后,通过谷歌学术(Google Scholar)找到的相关网站和文献为访谈提供了支持。 要开发出一种可推广的算法,所使用的数据不仅要有较高的质量,还必须是大量的、多样化的。然而,并不一定需要更多的数据,而是需要数据的可访问性。在临床实践中,缺乏存储数据的标准格式。此外,数据分散在不同的中心,数据共享受到 GDPR 的严格保护。 因此,有必要使用集中式数据平台,以结构化和受保护的方式从多个医疗机构收集统一且可链接的数据。这些数据应具有较高的质量,数量必须足以开发出稳健且具有代表性的算法。整个过程必须严格遵守 GDPR 规定的义务,确保患者的隐私得到保护。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
De noodzaak aan data voor artificiële intelligentie in de geneeskunde
The need for data for artificial intelligence in medicine In recent decades, there has been a digital revolution in medicine, with an increasing integration of innovative technologies across different disciplines in the medical world. Artificial intelligence (AI), in particular, has the potential to have a groundbreaking impact on the healthcare of the future. However, the core of this promising technology heavily relies on data. Relevant literature was systematically and structurally searched through the databases of PubMed and Embase. Interviews were conducted with experts based on the insights and considerations from the literature. These interviews formed the foundation of this paper. Finally, the interviews were supported by relevant websites and literature found through Google Scholar. To develop a generalizable algorithm, the used data should not only have a high quality, but must also be numerous and diverse. However, there is not necessarily a need for more data, but rather for accessibility of the data. In clinical practice, a standardized format to store data is lacking. Furthermore, the data are scattered across different centres, with data-sharing heavily protected by the GDPR. There is a need for uniform and linkable data that can be collected from multiple healthcare institutions in a structured and protected manner using a centralized data platform. This data should have a high quality and must be sufficient in number to develop a robust and representative algorithm. The entire process must comply with the strict obligations imposed by the GDPR, ensuring the protection of the patients’ privacy.
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
自引率
0.00%
发文量
0
期刊最新文献
Begint het huisartsentekort bij de opleiding? Begint het huisartsentekort bij de opleiding? Een onderzoek naar het gebruik van de Pediatric Sleep Questionnaire als screeningstool voor obstructief slaapapneusyndroom bij kinderen met ADHD Een onderzoek naar het gebruik van de Pediatric Sleep Questionnaire als screeningstool voor obstructief slaapapneusyndroom bij kinderen met ADHD Methylfenidaat als behandeling voor hypersomnolentie als gevolg van een thalamusinfarct
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1