10.65033 From Automation to Accountability: A Systematic Review of The Role of CSR in Ethical Al Integration in Nursing Practice
Abstract
Artificial Intelligence (AI) is reshaping modern healthcare by enhancing diagnostics, predictive analytics, and robotic-assisted interventions. Despite its widespread use in radiology and administrative processes, AI adoption in nursing remains relatively limited due to the profession’s inherently human-centered nature, requiring empathy, real-time judgment, and complex interpersonal interactions. As the healthcare sector confronts rising nurse shortages and burnout, AI presents an opportunity to augment nursing care rather than replace it. From Clinical Decision Support Systems (CDSS) to virtual nursing assistants, AI tools offer promising solutions to streamline workflows and enhance patient safety. However, ethical concerns such as algorithmic bias, data privacy, and workforce displacement pose significant challenges. To address these issues, this study applies the Corporate Social Responsibility (CSR) framework, particularly the ISO 26000 guidelines, to evaluate ethical AI integration in nursing. To explore this relationship systematically, this study adopts a Systematic Literature Review (SLR) approach guided by PRISMA to synthesize current academic insights on CSR principles applied to AI in nursing. This systematic literature review (SLR) adheres to PRISMA guidelines and includes peer-reviewed literature from 2015 to 2024 sourced from PubMed, Google Scholar, and Semantic Scholar. Articles focusing on AI ethics, CSR in healthcare, patient safety, workforce…
How strong is the evidence?
Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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