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Pressure Injury Prediction in Intensive Care Units Using Artificial Intelligence: A Scoping Review.

José Alves^1,2, Rita Azevedo^1,2, Ana Marques^1,3

¹Center for Interdisciplinary Research in Health, Faculty of Health Sciences and Nursing, Universidade Católica Portuguesa, 4169-005 Porto, Portugal.

Nursing Reports (Pavia, Italy)

|May 8, 2025

Summary

This summary is machine-generated.

Artificial intelligence (AI) can predict pressure injuries in intensive care units. Machine learning models show promise in reducing patient harm and healthcare burdens.

Keywords:

artificial intelligence critical care critical care nursing intensive care units pressure injury

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Area of Science:

Critical Care Medicine
Health Informatics
Artificial Intelligence in Healthcare

Background:

Pressure injuries are a significant healthcare challenge, especially in intensive care units (ICUs).
Existing risk assessment tools for pressure injuries have limitations.
Artificial intelligence (AI) offers a novel approach for pressure injury prediction in critical care.

Purpose of the Study:

To conduct a scoping review of AI technologies for pressure injury prediction in ICU patients.
To identify knowledge gaps and guide future research in this area.

Main Methods:

Adherence to the Joanna Briggs Institute's methodology for scoping reviews.
Prospective registration of the study protocol on the Open Science Framework platform.

Main Results:

The review included 14 studies, predominantly using machine learning models trained on electronic health records (EHRs).
Models utilized between 6 and 86 variables for training.
Clinical deployment was reported in only two studies, showing reduced nursing workload, fewer hospital-acquired pressure injuries, and shorter ICU stays.

Conclusions:

AI technologies offer a dynamic and innovative method for effective and timely pressure injury risk identification and prediction.
This review synthesizes current literature and provides direction for future research and development in AI for pressure injury prevention.