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Artificial intelligence-enhanced clinical reasoning in nurse practitioners: A systematic review
Su-Ying Yu1, Hui-Ping Lin1, Ying-Mai Kung2
1Department of Nursing, College of Nursing, Chang Gung University of Science and Technology, Taoyuan, Taiwan.
Artificial intelligence (AI) tools can enhance nurse practitioners' (NPs) clinical reasoning, improving diagnostic accuracy and efficiency. However, safe implementation requires validation, ethical considerations, and digital literacy training for effective use.
Area of Science:
- Nursing Informatics
- Clinical Decision Support Systems
- Artificial Intelligence in Healthcare
Background:
- Nurse practitioners (NPs) rely on robust clinical reasoning skills.
- Artificial intelligence (AI) tools show potential in supporting NP competency development.
- A significant gap exists in systematic reviews of AI-enhanced clinical reasoning specifically for NPs.
Purpose of the Study:
- To systematically review and synthesize evidence on AI-enhanced clinical reasoning among NPs.
- To identify the applications and impact of AI on NP clinical reasoning processes.
- To evaluate the effectiveness and limitations of AI interventions in NP practice.
Main Methods:
- Systematic review adhering to PRISMA 2020 guidelines.
- Comprehensive searches across PubMed, Embase, and CINAHL databases up to July 2025.
- Inclusion of 13 studies (quantitative, qualitative, and systematic reviews) examining AI interventions for NP clinical reasoning, with risk of bias assessment.
Main Results:
- AI applications included real-time monitoring, decision support, simulations, and large language models.
- AI supported data gathering, hypothesis generation, diagnostic justification, and reflective judgment.
- Quantitative data indicated improved diagnostic accuracy, consistency, and efficiency; qualitative data highlighted AI as a supportive tool for diagnostic reasoning and patient-centered care, emphasizing transparency and integration.
Conclusions:
- AI tools show promise in strengthening NP clinical reasoning, enhancing diagnostic accuracy, decision consistency, and care efficiency.
- Safe and effective integration necessitates rigorous validation, standardized evaluation, ethical safeguards, and digital literacy.
- Limitations include diverse AI applications and a reliance on simulation-based evidence over real-world clinical data.
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