Artificial intelligence (AI) in healthcare has begun offering novel solutions for disease prediction, monitoring, and management. A team of researchers conducted a literature search across the Web of Science, IEEE Xplore, and PubMed databases analysis to identify how AI, particularly machine learning, can be used in diabetic foot care and research.
The analysis included 25 peer-reviewed journal articles published between 2020 and 2024 that focused on the intersection of machine learning and diabetic foot management and identified key trends, focus areas, and methodological approaches in the application of machine learning to diabetic foot research.
The researchers found a steady increase in publications related to machine learning in diabetic foot research over the past five years. Among the included studies, image analysis was the most prevalent theme (12 articles), dominated by thermal imaging applications (ten articles). General clinical imaging was addressed in two articles. Seven studies focused on structured clinical data analysis, while six explored approaches such as smart insoles with integrated sensors for real-time foot monitoring.
The study concluded that the integration of machine learning into diabetic foot research is rapidly evolving, characterized by growing diversity in data and analytical techniques.
The open-access study, “Machine learning for diabetic foot care: Accuracy trends and emerging directions in healthcare AI,” was published in Frontiers in Public Health.
