Patient privacy in AI-driven omics methods

Juexiao Zhou; Chao Huang; Xin Gao

doi:10.1016/j.tig.2024.03.004

Patient privacy in AI-driven omics methods

Juexiao Zhou, Chao Huang, Xin Gao^*

^*Corresponding author for this work

Research output: Contribution to journal › Short survey › peer-review

1 Scopus citations

Abstract

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.

Original language	English (US)
Pages (from-to)	383-386
Number of pages	4
Journal	Trends in Genetics
Volume	40
Issue number	5
DOIs	https://doi.org/10.1016/j.tig.2024.03.004
State	Published - May 2024

Bibliographical note

Publisher Copyright:
© 2024 The Authors

Keywords

artificial intelligence
omics
privacy

ASJC Scopus subject areas

Genetics

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10.1016/j.tig.2024.03.004

Cite this

@article{5119c29ddf6444f5ab27c0bfdc40428e,

title = "Patient privacy in AI-driven omics methods",

abstract = "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.",

keywords = "artificial intelligence, omics, privacy",

author = "Juexiao Zhou and Chao Huang and Xin Gao",

note = "Publisher Copyright: {\textcopyright} 2024 The Authors",

year = "2024",

month = may,

doi = "10.1016/j.tig.2024.03.004",

language = "English (US)",

volume = "40",

pages = "383--386",

journal = "Trends in Genetics",

issn = "0168-9525",

publisher = "Elsevier Ltd",

number = "5",

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TY - JOUR

T1 - Patient privacy in AI-driven omics methods

AU - Zhou, Juexiao

AU - Huang, Chao

AU - Gao, Xin

PY - 2024/5

Y1 - 2024/5

N2 - 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.

AB - 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.

KW - artificial intelligence

KW - omics

KW - privacy

UR - http://www.scopus.com/inward/record.url?scp=85190715040&partnerID=8YFLogxK

U2 - 10.1016/j.tig.2024.03.004

DO - 10.1016/j.tig.2024.03.004

M3 - Short survey

C2 - 38637270

AN - SCOPUS:85190715040

SN - 0168-9525

VL - 40

SP - 383

EP - 386

JO - Trends in Genetics

JF - Trends in Genetics

IS - 5

ER -

Patient privacy in AI-driven omics methods

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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