![]() Furthermore, in Japan, where approximately 30% of the population is older than 65 years, nurses use AI-powered robots in long-term care (LTC) homes and hospital settings to assist patients with activities of daily living and to provide social interaction. Nurses in a Canadian home health organization use virtual health care assistant apps (chatbots) to support persons who have been diagnosed as having mental health conditions. Predictive analytics have been integrated into smart health care technologies to predict health status changes among patients in hospitals and community-based settings, enabling nurses to proactively intervene and initiate appropriate interventions. Predictive analytics that use ML technology can identify patterns in data and predict future patient outcomes such as a patient’s risk of developing pressure ulcers. As more data are presented to the ML application, the computer learns from the data and corrects the output. ML is a subset of AI that uses algorithms to derive knowledge from data and interpret the data without being explicitly programmed. Within the nursing profession, many different types of AIHTs are already being used or trialed, including predictive analytics that use machine learning (ML), virtual health care assistant apps, and robotic devices. There is an undeniable relationship that exists between human beings, technology, and the environment, and these relationships need to be further examined through the lens of nursing practice to effectively leverage AIHTs to augment the patient experience and health outcomes. Over the last few decades, the volume of technology has increased significantly in many health systems around the world, and consequently, the impact of technology on the nursing role has become an increasing focus within nursing research. The increased adoption of AIHTs in health care, driven partially by growing consumer demands for digital health technologies in clinical practice, may present a new means of addressing health challenges in the 21st century by enhancing workflows and supporting clinical decision making. It is critical for nurses to gain a broader understanding of these emerging technologies to shape the future of the profession and influence decisions about aspects of nursing care that can be safely performed by AIHTs. Without an understanding of the existing evidence on this topic, nurses will not fully appreciate the implications of AI for nursing practice, policy, administration, and research. Furthermore, there is limited research on nurses’ roles in influencing the implementation of AIHTs and the co-design of these technologies to protect patients’ safety and privacy and preserve person- and family-centered compassionate care. However, no published scoping reviews have mapped the breadth and depth of evidence concerning the current or predicted influences of AIHTs on the nursing profession and compassionate nursing care. Recent studies and expository papers have begun to explore the influence of AIHTs on nursing roles, workflows, processes, and patient care. Therefore, strong nursing leadership is required to drive this change and ensure the continued delivery of high-quality, person-centered compassionate nursing care. It is anticipated that emerging trends in AIHTs will change the nature of the nurse-patient relationship. Compassionate care helps nurses to shift their focus from simply completing tasks to engaging fully with patients by recognizing and responding to their individual needs, promoting well-being, and forming therapeutic relationships essential to effective care. Within the nursing profession, the delivery of person- and family-centered compassionate care is a core and valued component of nursing theory and practice and is reflected in numerous nursing practice frameworks. Nurses are the largest group of health professionals, and they currently practice in diverse settings and roles across the 5 domains of nursing activity identified by the Registered Nurses’ Association of Ontario (RNAO ie, administration, education, clinical practice, policy, and research). Given their potential to enhance workflows and guide clinical decision making, AIHTs are predicted to directly and indirectly transform the nursing profession in various ways. AI health technologies (AIHTs) are becoming increasingly prevalent in clinical settings worldwide, and global spending on these technologies is predicted to exceed US $36 billion by 2025. ![]() Artificial intelligence (AI) is a branch of computer science that focuses on building machines that can perform tasks that typically require human intelligence, such as decision making, speech recognition, visual perception, and language translation.
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