Machine Learning Applications for Venous Ulcer Assessment and Wound Care: A Review

Over recent years, venous ulcer wound care has experienced significant advancements through the application of machine learning (ML) models. The aim of the present study is a systematic, comprehensive analysis of prior research studies in this field covering the period between 2001 and August 2025. By searching multiple academic databases, including the Web of Science, Scopus, and PubMed, using relevant keywords and different queries, and screening reference lists of previously published manuscripts and review papers with a focus on the application of artificial intelligence in dermatology and medicine, an initial set of potential studies for review was obtained. To ensure the scope and relevance of the review, several inclusion and exclusion criteria were used to derive the final set of relevant research studies upon which a database for research data management was created. As a result, a total of 79 relevant research studies were comprehensively analysed, upon which detailed meta-analysis and analysis of application areas of ML models within venous ulcer wound care were conducted. Afterwards, a summary of benefits for medical systems and patients was given along with a general discussion regarding ML model limitations, trends, and opportunities, as well as research studies' limitations and possible future research directions. The presented analyses may be valuable for researchers interested in applying ML models not only to venous ulcer wound care but also to other types of chronic wound care.