Application of Artificial Intelligence Methodologies to Chronic Wound Care and Management: A Scoping Review

Mai Dabas, Dafna Schwartz, Dimitri Beeckman, Amit Gefen*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review


Significance: As the number of hard-to-heal wound cases rises with the aging of the population and the spread of chronic diseases, health care professionals struggle to provide safe and effective care to all their patients simultaneously. This study aimed at providing an in-depth overview of the relevant methodologies of artificial intelligence (AI) and their potential implementation to support these growing needs of wound care and management. Recent Advances: MEDLINE, Compendex, Scopus, Web of Science, and IEEE databases were all searched for new AI methods or novel uses of existing AI methods for the diagnosis or management of hard-to-heal wounds. We only included English peer-reviewed original articles, conference proceedings, published patent applications, or granted patents (not older than 2010) where the performance of the utilized AI algorithms was reported. Based on these criteria, a total of 75 studies were eligible for inclusion. These varied by the type of the utilized AI methodology, the wound type, the medical record/database configuration, and the research goal. Critical Issues: AI methodologies appear to have a strong positive impact and prospects in the wound care and management arena. Another important development that emerged from the findings is AI-based remote consultation systems utilizing smartphones and tablets for data collection and connectivity. Future Directions: The implementation of machine-learning algorithms in the diagnosis and managements of hard-to-heal wounds is a promising approach for improving the wound care delivered to hospitalized patients, while allowing health care professionals to manage their working time more efficiently.

Original languageEnglish
Pages (from-to)205-240
Number of pages36
JournalAdvances in Wound Care
Issue number4
StatePublished - 1 Apr 2023


  • bioengineering
  • chronic wounds
  • convolutional neural networks
  • deep learning
  • machine learning


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