Artificial intelligence-enhanced clinical reasoning in nurse practitioners: A systematic review

This systematic review aimed to synthesize the current evidence on artificial intelligence (AI)-enhanced clinical reasoning among nurse practitioners (NPs).

NPs require strong clinical reasoning skills and AI-based tools may support the development of these competencies; however, empirical evidence regarding their effectiveness remains limited. The strengthened literature review clearly identifies a critical gap, the absence of a prior systematic review specifically examining AI-enhanced clinical reasoning among NPs and will provid a strong rationale for the present review.

Systematic review following PRISMA 2020 guidelines.

Searches were conducted in PubMed, Embase and CINAHL through July 2025. Of the 429 records retrieved, 13 met inclusion criteria. Eligible studies examined AI interventions targeting clinical reasoning among NPs. Risk of bias was assessed using the Critical Appraisal Skills Programme checklists and Joanna Briggs Institute tools. Data were extracted on study design, population, AI application domain, outcomes and quality appraisal.

Thirteen studies were included: seven quantitative quasi-experimental, intervention validation, or retrospective cohort studies; three qualitative studies; and three systematic reviews. AI applications ranged from real-time monitoring and decision-support systems to simulation platforms and large language models, which supported clinical reasoning domains such as data gathering, hypothesis generation, diagnostic justification and reflective judgment. Quantitative studies showed improvements in diagnostic accuracy, consistency, efficiency and data collection, while qualitative studies found that NPs view AI as a supportive tool that enhances diagnostic reasoning and patient-centered care, while emphasizing the need for transparency, interpretability and workflow integration.

AI tools may strengthen NPs' clinical reasoning by improving diagnostic accuracy, decision consistency and care efficiency, but their safe use requires rigorous validation, standardized evaluation, ethical safeguards and digital literacy training. Limitations include heterogeneous AI applications across professional groups and a predominance of simulation-based evidence over real-world clinical evaluations.