Navarro Martínez Olga, Fernández-García David, Cuartero Monteagudo Noemí, Forero-Rincón Olga
Nursing Education and Care Research Group (GRIECE), Nursing Department, Faculty of Nursing and Podiatry, Universitat de València, Menéndez y Pelayo, 19, 46010 Valencia, Spain.
Faculty of Medicine and Health Sciences, Catholic University of Valencia San Vicente Mártir, C/Espartero 7, 46007 Valencia, Spain.
Nurs Rep. 2024 Oct 3;14(4):2746-2757. doi: 10.3390/nursrep14040203.
"DeepFakes" are synthetic performances created by AI, using neural networks to exchange faces in images and modify voices.
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.
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).
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.
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.
“深度伪造”是人工智能利用神经网络在图像中进行面部交换和修改声音而生成的合成表演。
由于其新颖性以及关于其风险/益处的文献有限,本文旨在确定护理专业的年轻学生如何看待深度伪造技术、其伦理影响以及在护理中的潜在益处。
这项定性研究采用主题内容分析(布劳恩和克拉克方法),对50名三年级护理专业学生录制的视频进行分析,这些学生回答了关于深度伪造技术的三个问题。使用ATLAS.ti(22版)对数据进行分析,该项目获得了伦理委员会的批准(代码UCV/2021 - 2022/116)。
数据分析确定了21个描述性代码,分为四个主要主题:优点、缺点、健康应用和伦理困境。学生们提到的益处包括用于诊断、陪伴患者、培训和学习。感知到的风险包括网络欺凌、身份丧失以及虚假记忆带来的负面心理影响。
护理专业学生看到了深度伪造技术的利弊,并意识到它带来的伦理困境。他们还确定了有前景的医疗保健应用,这些应用可以增强护士在数字健康领域的领导力,强调了监管和教育对于充分发挥其潜力的重要性。