Artificial intelligence in cardiac nursing practice: A systematic review of applications, challenges, and patient outcomes - Nova Listya Wahananingtyas; ⁠Rachmad Aprilio; Risnadhia Risnadhia; Marista Fiana; Fevi Padhila; Sabrina Rahmatillah Azhar (Institute of Computer Science (IOCS), 2026)
Cardiovascular diseases remain the leading cause of global mortality, requiring innovative approaches to improve cardiac care. This systematic review aimed to examine the applications of Artificial Intelligence (AI) in cardiac nursing practice, identify implementation challenges, and assess its impact on patient outcomes. The review was conducted in accordance with PRISMA 2020 guidelines, using PubMed, ScienceDirect, CINAHL, and Web of Science. Original studies published between 2020 and 2025 that explicitly addressed nursing roles in cardiac care were included. Eleven studies met the inclusion criteria and were appraised using the Joanna Briggs Institute (JBI) tools. The results show that AI applications, including ChatGPT and machine learning models, support cardiac nursing through clinical decision support, patient education, risk prediction, and home-based monitoring. These applications were associated with improved nursing efficiency, enhanced patient self-management, early detection of clinical deterioration, and potential reduction in hospitalization. However, challenges such as data accuracy, ethical concerns, algorithm transparency, and limited digital literacy among nurses were consistently reported. In conclusion, AI has strong potential to enhance evidence-based and patient-centered cardiac nursing practice. Successful integration requires ethical governance, adequate training, and interdisciplinary collaboration to ensure safe and effective implementation.