ArticleCureus2025
Real-Time Personal Protective Equipment (PPE) Compliance and Clinical Tool Monitoring Using Generative AI: A Novel Approach for Adaptive and Automated Healthcare Surveillance.
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Exploring AI-assisted cameras to assess use of contact precautions.Infection control and hospital epidemiology · 2026Article
- Bounding Boxes in Practice: Design and Early Evaluation of a Video-Based PPE Compliance Monitoring Application for Time-Critical Care: A preliminary evaluation of a video-based explainable interface to improve the usability of AI model outputs.Extended abstracts on Human factors in computing systems. CHI Conference · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHospital-acquired infections (HAIs) remain a critical patient safety concern, affecting one in 31 hospitalized patients daily. Non-compliance with personal protective equipment (PPE) protocols is a preventable driver. Current monitoring methods, such as manual audits and closed-circuit television (CCTV), are limited by delays, inconsistency, and reactivity. Traditional artificial intelligence (AI) systems are rigid and require retraining when protocols change.
objectiveTo construct and evaluate a generative AI-driven compliance monitoring system, built with Google Gemini (Mountain View, CA, USA) on Raspberry Pi (Cambridge, UK) hardware that translates hospital rulebooks or free-text prompts into real-time enforcement logic without retraining.
methodsThe system integrated Gemini, OpenCV (Dover, DE, USA) and Streamlit (San Francisco, CA, USA) to convert natural language rules into executable logic. Performance was tested in 168 mannequin-based trials under varied conditions (skin tones, orientations, and object presence). Outcomes were compared with reference labels using accuracy, recall, specificity, F1 score, and Cohen's Kappa.
resultsThe system achieved 95.8% accuracy, 91.0% recall, 100% specificity, F1 = 0.95, and Cohen's Kappa = 0.92. Performance was consistent across mannequin skin tones and between rulebook-derived and free-text prompts, with no false positives recorded.
conclusionThis generative AI compliance system demonstrated strong accuracy, adaptability, and cost efficiency. Integration into hospital workflows could enable proactive real-time monitoring of evolving safety protocols, improving compliance and reducing costs relative to current methods.
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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.