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Application of artificial intelligence and new technologies in the diagnosis of diabetic foot
1
Students’ Scientific Club, Dr W. Biegański Collegium Medicum, Jan Długosz University in Czestochowa, Poland
2
Dr W. Biegański Collegium Medicum, Jan Długosz University in Czestochowa, Poland
3
Students’ Scientific Club, Department of Pathomorphology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland
4
Department of Pathomorphology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland
Corresponding author
Karolina Kaleta
Koło Naukowe The Science Force, Collegium Medicum, Uniwersytet Jana Długosza w Częstochowie, ul. Armii Krajowej 13/15, 42-200 Częstochowa, tel. +48 34 888 02 68
Koło Naukowe The Science Force, Collegium Medicum, Uniwersytet Jana Długosza w Częstochowie, ul. Armii Krajowej 13/15, 42-200 Częstochowa, tel. +48 34 888 02 68
KEYWORDS
diabetic foot syndromediabetic foot ulcerartificial intelligencedeep learningexplainable AImedical image analysiswearable sensorsthermographyclinical decision supportdigital health
TOPICS
ABSTRACT
Diabetic foot syndrome (DFS), particularly diabetic foot ulcers (DFU), represents a severe complication of diabetes leading to infection and amputation. Early detection is challenging due to complex pathophysiology and conventional diagnostic limitations. Advances in artificial intelligence (AI), deep learning, and wearable technologies offer new opportunities for improved diagnosis and monitoring. The aim of this review is to analyse current applications of AI, deep learning models, and sensor-based technologies in the diagnosis, classification, and prevention of DFS. A narrative review was conducted using PubMed, ScienceDirect, and Google Scholar (2020–2025). Studies employing deep learning for DFU detection and wearable technologies for monitoring were evaluated for clinical applicability. Deep learning architectures, including convolutional neural networks (CNNs), hybrid models, and transformers, achieved high diagnostic performance, frequently exceeding 99% accuracy in DFU classification. Models integrating segmentation enabled precise lesion localization. Explainable AI (XAI) approaches improved clinical trust, while thermographic imaging showed promise for non-invasive detection. Wearable technologies, such as smart socks and pressure-sensing insoles, enabled continuous physiological monitoring, supporting personalized prevention strategies. AI and emerging technologies significantly enhance diagnostic capabilities in DFS. Deep learning models demonstrate excellent performance, while sensor-based systems extend monitoring beyond clinical settings. However, widespread implementation requires further prospective validation and standardized protocols. AI solutions should be viewed as decision-support tools that complement clinical expertise.
FUNDING
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
CONFLICT OF INTEREST
The authors declare no conflict of interest regarding the publication of this manuscript. None of the commercial systems mentioned have provided financial support, incentives, or funding for this review.
ADDITIONAL INFORMATION
As this study is a literature synthesis and did not involve direct human participants or animals, institutional review board approval was not required. However, the authors emphasize that clinical adoption of the discussed AI technologies must adhere strictly to patient data privacy regulations (e.g., GDPR) and established bioethical standards.
Use of AI tools statement: ChatGPT was used solely for the purpose of integrating and styling the content. The entire research process, including the literature review, analysis of scientific articles, and selection of key information regarding diabetic foot diagnostics, was conducted independently by the authors.
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