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2023
DOI: 10.3390/make5030053
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Artificial Intelligence Ethics and Challenges in Healthcare Applications: A Comprehensive Review in the Context of the European GDPR Mandate

Mohammad Mohammad Amini,
Marcia Jesus,
Davood Fanaei Sheikholeslami
et al.

Abstract: This study examines the ethical issues surrounding the use of Artificial Intelligence (AI) in healthcare, specifically nursing, under the European General Data Protection Regulation (GDPR). The analysis delves into how GDPR applies to healthcare AI projects, encompassing data collection and decision-making stages, to reveal the ethical implications at each step. A comprehensive review of the literature categorizes research investigations into three main categories: Ethical Considerations in AI; Practical Chall… Show more

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Cited by 23 publications
(3 citation statements)
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“…Furthermore, guided by the principles of the 3Rs (replacement, reduction, and refinement) in animal research, there exists an ethical commitment to diminish the reliance on humans and animals in studies and to explore alternative methodologies, including in vitro and in silico models, for compound testing [ 42 ]. However, the ethical impact on industry and the need for guidelines to validate the predictions of these models, ensure transparency and interpretability, and prevent misuse of them highlight the importance of a balanced and accountable approach to harnessing the benefits of AI in drug development [ 43 , 44 ]. International organizations such as the OECD have developed guidance and recommendations for validating QSAR modeling, facilitating the regulatory acceptance and adoption of in silico -generated data, and ultimately aiming at minimizing the necessity for animals in toxicity studies [ 45 ].…”
Section: Discussionmentioning
confidence: 99%
“…Furthermore, guided by the principles of the 3Rs (replacement, reduction, and refinement) in animal research, there exists an ethical commitment to diminish the reliance on humans and animals in studies and to explore alternative methodologies, including in vitro and in silico models, for compound testing [ 42 ]. However, the ethical impact on industry and the need for guidelines to validate the predictions of these models, ensure transparency and interpretability, and prevent misuse of them highlight the importance of a balanced and accountable approach to harnessing the benefits of AI in drug development [ 43 , 44 ]. International organizations such as the OECD have developed guidance and recommendations for validating QSAR modeling, facilitating the regulatory acceptance and adoption of in silico -generated data, and ultimately aiming at minimizing the necessity for animals in toxicity studies [ 45 ].…”
Section: Discussionmentioning
confidence: 99%
“…This strong inclination towards understanding the multifaceted nature of AI reflects a recognition of the complexity and evolving ethical challenges that AI technologies bring to the healthcare sector (Prakash et al ., 2022). This trend underscores the pressing need for a comprehensive educational framework that not only introduces students to the technicalities of AI but also immerses them in the ethical, legal, and social implications of AI deployment in healthcare (Mohammad Amini et al ., 2023). It highlights again the necessity for curricula to evolve, integrating modules that address practical AI applications, ethical decision-making in AI contexts, and the role of AI in sustainable leadership.…”
Section: Discussionmentioning
confidence: 99%
“…Despite the various benefits and the potential of AI in medical education, some areas still require further investigation. These include clarifying the long-term implications of AI-driven learning methodologies for student performance, instructor–student interactions, and the ethical implications of AI [ 4 , 6 , 7 , 10 , 17 , 20 , 21 ].…”
Section: Introductionmentioning
confidence: 99%