Ethical and regulatory considerations in the use of AI and machine learning in nursing: A systematic review

Aim: This study systematically explores the ethical and regulatory considerations surrounding the integration of artificial intelligence (AI) and machine learning (ML) in
nursing practice, with a focus on patient autonomy, data privacy, algorithmic bias, and
accountability.
Background: AI and ML are transforming nursing practice by enhancing clinical
decision-making and operational efficiency. However, these technologies present significant ethical challenges related to ensuring patient autonomy, safeguarding data privacy,
mitigating algorithmic bias, and ensuring transparency in decision-making processes.
Current frameworks are not sufficiently tailored to nursing-specific contexts.
Methods: A systematic review was conducted, adhering to PRISMA guidelines. Six
major databases were searched for studies published between 2000 and 2024. Seventeen
studies met the inclusion criteria and were included in the final analysis.
Results: Five key themes emerged from the review: enhancement of clinical decisionmaking, promotion of ethical awareness, support for routine nursing tasks, challenges
in algorithmic bias, and the importance of public engagement in regulatory frameworks.
The review identified critical gaps in nursing-specific ethical guidelines and regulatory
oversight for AI integration in practice.
Discussion: AI technologies offer substantial benefits for nursing, particularly in
decision-making and task efficiency. However, these advantages must be balanced
against ethical concerns, including the protection of patient rights, algorithmic transparency, and bias mitigation. Current regulatory frameworks require adaptation to meet
the ethical needs of nursing.
Conclusion and implications for nursing and health policy: The findings emphasize
the need for the development of nursing-specific ethical guidelines and robust regulatory frameworks to ensure the responsible integration of AI technologies into nursing
practice. AI integration must uphold ethical principles while enhancing the quality of
care.