Patient privacy in AI-driven omics methods

被引：1

作者：

Zhou, Juexiao ^{[1
,2
]}

Huang, Chao ^{[3
]}

Gao, Xin ^{[1
,2
]}

机构：

[1] King Abdullah Univ Sci & Technol KAUST, Comp Sci Program, Comp Elect & Math Sci & Engn Div, Thuwal 239556900, Saudi Arabia

[2] King Abdullah Univ Sci & Technol KAUST, Computat Biosci Res Ctr, Comp Elect & Math Sci & Engn Div, Thuwal 239556900, Saudi Arabia

[3] Chinese Acad Sci, Ningbo Inst Informat Technol Applicat, Ningbo, Peoples R China

来源：

TRENDS IN GENETICS | 2024年 / 40卷 / 05期

关键词：

SECURE;

D O I：

10.1016/j.tig.2024.03.004

中图分类号：

Q3 [遗传学];

学科分类号：

071007 ; 090102 ;

摘要：

Artificial intelligence (AI) in omics analysis raises privacy threats to patients. Here, we briefly discuss risk factors to patient privacy in data sharing, model training, and release, as well as methods to safeguard and evaluate patient privacy in AI-driven omics methods.

引用

页码：383 / 386

页数：4

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