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

Trends Genet. 2024 May;40(5):383-386. doi: 10.1016/j.tig.2024.03.004. Epub 2024 Apr 17.

Authors

Juexiao Zhou¹, Chao Huang², Xin Gao³

Affiliations

¹ Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia; Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia.
² Ningbo Institute of Information Technology Application, Chinese Academy of Sciences (CAS), Ningbo, China.
³ Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia; Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia. Electronic address: xin.gao@kaust.edu.sa.

PMID: 38637270
DOI: 10.1016/j.tig.2024.03.004

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.

Publication types

Review
Research Support, Non-U.S. Gov't

MeSH terms

Artificial Intelligence*
Genomics* / methods
Humans
Information Dissemination
Privacy