Artificial Intelligence in Nursing Practice: Applications, Opportunities, Challenges, and Future Perspectives

Background: The field of nursing and broader healthcare systems is undergoing a rapid evolution due to artificial intelligence, which optimizes clinical choices, patient oversight, administrative record-keeping, and the overall distribution of medical services. AI refers to computer-based technologies capable of learning, reasoning, and performing tasks similar to human intelligence. This review article highlights the history, stages, principles, applications, advantages, challenges, and limitations of AI in nursing. AI technologies such as machine learning, robotics, sensor-based devices, speech recognition, tele-health, and remote monitoring have significantly enhanced clinical and community nursing care.{1} AI supports nurses in clinical decision-making, medication adherence, risk assessment, electronic documentation, and patient safety while reducing workload and improving efficiency. The article also discusses the role of AI during pandemics and in rural healthcare services. Despite its benefits, challenges such as lack of trained personnel, infrastructure limitations, high costs, and reduced human interaction remain significant concerns. {2} When successfully incorporated into academic curricula and clinical environments, artificial intelligence fortifies medical frameworks and fosters superior, individualized patient treatment.
Methods: A narrative review of published literature was conducted using electronic databases including PubMed, Scopus, CINAHL, and Web of Science. Relevant studies addressing AI in nursing, improving medication adherence, digital documentation, and remote patient monitoring were reviewed and synthesized.
Results: The review demonstrated that artificial intelligence (AI) has emerged as a transformative technology in nursing practice, education, and healthcare delivery. Evidence from the literature indicates that AI applications support clinical decision-making, patient monitoring, risk assessment, medication adherence, documentation, tele health, and remote patient management. In clinical settings, AI-powered decision support systems assist nurses in identifying patient deterioration, predicting adverse events, and prioritizing interventions. Sensor-based technologies and wearable devices enable continuous monitoring of physiological parameters such as heart rate, respiratory rate, blood pressure, oxygen saturation, and physical activity, thereby improving patient safety and early detection of complications.
Conclusion: Artificial intelligence is rapidly reshaping nursing practice and healthcare delivery by enhancing clinical decision-making, improving patient monitoring, supporting documentation, and expanding access to healthcare services through tele-health and remote monitoring. AI technologies have the potential to improve efficiency, patient safety, and quality of care while reducing the workload of nursing professionals. However, successful integration of AI into nursing requires adequate infrastructure, workforce training, ethical governance, and policies that ensure patient privacy, transparency, and equitable access to technology. AI should be viewed as a supportive tool that complements, rather than replaces, the clinical judgment, compassion, and human-centered care provided by nurses.