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Background <p>Errors 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.</p> Methods <p>This 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.</p> Results <p>Five studies (published 2022–2025) from China (<i>n</i> = 2) and Australia (<i>n</i> = 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.</p> Conclusion <p>AI 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.</p>

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Artificial intelligence-assisted personal protective equipment donning and doffing training for health professions students and healthcare workers: a scoping review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月23日

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  • Yu Lv,
  • Minhong Cai,
  • Qian Xiang,
  • Pingping Wang

摘要

Background

Errors 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.

Methods

This 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.

Results

Five 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.

Conclusion

AI 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.