ArticleBMC medical education2025
Artificial intelligence-assisted personal protective equipment donning and doffing training for health professions students and healthcare workers: a scoping review.
Article in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Artificial intelligence in infection prevention and control education: toward intelligent, real-time clinical learning systems.Frontiers in public health · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundErrors in donning and doffing personal protective equipment (PPE) significantly contribute to self-contamination among healthcare workers and health professions students, potentially leading to occupational exposure. Artificial intelligence (AI) offers a promising approach to enhance PPE training, but no comprehensive review of its applications and effectiveness exists.
methodsThis scoping review followed the Joanna Briggs Institute (JBI) framework and PRISMA-ScR guidelines. Four databases (PubMed, Scopus, Embase, Web of Science), grey literature, and citation searching were searched for studies published between January 2000 and November 2025. Included studies focused on AI-assisted PPE donning and doffing training for healthcare workers or health professions students.
resultsFive studies (published 2022-2025) from China (n = 2) and Australia (n = 3) met the inclusion criteria. Study designs were heterogeneous, including controlled experiment, prospective cohort, clinical cohort validation, pilot simulation study, and pre-post intervention, with sample sizes ranging from a single participant to 3382 individuals. The applied AI technologies primarily involved computer vision and machine learning, integrated into systems for real-time feedback, virtual simulation, and compliance monitoring. Evaluations suggested that AI-assisted training was associated with improved operational accuracy, with some studies reporting an increase to over 98%. One study observed a concurrent decrease in clinical infection rates, though causality cannot be established due to study design limitations.
conclusionAI shows strong potential to enhance PPE training through real-time feedback and personalized skill development. However, the current evidence base is limited to five studies conducted exclusively in China and Australia, which restricts the geographical generalizability of the findings. Future research should explore integrated training curricula, long-term effectiveness, and cost-efficient AI implementations.
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What OpenQuestion holds
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.