ArticlePloS one2022
A time motion study of manual versus artificial intelligence methods for wound assessment.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- Artificial Intelligence Methods for Diagnostic and Decision-Making Assistance in Chronic Wounds: A Systematic Review.Journal of medical systems · 2025Pooled it
- Development of a mobile application for wound severity detection and modern dressing selection.Journal of Taibah University Medical Sciences · 2026Article
- Rapid noncontact laser speckle imaging for perfusion-based differentiation of wound necrosis.iScience · 2026Article
- Using Artificial Intelligence-Enabled Digital Health Tools for Wound Management: A Scoping Review.International wound journal · 2026Article
- Effects of a transformer-based AI-based application to support incontinence-associated dermatitis and pressure injury assessment, nursing care and documentation: Controlled pilot intervention study.International journal of nursing studies advances · 2026Article
- Clinical and Operational Outcomes Associated with the Adoption of a Digital Wound Care Solution in Home Health Settings.Healthcare (Basel, Switzerland) · 2026Article
- The Power of Multimodality in Multimodal Large Language Models, Unimodal ChatGPT 5.0, and Human Clinical Experts on a Wound Care Certification Examination: Cross-Sectional Comparative Study.JMIR formative research · 2026Article
- Artificial Intelligence-Enhanced Wound Care to Improve Access, Efficacy, and Equity in Wound Care for Older Adults in Rural and Remote Regions of Canada.JMIR nursing · 2026Article
- Stimuli-Responsive Nanomaterial-Based Biosensor Structures for Wound Care: pH, ROS, and Temperature Sensing Strategies.Micromachines · 2026Review
- POC Sensor Systems and Artificial Intelligence-Where We Are Now and Where We Are Going?Biosensors · 2025Review
- Controlled Pilot Intervention Study on the Effects of an AI-Based Application to Support Incontinence-Associated Dermatitis and Pressure Injury Assessment, Nursing Care and Documentation: Study Protocol.Research in nursing & health · 2025Article
- Empowering Patients and Caregivers to Use Artificial Intelligence and Computer Vision for Wound Monitoring: Nonrandomized, Single-Arm Feasibility Study.Journal of participatory medicine · 2025Article
- Assessing Clinician Consistency in Wound Tissue Classification and the Value of AI-Assisted Quantification: A Cross-Sectional Study.International wound journal · 2025Article
- Impact of digital wound care solution on healing time: A descriptive study in home health settings.PLOS digital health · 2025Article
- Clinical, Operational, and Economic Benefits of a Digitally Enabled Wound Care Program in Home Health: Quasi-Experimental, Pre-Post Comparative Study.JMIR nursing · 2025Article
- Current status, challenges, and prospects of artificial intelligence applications in wound repair theranostics.Theranostics · 2025Review
- Research trends and hot topics of wearable sensors in wound care over past 18 years: A bibliometric analysis.Heliyon · 2024Article
- A Time Study Analysis of Fluoride Varnish Application in Pediatric Well Visits to Address Health Disparities among Children.Journal of applied research on industrial engineering · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis time-motion study explored the amount of time clinicians spent on wound assessments in a real-world environment using wound assessment digital application utilizing Artificial Intelligence (AI) vs. manual methods. The study also aimed at comparing the proportion of captured quality wound images on the first attempt by the assessment method.
methodsClinicians practicing at Valley Wound Center who agreed to join the study were asked to record the time needed to complete wound assessment activities for patients with active wounds referred for a routine evaluation on the follow-up days at the clinic. Assessment activities included: labelling wounds, capturing images, measuring wounds, calculating surface areas, and transferring data into the patient's record.
resultsA total of 91 patients with 115 wounds were assessed. The average time to capture and access wound image with the AI digital tool was significantly faster than a standard digital camera with an average of 62 seconds (P<0.001). The digital application was significantly faster by 77% at accurately measuring and calculating the wound surface area with an average of 45.05 seconds (P<0.001). Overall, the average time to complete a wound assessment using Swift was significantly faster by 79%. Using the AI application, the staff completed all steps in about half of the time (54%) normally spent on manual wound evaluation activities. Moreover, acquiring acceptable wound image was significantly more likely to be achieved the first time using the digital tool than the manual methods (92.2% vs. 75.7%, P<0.004).
conclusionsUsing the digital assessment tool saved significant time for clinicians in assessing wounds. It also successfully captured quality wound images at the first attempt.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.