The Impact and Issues of Artificial Intelligence in Nursing Science and Healthcare Settings

Among the most popular applications of artificial intelligence (AI), those used in healthcare represent the largest proportion in terms of usage and expectations. AI technologies are being developed, tested, evaluated, and applied to healthcare in many countries, often with limited involvement of nurses across settings and specialties. As AI becomes more advanced, accurate, practical, effective, efficient, and economical for nursing care, there are both opportunities and pressures to apply it. It is urgent to rethink which interventions should be performed by nurses versus AI devices, requiring critical thinking to separate roles in delivering appropriate patient care. The purpose of valuable technology is to solve problems or make improvements. Examples include speech recognition to speed and improve accuracy of nursing documentation and machine-learning models (e.g., nomograms, web calculators, early warning scores) to support clinical assessment and reduce in-hospital mortality. Considering nurses’ problems and challenges, AI may help now and in the future, but further development is needed to optimize nurse performance. Although research and development of AI in healthcare have increased, few studies move beyond proof-of-concept or laboratory experiments into real-world application, and even fewer evaluate impact on clinical outcomes. To meet new demands, AI should be integrated into nursing science and healthcare settings. This article aims to explore and discuss the impact of applying AI in nursing science and healthcare systems to provide appropriate nursing care and to assess current use to stimulate further research and development.

The paper synthesizes existing literature indicating growing AI adoption and potential in nursing and healthcare, while highlighting persistent gaps. Prior studies identify specific gaps in applying AI to outcomes research across diverse therapeutic areas and settings such as nursing homes, long-term care, and mental health. There is a need for careful consideration before incorporating AI into health technology assessment decision-making, particularly for clinical use. Evidence shows limited deployment and evaluation of AI in nonhospital care settings (e.g., home care, outpatient long-term care). Reviews and commentaries point to AI’s promise for enhancing nursing skills, enabling evidence-based and personalized care, improving documentation, and reducing errors, but also flag unintended consequences, including bias, trust, governance, and accountability concerns. The literature emphasizes the necessity of frameworks for development, validation, implementation, and governance, as well as attention to explainability, human factors, and training.

- Identified positive impacts of AI in nursing and healthcare settings: (1) expanded access to high-quality medical care through personalized interventions, monitoring, reduced errors, and lower costs; (2) improved organization and accessibility of electronic medical records as data volume grows; and (3) improved quality of services via enhanced efficiency, safety, access, and interdisciplinary collaboration, which can reduce time-consuming tasks and free nurses for direct patient care. - Documented key issues and risks: (1) bias in AI systems, including algorithmic and social bias (e.g., factors such as gender, race, measurement error), which can lead to suboptimal or inaccurate outcomes for certain groups; and (2) uncertainty about validity and reliability thresholds for AI algorithms to become standard of care, alongside evidence of clinical decision algorithms embedding racial bias. - Noted current applications and potential benefits relevant to nursing: speech recognition for more efficient documentation; machine-learning tools (e.g., nomograms, web calculators, early warning scores) to support clinical assessment and potentially reduce in-hospital mortality; and a range of emerging AI modalities (intelligent agents, ML/DL, NLP, RPA, administrative applications, explainable AI).

The analysis addresses the research aim by delineating how AI can support nursing care delivery through enhanced access, documentation, and service quality, thus aligning with goals of efficiency, safety, personalization, and cost-effectiveness. At the same time, it recognizes that AI may reproduce or amplify biases and introduces unresolved questions about clinical accountability and standards of care. These considerations underscore the importance of clearly defining nurse versus AI roles, establishing governance and validation frameworks, ensuring explainability, and engaging nurses in the development and evaluation process. Overall, AI’s potential to optimize nursing processes and outcomes is substantial, but realizing benefits while minimizing harms requires deliberate integration, ongoing evaluation, and attention to ethical, legal, and social implications.

Current observable impacts of AI in nursing and healthcare include expanding access to quality medical care, improving medical records, and enhancing service quality. These benefits support the urgency of integrating AI technologies to optimize healthcare services. However, issues such as bias and uncertainties around algorithms’ validity and reliability must be addressed, necessitating continuous monitoring, corrective actions, and updates to minimize errors. Future work should focus on rigorous real-world evaluations of clinical outcomes, robust governance and accountability frameworks, mitigation of algorithmic and social bias, and active involvement of nurses in AI design, implementation, and assessment.