Ethical and regulatory considerations in the use of AI and machine learning in nursing: A systematic review
Abstract
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 decision-making, 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…
How strong is the evidence?
Evidence Hub reads this as a systematic review, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
Read the paper
Related studies
- Improving sleep quality leads to better mental health: A meta-analysis of randomised controlled trials
- Tocilizumab in patients with severe COVID-19: a retrospective cohort study
- A new aging measure captures morbidity and mortality risk across diverse subpopulations from NHANES IV: A cohort study
- Fruit, vegetable, and legume intake, and cardiovascular disease and deaths in 18 countries (PURE): a prospective cohort study
- Are noise and air pollution related to the incidence of dementia? A cohort study in London, England
- Long COVID symptoms and duration in SARS-CoV-2 positive children — a nationwide cohort study
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.