Patient privacy in AI-driven omics methods.

Zhou, Juexiao; Huang, Chao; Gao, Xin

Zhou, Juexiao; Huang, Chao; Gao, Xin.

Afiliación

Zhou J; 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.
Huang C; Ningbo Institute of Information Technology Application, Chinese Academy of Sciences (CAS), Ningbo, China.
Gao X; 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.

Trends Genet ; 40(5): 383-386, 2024 May.

Article en En | MEDLINE | ID: mdl-38637270

ABSTRACT

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.

Asunto(s)

Inteligencia Artificial; Genómica; Humanos; Genómica/métodos; Privacidad; Difusión de la Información

Palabras clave

artificial intelligence; omics; privacy

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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Inteligencia Artificial / Genómica Límite: Humans Idioma: En Revista: Trends Genet Asunto de la revista: GENETICA Año: 2024 Tipo del documento: Article

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