Possible Health Benefits and Risks of DeepFake Videos: A Qualitative Study in Nursing Students

Abstract

BACKGROUND: "DeepFakes" are synthetic performances created by AI, using neural networks to exchange faces in images and modify voices. OBJECTIVE: Due to the novelty and limited literature on its risks/benefits, this paper aims to determine how young nursing students perceive DeepFake technology, its ethical implications, and its potential benefits in nursing. METHODS: This qualitative study used thematic content analysis (the Braun and Clarke method) with videos recorded by 50 third-year nursing students, who answered three questions about DeepFake technology. The data were analyzed using ATLAS.ti (version 22), and the project was approved by the Ethics Committee (code UCV/2021-2022/116). RESULTS: Data analysis identified 21 descriptive codes, classified into four main themes: advantages, disadvantages, health applications, and ethical dilemmas. Benefits noted by students include use in diagnosis, patient accompaniment, training, and learning. Perceived risks include cyberbullying, loss of identity, and negative psychological impacts from unreal memories. CONCLUSIONS: Nursing students see both pros and cons in DeepFake technology and are aware of the ethical dilemmas it poses. They also identified promising healthcare applications that could enhance nurses' leadership in digital health, stressing the importance of regulation and education to fully leverage its potential.