AI-Assisted Skin Assessment for Pressure Injury Prevention in Critical Care Nurses (IT-PIP)

A Randomized Controlled Trial on the Application of Artificial Intelligence (AI) in Skin Assessment for Pressure Injury Prevention and Staging by Critical Care Nurses

The goal of this clinical trial is to learn whether an artificial intelligence (AI)-assisted skin assessment tool can improve the accuracy of pressure-injury staging in critical-care nurses. The study also aims to understand whether the AI tool increases nurses' knowledge and confidence in performing skin assessments. The main questions it aims to answer are:

Does AI-assisted assessment improve the accuracy of pressure-injury staging compared with standard visual assessment?

Does the use of AI improve nurses' knowledge and confidence related to skin assessment and pressure-injury staging?

Researchers will compare nurses who use an AI-assisted mobile application with nurses who perform standard manual assessments to see whether the AI tool improves staging accuracy and supports early identification of pressure injuries.

Participants will:

Complete brief questionnaires about their knowledge and confidence before and after training

Perform skin assessments on their assigned ICU patients using either standard methods or the AI tool.

Have their assessments compared with those of a blinded wound-care specialist, who will determine the most accurate staging

Study Overview

• Status: Recruiting
• Conditions: The Study Focuses on Skin Assessment and PI Staging in ICU Patients
• Intervention / Treatment: Other: ChatGPT Skin Assessment

Detailed Description

Pressure injuries remain a significant and largely preventable complication among critically ill patients, with ICU populations at particularly high risk due to immobility, hemodynamic instability, and complex medical needs. At KFSHRC-Jeddah, more than half of all hospital-acquired pressure injuries reported in 2024 occurred in critical-care settings, underscoring ongoing challenges in early detection and consistent staging. Although the organization follows evidence-based practices and uses tools such as the Braden Scale and NPIAP staging guidelines, variability in nurses' knowledge, skill, and confidence continues to influence prevention quality and accuracy of assessment.

Traditional skin assessment relies primarily on visual inspection and clinical judgement, which can lead to inconsistent interpretation of early tissue changes, particularly in darker skin tones, deep tissue injuries, and moisture-associated skin damage. These limitations highlight the need for innovative approaches that support more consistent and objective staging.

Artificial intelligence (AI)-assisted image recognition has emerged as a potentially valuable adjunct to standard nursing assessment. By analyzing skin characteristics such as color, texture, and contour, AI tools may assist nurses in identifying early-stage changes and provide decision support aligned with NPIAP criteria. Integrating AI into routine practice has the potential to enhance early detection, improve staging accuracy, and reduce practice variation.

This randomized controlled trial evaluates the use of an AI-assisted mobile application compared with standard manual skin assessment performed by critical-care nurses. The intervention uses an image-recognition tool that analyzes standardized photographs of high-risk skin areas and provides staging recommendations based on NPIAP definitions. Nurses in the control group will continue performing traditional visual and palpation-based assessments according to existing hospital protocols.

All participating nurses will receive pre-intervention education on pressure injury prevention, comprehensive skin assessment, and NPIAP staging to establish a consistent baseline. The intervention group will undergo additional training on standardized image capture to ensure appropriate lighting, distance, and positioning. A blinded wound-care specialist will independently review all assessments and images; this external review serves as the reference standard for evaluating accuracy and inter-rater reliability.

In addition to examining staging accuracy, the study will assess changes in nurses' knowledge and confidence before and after the intervention using validated instruments. It will also explore the feasibility and acceptability of integrating AI into ICU workflows. The findings are expected to inform how AI technology can support nursing practice, enhance clinical decision-making, and help reduce the incidence of hospital-acquired pressure injuries in critical-care environments.

• Study Type: Interventional
• Enrollment (Estimated): 90
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Dr. Jennifer De Beer, PhD; Phone Number: +966559232671; Email: debeerj@kfshrc.edu.sa

Study Locations

• Saudi Arabia
  • Mecca Region
    • Jeddah, Mecca Region, Saudi Arabia, 21423
      • Recruiting
      • King Faisal Specialist Hospital and Research Center- Jeddah
      • Contact: Abeer Omer, MBBS; Phone Number: +966114423657/+966503284853; Email: aomer@kfshrc.edu.sa
      • Contact: Aziz Ashraf Afshan, MBCHB; Phone Number: +966554965750; Email: aali01@kfshrc.edu.sa
      • Principal Investigator: Jennifer de Beer, PhD Nursing

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

Description

Inclusion criteria

• Nurses working within the organisation for at least 6 months
• Nurses involved in direct patient care for over 50% of their work time.
• Skin assessments and staging for patients at risk for developing pressure injuries (Using the Braden Scoring system).
• Adult Patients (18 years and older)
• Patients who are currently admitted to the ICU and are receiving critical care treatment.
• No current severe skin conditions patients without active severe dermatological conditions (e.g., large open wounds, severe rashes) that would interfere with the AI-based skin assessment process.

Exclusion criteria

• Nurses working within the organization for less than 6 months
• Nurses involved in direct patient care for less than 50% of their work time
• End-of-Life Care or Terminal Illness- patients receiving end-of-life care or those with a terminal diagnosis, where the prevention of pressure injuries may not be a priority and where participation in the study may not align with their care goals.
• Severe or active dermatological conditions- patients with active skin conditions such as severe rashes, burns, or other dermatological issues that could interfere with accurate skin assessments by AI or confound the study results.
• Recent Skin Grafts or Advanced Wound Care- patients who have recently undergone skin grafts or those receiving complex wound care treatments that are outside the scope of typical pressure injury prevention practices.
• Inability to Maintain Required Positioning for Skin Assessment- patients who are physically unable to remain in the necessary position for the skin assessments, either due to severe mobility restrictions or critical medical conditions.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Prevention
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Single

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
No Intervention: Standard Assessment; Standard assessment
Other: ChatGPT-Assisted Skin Assessmen; Nurses will use ChatGPT-based AI support to assist in skin assessment and staging.	Other: ChatGPT Skin Assessment; ChatGPT Skin Assessment

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Agreement Between Nurse and Expert Skin Assessment and Pressure Injury Staging Using NPIAP Criteria	The primary outcome explicitly measures agreement between nurse-assigned and expert-assigned pressure injury stages using the National Pressure Injury Advisory Panel (NPIAP) staging criteria, rather than stating a study objective. Agreement will be quantified using Cohen's Kappa statistic, and accuracy will be summarized as the percentage of nurse-assigned stages that exactly match expert-assigned stages. Agreement analyses will be conducted separately for manual nurse assessments and AI-assisted nurse assessments, allowing clear and reportable comparison between study groups. Agreement outcomes will be summarized across individual assessment domains, including erythema, discoloration, edema, temperature, and overall pressure injury staging, using quantitative agreement metrics. The primary outcome will be assessed from Day 1 through 6 months.	Day 1 through 6 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Knowledge- Change From Baseline in Nurse Knowledge Score on the Pressure Ulcer Prevention Knowledge Assessment Instrument (PUPKAI)	Nurse knowledge related to pressure injury prevention and staging will be measured using the Pressure Ulcer Prevention Knowledge Assessment Instrument (PUPKAI), a validated 26-item multiple-choice questionnaire. Each correct response will be scored as one point, and item scores will be summed to generate a total knowledge score ranging from 0 to 26, with higher scores indicating greater knowledge. Total PUPKAI scores will be calculated at two prespecified time points: Day 1 (baseline, prior to the intervention) and Day 1 (immediately post-intervention). The outcome will be reported as change from baseline in total PUPKAI score, calculated as the post-intervention score minus the baseline score. For statistical analysis, descriptive statistics will be used to summarize baseline and post-intervention scores. Within-group changes in PUPKAI scores will be analyzed using paired statistical tests (paired t-test or Wilcoxon signed-rank test, depending on data distribution).	Day 1 (Baseline) and Day 1 (Immediately Post-intervention)
Change From Baseline in Nurse Confidence Score on the Skin Assessment Confidence Scale (SACS)	Nurse confidence in skin assessment and pressure injury staging will be measured using the Skin Assessment Confidence Scale (SACS), a 10-item Likert-scale instrument. Each item is scored on a 5-point scale, and item scores are summed to generate a total confidence score ranging from 10 to 50, with higher scores indicating greater confidence in performing skin assessments and applying pressure injury staging criteria. Total SACS scores will be calculated at two prespecified time points: Day 1 (baseline, prior to the intervention) and Day 1 (immediately post-intervention). The outcome will be reported as change from baseline in total SACS score, calculated as the post-intervention score minus the baseline score. For statistical analysis, descriptive statistics will be used to summarize baseline and post-intervention confidence scores. Within-group changes in SACS scores will be analyzed using paired statistical tests (paired t-test or Wilcoxon signed-rank test, depending on data distr	Day 1 (Baseline) and Day 1 (Immediately Post-intervention)

Collaborators and Investigators

• Sponsor: King Faisal Specialist Hospital & Research Center

Study record dates

Study Major Dates

• Study Start (Actual): November 24, 2025
• Primary Completion (Estimated): February 28, 2026
• Study Completion (Estimated): May 16, 2026

Study Registration Dates

• First Submitted: November 24, 2025
• First Submitted That Met QC Criteria: December 18, 2025
• First Posted (Actual): January 6, 2026

Study Record Updates

• Last Update Posted (Actual): January 6, 2026
• Last Update Submitted That Met QC Criteria: December 18, 2025
• Last Verified: November 1, 2025

More Information

Terms related to this study

• Keywords: critical care; intensive care; Artificial intelligence; nurses; Pressure injuries
• Additional Relevant MeSH Terms: Skin Diseases; Skin Ulcer; Skin and Connective Tissue Diseases; Pressure Ulcer
• Other Study ID Numbers: RAC 2251201; Research Ethics Committee (Other Identifier: The Research Ethics Committee of the College of Dentistry, University of Baghdad. Project No. 1026125)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: Individual participant data (IPD) will not be shared publicly because the dataset includes clinical images and sensitive nursing performance data collected within a single hospital site. These data could potentially identify participants, and institutional policy at King Faisal Specialist Hospital and Research Centre restricts the external sharing of raw clinical data. Aggregated and de-identified summary results will be available upon reasonable request following publication of study findings.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No