Effectiveness of natural language intelligence technology in chronic diseases nursing: A systematic review and meta-analysis - PubMed

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Chronic diseases present a significant global burden, with challenges in managing unstructured data for continuous care.
Natural language intelligence technology (NLIT) shows potential in improving chronic disease nursing.
A systematic review and meta-analysis was conducted following PRISMA guidelines, analyzing studies from 2014 to 2024.
NLIT demonstrated effectiveness in COPD management, reducing 30-day readmission rates by 27.9% via an ANN-powered app.
In diabetes care, NLIT improved self-management behaviors and increased diabetic retinopathy referral compliance by 19.34%.
Stroke rehabilitation saw enhanced motor function and shoulder range of motion with NLIT intervention.
NLIT achieved 88.46% accuracy in predicting COPD exacerbations and 92% diagnostic accuracy for diabetes using GPT-4.
Meta-analysis showed NLIT benefits for clinical outcomes with a pooled RR of 1.20, though with substantial heterogeneity.
Five studies were rated as low risk of bias, with one having moderate risk due to hypothetical data.
NLIT is valuable for personalized care but faces challenges like data heterogeneity and bias, requiring further refinement.

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