The ethical use of artificial intelligence (AI) in healthcare has garnered significant attention in recent years. Various stakeholders, including private companies, research institutions, and public-sector organizations, have issued principles and guidelines for ethical AI. However, there is an ongoing debate about the definition of "ethical AI" and the necessary ethical requirements, technical standards, and best practices for its realization (Jobin & Ienca, 2019). In the context of healthcare, governments must ensure that AI solutions are used ethically and transparently with a focus on protecting patient privacy (Saeed et al., 2023). The application of AI in healthcare has raised concerns about its ethical implications, leading to the emergence of numerous ethical guidelines for the use of AI and data in healthcare (Smallman, 2022). Despite the promising future of AI in healthcare, its implementation faces several legal and ethical challenges (Meszaros et al. 2022).

Furthermore, the ethical, regulatory, and practical considerations required for the widespread use or deployment of AI-driven intervention research in global health have been identified as areas of concern (Schwalbe and Wahl 2020). A comprehensive understanding of the ethical implications of AI in healthcare, including privacy, security, bias, transparency, accountability, informed consent, and human interaction, is essential (Amedior 2023). To effectively address ethical issues, an” embedded ethics' approach has been proposed that emphasizes the integration of robust ethical considerations into the practical development of medical AI through collaboration between ethicists and developers (McLennan et al., 2022). While conversations around the ethical use of AI have emerged in recent years, there is a need to ensure that ethical practices impact responsible AI towards digital health (Jennings & Cox, 2023; Wamba & Queiroz, 2021).

Operationalizing ethics in AI for healthcare requires mapping human values to ethical principles and conceptualizing ethical issues arising in AI for healthcare (Solanki et al., 2022). However, it has been argued that the current AI ethics field is largely ineffective and prone to manipulation, thus emphasizing the need for a more robust ethical framework (Rességuier & Rodrigues, 2020). The responsibility for realizing a vision of ethical and accountable AI in healthcare extends to clinicians, data scientists, tech companies, ethicists, and regulators (Hindocha & Badea, 2021). Additionally, the literature on patients' views on the use of wearable devices and AI in healthcare is limited, highlighting the need for further research in this area (Tran & Ravaud, 2019).

The abstract nature of AI ethics principles has made it challenging for practitioners to operationalize them, emphasizing the need for co-designed checklists to understand organizational challenges and opportunities around fairness in AI (Madaio et al., 2020). Ethical governance is considered essential for building trust in robotics and AI systems, with a growing consensus that robots and AIs should be designed to reflect the ethical and cultural norms of their users and societies (Winfield & Jirotka, 2018). The implementation of AI systems requires ethical considerations because of their unique nature and influence on various stakeholders (Vakkuri et al., 2019).

In conclusion, the ethical use of AI in healthcare requires a comprehensive understanding of the ethical implications, collaboration between stakeholders, and the development of robust ethical frameworks to ensure responsible and accountable AI practices.

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