The Efficacy and Ethics of AI-Powered Clinical Decision Support Systems in Nursing Practice: A Systematic Review
Abstract
Background: Artificial Intelligence integration into Clinical Decision Support Systems is rapidly changing nursing practice and ushering in new, unparalleled levels of data analysis and clinical guidance. These AI-facilitated tools are said to improve diagnostic accuracy and optimize treatment planning, but they also raise profound ethical challenges, such as algorithmic bias, accountability gaps, and a devaluation of nursing intuition.
Aim: This systematic review aims to synthesize evidence concerning the effectiveness of AI-powered CDSS in enhancing diagnostic accuracy and treatment planning by nurses, while considering ethical and bias issues related to the use of such technology.
Methods: A systematic search of the literature was carried out across PubMed, CINAHL, Scopus, Web of Science, and IEEE Xplore for literature published between 2013-2025. Experimental, observational, and qualitative studies reporting evaluations of AI-CDS tools in nursing contexts were considered for review.
Results: Evidence shows that AI-CDS has great potential for improving early warning scores and reducing diagnostic errors, especially in sepsis detection and assessment of risk for pressure injuries. However, the performance of such systems significantly varies between the systems and clinical contexts. Ethically, these systems raise critical concerns regarding data bias propagation, transparency deficits ("black box" problem), and shifts in nursing responsibility and autonomy.
Conclusion: AI-driven CDSSs are a double-edged innovation in nursing. While exhibiting quantifiable advantages in clinical decision-making, their successful integration requires solid validation, mitigation strategies for bias, and ethical frameworks that preserve the human elements of nursing care.
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References
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Authors
Copyright (c) 2024 Ashwag Sultan Alenazi, Jawaher Abdullah Alrsheedi, Khalid Abdullah alrshidi, Reema Mohammed Nasser Alshehri, Huda Mohammed Hassan Al Dehimi, Wala Ghalib Faisl Alrukhaimi, Ahmed Ibrahim ALnogimish, Haya Saud Alanazi, Abdulrahman muteb Almutairi, khalid Maseer Almutairi, Talal Ayed Alotaibi, Afaf Mutiq Raja Alrasheedi, Fatimah Abdullah Alrashdi, Abeer Mohammed Almutairi
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