Applications of Artificial Intelligence in Medical Surgical Nursing: An Integrative Review with a Holistic Patient-Centered Perspective

Abstract

Background: Medical-surgical patients frequently experience anxiety, depression, fear, uncertainty, stress, sleep disturbances, and emotional distress associated with acute illness, surgery, chronic disease, and hospitalization. Artificial intelligence may support nurses in identifying psychosocial needs, facilitating early recognition of psychological distress, enhancing patient education, and promoting holistic patient-centered care. Artificial intelligence has rapidly emerged as one of the most transformative innovations in healthcare, fundamentally changing the way healthcare professionals deliver patient care, manage clinical information, and support evidence-based decision-making. Medical-surgical nursing requires comprehensive, patient-centered care that addresses physical, psychological, educational, and social dimensions of health. Artificial intelligence is increasingly being integrated into healthcare systems to support clinical decision making, improve patient safety, enhance workflow efficiency, and promote individualized care.

Aim: This integrative review aimed to synthesize the existing evidence regarding the applications of artificial intelligence in medical-surgical nursing, identify major themes across the literature, examine reported benefits and implementation challenges, and propose future directions for nursing practice, education, leadership, and research.

Methods: An integrative review methodology guided by Whittemore and Knafl's framework was employed. Five stages were as follows: problem identification, literature search, data evaluation, data analysis, and presentation of findings. The review synthesized evidence from the studies contained in the author's dataset, which included peer-reviewed publications addressing artificial intelligence applications relevant to medical-surgical nursing. Data were extracted regarding study characteristics, Artificial Intelligence technologies, healthcare settings, principal findings, and implications for nursing practice. Constant comparative analysis was used to generate overarching themes.

Result: The reviewed literature demonstrates a substantial increase in Artificial Intelligence research within medical-surgical nursing during recent years. Nine major themes emerged, including Clinical decision support, Predictive analytics, Patient monitoring, Medication safety, Perioperative nursing, Wound care, Nursing informatics, Nursing education, and Ethical governance. Collectively, the evidence suggests that Artificial Intelligence has considerable potential to improve healthcare quality, strengthen patient safety, reduce nurses' cognitive workload, and facilitate evidence-based clinical practice. However, successful implementation requires appropriate education, digital competency, ethical oversight, organizational readiness, and interdisciplinary collaboration.

Conclusion: Artificial intelligence is progressively reshaping medical-surgical nursing by augmenting clinical decision-making and supporting high-quality patient care. Future research should emphasize implementation science, clinical effectiveness, ethical governance, and competency-based education to ensure responsible integration of AI into medical-surgical nursing practice.