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The review maps barriers and facilitators faced by healthcare teams in integrating AI-based wound management technologies.

June 10, 2026Journal of Multidisciplinary Healthcare0 citationsOpen Access

Artificial Intelligence Integration in Multidisciplinary Wound Management: A Scoping Review of Barriers and Facilitators in Clinical Workflows

FSFaiza Sa’banPadjadjaran University CPChandra PurbaPadjadjaran University URUrip RahayuPadjadjaran University

Key Points

The review maps barriers and facilitators faced by healthcare teams in integrating AI-based wound management technologies.
Systematic literature search performed in PubMed, Scopus, and ScienceDirect.
Included empirical studies published between 2021 and 2026 focused on AI in clinical workflows.
Followed Arksey and O’Malley framework and PRISMA-ScR guidelines.
Nine studies met eligibility criteria.
Facilitators included improved documentation efficiency and adherence to evidence-based guidelines.
Barriers included increased workload during adoption, limited interoperability with electronic health records, and concerns over automation bias.

Abstract

Background: Chronic wound management is a complex global health challenge that requires coordinated multidisciplinary care. Artificial intelligence (AI) has the potential to improve wound assessment, documentation, and clinical decision support. However, its successful implementation depends not only on algorithmic accuracy but also on its alignment with existing sociotechnical systems and clinical workflows. Objective: This scoping review aimed to map the operational barriers and facilitators encountered by interprofessional healthcare teams when integrating AI-based wound management technologies into clinical practice. Methods: Guided by the Arksey and O’Malley framework and the PRISMA-ScR guidelines, a systematic literature search was conducted in PubMed, Scopus, and ScienceDirect. Empirical studies published between 2021 and 2026 were included if they examined AI-based wound management technologies in relation to clinical workflows, workload, documentation, or implementation outcomes. Results: Nine primary studies met the eligibility criteria. The thematic synthesis identified several workflow-related facilitators, including improved documentation efficiency, greater adherence to evidence-based guidelines, enhanced diagnostic objectivity, and support for preventive care. Key barriers included increased cognitive and administrative workload during early adoption, limited interoperability with primary electronic health records, risk of automation bias, and concerns that AI may weaken relational and sensory-based aspects of clinical care. Conclusion: AI integration in multidisciplinary wound care may support workflow efficiency and clinical decision-making, but its implementation remains a sociotechnical challenge. Sustainable adoption requires native EHR interoperability, careful mitigation of digital fatigue, and human-in-the-loop design to ensure that AI enhances clinical practice without compromising professional judgment and humanistic patient care. Keywords: artificial intelligence, clinical workflows, wound care, barriers and facilitators, scoping review

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