Ahead of Print

Introduction

The use of artificial intelligence (AI) in clinical practice has been rapidly increasing in popularity, particularly in patient-led care planning, diagnostic and clinical decision-making.^1-3 The use of AI in healthcare is not new, being introduced in a limited capacity in the 1960s and growing in demand, adoption, and efficiency in clinical care since the 2000s.^4-5 AI can be defined as the development of computer systems that manage complex tasks performed by humans through algorithms, which encompass a set of rules implemented through multiple models and programs.^1,3 Applications of AI in healthcare have ranged from time and stress-saving capabilities in the automation of administrative tasks^2,6 to improving the accuracy of diagnostics through image-based analysis⁷ and symptom input into a clinical decision tool.⁵ More specifically, AI has gained traction in fields such as pharmaceutical development⁸, medical imaging analysis⁹, and psychiatric note writing.¹⁰ AI is expected to be integrated into the bedside practice of healthcare professionals to varying degrees and could help address future healthcare challenges.^6.11-12 Therefore, considering the complexities that AI in healthcare introduces, its implementation into wound care needs further research and policy frameworks to guide its safe integration.¹³

The ever-growing market for AI-assisted healthcare has alerted clinical governance to act quickly in response.^13-17 According to the Organisation for Economic Co-operation and Development [OECD], an international policy developing organisation¹⁸, AI-driven health programs are being integrated haphazardly globally, with no clear indication of their level of adoption, skill acquisition requirements, and development of integration.^19-20 To date, the authors have been unable to source any evidence-based resources that guide standardised wound care with AI integration, instead finding a broad range of materials that provide AI-assisted healthcare frameworks and position statements.^13-17 Furthermore, the implications of using AI in healthcare could result in legal and ethical ramifications for healthcare professionals without an accepted understanding stated within the individual faculties and multidisciplinary codes of conduct.^{14, 21-24,} The guidelines regarding AI in healthcare were developed with limited studies evaluating their real-world implementation.^{14, 21-24} As a result, there is a lack of robust evidence on how these guidelines function in clinical practice, particularly concerning their legal and ethical implications for healthcare professionals.^21-24 Researchers highlight this gap in recently published studies, particularly regarding accountability, liability, and the development of professional regulatory guidance for AI in healthcare.^21-24 Research interest in AI frameworks for healthcare has significantly increased, with current studies now concentrating on their practical implementation and the development of standardised approaches to ensure their consistent and effective integration into clinical settings.^25-27 Recent studies on AI adoption in medical, nursing, or allied health education demonstrate that healthcare educators are increasingly integrating AI into students’ learning processes, reflecting a shift toward technologically enhanced education in clinical and biomedical training.^28-30 Therefore, future healthcare professionals will have basic knowledge of and experience with these systems⁷ before integrating them into their clinical practice, potentially, without local governance.

The use of AI-embedded medical devices, tools, and programs has promising applications when integrated into the wound care context for specific wound types or to manage complex wound problems.^4,31-32 Consideration of the potential advantages of a tool, such as an AI mHealth application or a biosensor measuring the pH level of ‘a wound, should be made in the context of a comprehensive, transformative, and lasting change to wound care management.^11,33 Additional examples include the use of AI in aiding the diagnostic classification of cancerous lesions⁹ and its incorporation into monitoring surgical wound flaps using predictive modelling.³⁴ In wound management, AI is utilised for treating difficult to treat wounds, including the YOLOv8 pressure injury image-based wound assessment program³⁵ and wearable smart sensor dressings for diabetic foot wounds, which provide real-time assessment data.³³

AI-assisted wound management has gained positive traction across healthcare settings, with evidence suggesting improvements in clinical efficiency, including enhanced time management, accurate wound assessments, and increased patient engagement.^4,36-39 Healthcare professionals and researchers have also expressed optimism about AI’s potential to reduce costs and streamline workflows in healthcare more broadly.^25,40 Despite these promising developments, a notable gap remains in the literature regarding the barriers and enablers to AI adoption in wound management, particularly from the perspective of diverse healthcare professionals involved in wound management. Existing studies have primarily focused on the experiences of nurses and allied health staff, offering valuable insights into the challenges and opportunities within clinical and aged care environments.^41-42 However, given that AI implementation in wound management is inherently multidisciplinary, the experiences of a broader range of healthcare professionals need to be explored to develop inclusive and effective strategies for its integration.^7,39,43

Healthcare professionals’ perceptions are divided between  optimism and a sense of caution about the potential pitfalls of using AI in their healthcare practice.^12,44-45 Questions arising include: do the advantages of a clinical decision support tool outweigh the potential risks of data privacy breaches, biased outputs, or the potential overreliance on AI and deskilling of healthcare professionals’ in wound management?²⁵ Nevertheless, research has shown that healthcare professionals who use AI as a tool have measurable advantages in their clinical performance and efficiency that exceed those of AI programs working without human input or healthcare professionals working without AI.¹⁴ The potential benefits of integrating AI technology in wound management have been considered^31,36,46-47 particularly in comparison to current practice.^7,48 AI-assisted wound management has demonstrated measurable improvements in clinical efficiency, including enhanced time management, more accurate assessments of wound healing and measurements, increased patient engagement in care, improved identification of wound types, and more precise selection of appropriate wound dressings.^{4,31,38,46-48}

The limitations of AI-assisted wound management are not dissimilar to those in other healthcare fields, with numerous potential solutions identified to address these risks while appreciating the positive benefits.^7,11,36 The risk-benefit argument is commonly used against AI, particularly given its current functionality. There are clear advantages to particular programs, such as Tissue Analytics, which has been shown to improve wound assessment and management during the COVID-19 pandemic.³⁶ Additionally, AI-based pressure injury prediction models in intensive care settings have also shown promise.^37-38 These studies highlight benefits, such as more accurate assessments, personalised care, earlier interventions, and improved prognostic capabilities.^36-38 Conversely, machine learning, a model involving multiple algorithmic methods to find solutions to problems with specific programming⁴⁹, uses sensitive healthcare datasets that may not be generalisable due to potential bias, class imbalances and misrepresentation of a particular setting or population, if the dataset is not externally validated before implementation.^17,25 Concerns arising from inaccuracies in outputs, data security, inaccessible and externally validated models, acceptability, ethical care, unclear technical skill frameworks, and privacy are not comprehensively addressed.^{9-10,25-26,43,50} Healthcare professionals and policy makers’ pessimistic view of AI in healthcare argues that it cannot be safely and comprehensively embedded into clinical practice until the perceived and identified challenges are addressed with practical risk mitigation strategies.^{9,16-17,43-44,50} The World Health Organization (WHO) has emphasised the importance of being hopeful that safeguard measures built in collaboration with stakeholders, guided by governance standards will mitigate potential risks.¹⁶ Examples of local governance frameworks include the New South Wales (NSW) AI Assessment Framework⁵¹ and the Australian Government National AI Assurance Framework⁵², which ensure the safe, ethical, and equitable use of AI.

There is a known gap in evidence-based practice in wound management among healthcare professionals, such as nurses⁵³, doctors, and podiatrists^41-42,54-55 that AI may be able to address. A multidisciplinary example was provided by Hughes et al⁵⁶ who conducted a study into the knowledge of nurses and allied health staff in wound infection, noting that staff surveyed (38%) incorrectly stated that all wounds, despite their assessment findings, require cleansing. This example illustrates a specific knowledge gap that an AI-assisted wound management program, such as a clinical decision tool, could address.³¹ Yet AI-assisted wound management could be beneficial in combating knowledge gaps through accurate risk prediction and wound assessment tools, and provide personalised, holistic treatment aligning with patient care goals.^7,11 Health executives have touted AI as the solution to staff shortages, cost blowouts, and bridging the gap in healthcare professionals’ practices by preventing errors, and delivering efficient, evidence-based care.⁹ Healthcare professionals are agreeable to using AI with expert oversight, provided governance is developed and evaluated in collaboration with actively practising healthcare professionals using AI.^1,7,57

Study rationale

There is limited literature examining healthcare professionals’ views on AI use in wound management.^7,43 A scoping review is warranted to understand this rapidly evolving field, including the barriers and enablers to adopting AI-assisted wound care. A scoping review can act as a precursor to a systematic review, identifying the current gaps in the literature, confirming concepts and terminology, and addressing the wide scope of the research question proposed.

For the safe and effective adoption of AI in wound care, a greater exploration of potential topics, including regulatory compliance, data equity, trust, workflow and assurances of patient safety and clinical validity, is required.^13,52 Research into AI use in wound management has described the development of algorithms and data sets, the trial of AI in multiple healthcare settings²⁴, patient perspectives^36,48, and the efficiency and feasibility of AI programs (mobile applications)^11,36, head-mounted displays (smart glasses)^32,58 and wearable patient devices (smart dressings and bio-sensors) in wound care.^32-33 A scoping review that encompasses the heterogeneity of evidence in wound care, while acknowledging the rapid expansion of AI applications in healthcare, is beneficial.^15-17 This protocol aims to describe the process of conducting a scoping review to explore the available research on healthcare professionals’ experiences of barriers and enablers to AI-assisted wound management.

Objectives

The objective of this scoping review is to explore healthcare professionals’ experiences of barriers and enablers to AI-assisted wound management. In particular, we will focus on the barriers and enablers to AI-assisted wound assessment, diagnosis, and management plans in various care settings, and healthcare professionals’ awareness of clinical practice guidelines and policies related to AI-assisted wound management.

Protocol development

The proposed scoping review will use the six-stage framework by Arksey and O’Malley,⁵⁹ with refinements by Levac et al.⁶⁰ The scoping review will be guided by the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist.⁶¹ The scoping review protocol is registered with the Open Science Framework [OSF].⁶² The research team revised the protocol approach throughout the development, which is listed below.

Methods

Stage 1: Identifying the research question

Research question

• What are the healthcare professionals’ experiences of barriers and enablers to AI-assisted wound management?

Stage 2: Identifying relevant studies

The Campbell Collaboration, Cochrane Collaboration and the International Prospective Register of Systematic Reviews [PROSPERO] registries were searched for similar studies; no similar review has been registered or published to date.

A librarian was consulted for preliminary research advice, and the research team guided the development of the search strategy. A preliminary search using ‘barrier’, ‘enabler’, ‘wound’, ‘healthcare professional, ‘experience’ and ‘artificial intelligence’ as keywords with a combination of truncations, synonyms and Medical Subject Headings (MeSH terms) was conducted. Additional limits were chosen to restrict results to journal articles published within 2010–current, full text and in English to identify relevant and current articles related to the research question. Table 1 summarises the search terms used.

 

Table 1. Summary of search terms used

 

Electronic journal databases to be searched include CINAHL Complete, Embase, Ovid MEDLINE, Scopus, PubMed, Web of Science, and ProQuest. Grey literature sources include Google Scholar (first 10 pages), Google Advanced and Connected Papers with ‘gold set’ articles from the preliminary search. Appendix A includes an example of the preliminary search performed in the CINAHL Complete database, which was conducted in September 2025.

If new keywords are identified, the search strategy will be updated, and additional searches will be conducted to incorporate new results into the scoping review. The database results will be manually reviewed for journal articles that align with the research inquiry and inclusion criteria. The search will be supplemented by manually reviewing the reference lists of ‘gold set’ articles. Co-citation of the ‘gold set’ articles will be performed through Connected Papers, which aims to highlight additional articles for review.

Stage 3: Study selection

The research inquiry and research question were developed using the Population, Interest, and Context (PICo) method.

• Population: healthcare professionals
• Interest: barriers and enablers to AI-assisted wound management
• Context: community, home, acute and primary clinical care settings where wound management is conducted (for example clinical wards, aged care facilities or home residences)

Study designs

The scoping review will include qualitative and mixed-methods studies. Qualitative studies, including grounded theory, phenomenology, qualitative descriptive research using methods such as case studies, observation, focus groups, and individual interviews (structured or semi-structured), will be included in this review, focusing on barriers and enablers to AI-assisted wound management. Mixed-methods studies will incorporate the above designs and surveys with open-ended questions that discuss the experiences and perspectives of healthcare professionals. Feasibility studies will also be included to identify experiences related to the implementation of AI wound programs. Including the majority of relevant study types in the review ensures that a broad scope has been applied to retrieve the available evidence related to the research inquiry. Quantitative studies will not be included as they will not provide research on the experiences of healthcare professionals related to the research question. The inclusion and exclusion criteria are summarised in Table 2.

 

Table 2. Inclusion and exclusion criteria

 

The PRISMA-ScR 2020 flow diagram will display the studies included, excluded, and reviewed.⁶³ Appendix B provides an example of the flow diagram that will be populated with the search findings.

Stage 4: Charting the data

The extracted results will be guided and structured by the JBI Manual for Evidence Synthesis⁶⁴ and JBI interim guidance articles.⁶⁵ Two independent reviewers (SM & AM) will perform a staged screening of the retrieved studies based on the title and abstract search strategy. Three authors (SM, VT & AM) will review any additional articles identified to ascertain suitability according to the inclusion criteria. Two independent reviewers (SM & AM) will then review full-text articles deemed relevant to the research question. If disagreements arise during any review phase between the authors, a third reviewer (VT) will be requested to review the article and determine its eligibility.

The EndNote 20.5 program will be applied as a reference manager. Screening and data extraction will be conducted through the Covidence® program. If duplicate articles are identified, they will be removed in EndNote and Covidence®. The anticipated data charting approach will outline the characteristics to be recorded from articles in the review, and specific details related to AI tools will be extracted.

• Author(s)
• Year of publication
• Country of origin
• Aims
• Study population (such as, nurse, doctor, allied health professionals) and sample size
• Methodology/methods
• Outcome measures/results
• Funding sources
• Key findings that relate to the research question

Additional (if sufficient data is present)

• Type of AI
• Device, tool or program
• Name of the AI product
• Wound care function/purpose
• Accessibility to clinician (cost and availability status)

Theoretical Domains Framework

Data synthesis will be guided by the Theoretical Domains Framework (TDF), which has been successfully applied to describe barriers and enablers associated with implementation-related behaviours of healthcare professionals.^66-67 The TDF has 14 domains that describe individual, environmental, and social factors that influence a particular health care provision-related behaviour.⁶⁸ The TDF was selected for this study because it offers a complex and comprehensive structure for identifying a wide range of behavioural barriers and enablers. Its multidimensional approach is particularly suited to exploring the emerging and varied experiences of healthcare professionals in AI-assisted wound management.⁶⁹

After a critical appraisal, the results of credible and trustworthy qualitative articles will be aligned with the respective TDF domains and constructs for barriers and enablers identified. In conjunction with data charting, Table 3 will detail the identified TDF domains, constructs, and coding of barriers and enablers identified in data extraction.

 

Table 3. TDF domains, constructs and results (barriers and enablers)⁶⁸

 

Stage 5: Collating, summarising and resporing the results

Qualitative articles selected will be analysed for credibility and reliability using the consolidated criteria for reporting qualitative research [COREQ]⁷⁰ or the standards for reporting qualitative research [SRQR].⁷¹ Mixed methods articles will use the Mixed Methods Appraisal Tool [MMAT].⁷²

Stage 6: Consultations – Patient and public involvement

The protocol aims to outline the steps of the scoping review to develop a future understanding of healthcare professionals’ experiences of barriers and enablers to AI-assisted wound management. Patients and healthcare professionals will not be involved in the development or implementation of the proposed scoping review. The results of this review will inform future research in the evolving field of AI healthcare by providing new insights into the implementation of AI in wound management. Furthermore, the first author, a clinical nurse educator, holds full membership with wound-affiliated organisations, providing a platform for disseminating the proposed scoping review through educational formats and peer-reviewed journal publication.

Discussion

Ethics and dissemination

To ensure trustworthiness and data integrity in research are maintained, the authors will consider ethical implications, such as providing a transparent search strategy process, presenting objective results, and aiming to identify and report biases in the studies included in this review. Ethics approval will not be sought for the scoping review because no ethical concerns are anticipated, as the project does not involve any human or animal subjects.

The scoping review outcomes will be disseminated through a peer-reviewed journal publication, and the data will be made available upon reasonable request. Data will be collected from publicly available databases and websites, either via open access or a subscription.

Limitations

This scoping review excludes studies that reflect the perspectives and experiences of patients, families, informal carers and students in nursing, medical, and allied health fields. Future reviews could focus on gaining their unique understanding of individuals’ experiences with using AI in wound care.

Some barriers and enablers to AI-assisted wound management may be underreported, as we plan to exclude articles published in languages other than English that do not have a translation available. Furthermore, articles on ‘clinicians’, ‘health workers’, ‘fuzzy logic’ or ‘agentic AI’ may be unintentionally removed from the search findings due to potential terminology use surrounding healthcare professionals and AI.

Conclusions

While AI presents an exciting opportunity to address the ongoing and ever-present challenges in wound management, the healthcare professional community is cautiously optimistic about its implementation and integration at various levels and formats of clinical practice. This protocol outlines the steps of the planned scoping review of healthcare professionals’ experience of barriers and enablers to AI-assisted wound management, and the findings of the review will provide a summary of these barriers and enablers.

ORCID

Sarah Montalto (Hulbert-Lemmel) 0009-0005-3764-4680
Auxillia Madhuvu 0000-0001-6841-1461
Victoria Team 0000-0001-6615-6874

Acknowledgements

Thank you to the Monash Library team for their assistance in the literature searches.

Conflict of interest

The authors declare no conflicts of interest.

Ethics statement

An ethics statement is not applicable.

Funding

The authors received no funding for this study.

Author contribution

SM is the guarantor. SM produced the first draft of the manuscript. All authors (SM, AM, VT) contributed to the conceptualisation of this review, development of the selection criteria, the risk of bias assessment strategy and data extraction criteria. SM and a librarian collaborated to develop the search strategy with the advice of the research team (VT & AM). SM and AM will conduct the screening, and VT will serve as the third reviewer in the event of disagreements. All authors read, provided feedback and approved the final version of the manuscript.

Author(s)

Sarah Montalto¹, Auxilia Madhuvu¹, Victoria Team¹*
¹Monash University, Clayton Campus, 35 Rainforest Walk, Clayton VIC 3800, Australia

*Corresponding author email victoria.team@monash.edu

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Appendices

Appendix A. CINAHL complete search strategy

 

Appendix B. PRISMA2020 flow diagram for new systematic reviews, which includes searches of databases, registers and other sources⁶³