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Federated data access and federated learning: improved data sharing, AI model development, and learning in intensive care.

作者信息

van Genderen Michel E, Cecconi Maurizio, Jung Christian

机构信息

Department of Adult Intensive Care, Erasmus MC, University Medical Center Rotterdam, (internal postadress-Room Ne-403), Doctor molewaterplein 40, 3015 GD, Rotterdam, The Netherlands.

Biomedical Sciences Department, Humanitas University, Milan, Italy.

出版信息

Intensive Care Med. 2024 Jun;50(6):974-977. doi: 10.1007/s00134-024-07408-5. Epub 2024 Apr 18.

DOI:10.1007/s00134-024-07408-5
PMID:38635044
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11164808/
Abstract
摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6981/11164808/25d4ff2fb15a/134_2024_7408_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6981/11164808/25d4ff2fb15a/134_2024_7408_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6981/11164808/25d4ff2fb15a/134_2024_7408_Fig1_HTML.jpg

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本文引用的文献

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Machine-Made Empathy? Why Medicine Still Needs Humans-Reply.机器制造的同理心?医学为何仍需要人类——回应
JAMA Intern Med. 2023 Nov 1;183(11):1279-1280. doi: 10.1001/jamainternmed.2023.4392.
2
The shaky foundations of large language models and foundation models for electronic health records.用于电子健康记录的大语言模型和基础模型的不稳定基础。
NPJ Digit Med. 2023 Jul 29;6(1):135. doi: 10.1038/s41746-023-00879-8.
3
Striking the balance: privacy protection and data accessibility in critical care research.寻求平衡:重症监护研究中的隐私保护与数据可及性
颅内压监测的概念演变——从传统监测到精准医学
Neurotherapeutics. 2025 Jan;22(1):e00507. doi: 10.1016/j.neurot.2024.e00507. Epub 2025 Jan 3.
4
What if we do, but what if we don't? The opportunity cost of artificial intelligence hesitancy in the intensive care unit.如果我们这样做了会怎样,但如果不这样做又会怎样呢?重症监护病房中人工智能迟疑不决的机会成本。
Intensive Care Med. 2025 Feb;51(2):378-381. doi: 10.1007/s00134-024-07747-3. Epub 2024 Dec 11.
5
Federated learning: a step in the right direction to improve data equity.联邦学习:朝着改善数据公平性的正确方向迈出的一步。
Intensive Care Med. 2024 Aug;50(8):1393-1394. doi: 10.1007/s00134-024-07525-1. Epub 2024 Jul 2.
6
Why federated learning will do little to overcome the deeply embedded biases in clinical medicine.为什么联邦学习在克服临床医学中根深蒂固的偏见方面作用不大。
Intensive Care Med. 2024 Aug;50(8):1390-1392. doi: 10.1007/s00134-024-07491-8. Epub 2024 Jun 3.
Intensive Care Med. 2023 Aug;49(8):1029-1030. doi: 10.1007/s00134-023-07153-1. Epub 2023 Jul 10.
4
How could ChatGPT impact my practice as an intensivist? An overview of potential applications, risks and limitations.ChatGPT如何影响我作为一名重症监护医生的工作?对潜在应用、风险和局限性的概述。
Intensive Care Med. 2023 Jul;49(7):844-847. doi: 10.1007/s00134-023-07096-7. Epub 2023 May 31.
5
Foundation models for generalist medical artificial intelligence.通用型医学人工智能的基础模型。
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A survey on federated learning: challenges and applications.联邦学习综述:挑战与应用
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AI in health and medicine.人工智能在医疗中的应用。
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Moving from bytes to bedside: a systematic review on the use of artificial intelligence in the intensive care unit.从字节到床边:人工智能在重症监护病房中的应用的系统评价。
Intensive Care Med. 2021 Jul;47(7):750-760. doi: 10.1007/s00134-021-06446-7. Epub 2021 Jun 5.