Artificial Intelligence Technologies Supporting Clinical Judgement in Nursing: A Scoping Review

Area of Science:

• Nursing Informatics
• Clinical Judgment
• Artificial Intelligence

Background:

• Artificial intelligence (AI) integration in nursing practice requires understanding its impact on clinical judgment.
• Existing research often lacks theoretical frameworks, limiting insight into AI's interaction with cognitive processes.

Purpose of the Study:

• To map and analyze the interaction between artificial intelligence technologies and clinical judgment processes in nursing.
• To identify how AI supports nursing practice and education.

Main Methods:

• A comprehensive scoping review was conducted using JBI methodology.
• Searches included MEDLINE, CINAHL, Scopus, Web of Science, IEEE Xplore, and grey literature from January 2015.
• Eleven studies were included in the analysis.

Main Results:

• AI applications predominantly supported the 'noticing' and 'interpreting' phases of Tanner's Clinical Judgement Model.
• Predictive models and decision support systems were common AI tools identified.
• The 'responding' and 'reflecting' phases of clinical judgment received minimal attention in the reviewed studies.

Conclusions:

• Current AI research in nursing focuses on pattern recognition, neglecting the reflective processes crucial for expertise.
• Future research should explore AI's influence across all clinical judgment phases, especially in nursing education.
• AI integration must prioritize patient safety, professional autonomy, and quality of care.