Previously submitted to: JMIR Nursing (no longer under consideration since Jan 28, 2026)
Date Submitted: Jun 15, 2025
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Artificial Intelligence Linguistic Models in Family Medicine Nursing Practice: Cross-Sectional Questionnaire and Interview Study
ABSTRACT
Background:
Artificial intelligence (AI), particularly language models, is transforming nursing practice in family medicine by enhancing communication accuracy and reducing diagnostic errors.
Objective:
To assess the clinical usability and impact of AI-based linguistic models on nurses’ decision-making confidence and patient education in family medicine settings.
Methods:
A cross-sectional mixed-methods study was conducted with 120 registered nurses at three urban family medicine clinics. Quantitative data were collected via pre- and post-intervention surveys; qualitative insights were gathered through semi-structured interviews.
Results:
Decision-making confidence significantly increased (mean score rose from 0.8 ± 0.7 to 3.4 ± 0.6; p < 0.001). Patient comprehension improved by 28%. Interviews revealed enhancements in communication and efficiency, alongside concerns about ethical implications and AI dependency.
Conclusions:
Conclusions:
AI language models significantly support nursing decision-making and patient education in family medicine. Further research should explore long-term outcomes and ethical considerations.
Citation
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Copyright
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