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Advancing Diabetic Foot Ulcer Care: AI and Generative AI Approaches for Classification, Prediction, Segmentation, and

Suhaylah Alkhalefah¹, Isra AlTuraiki¹, Najwa Altwaijry¹

¹Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.

Healthcare (Basel, Switzerland)

|March 28, 2025

Summary

This summary is machine-generated.

Artificial intelligence (AI) and generative AI are improving diabetic foot ulcer (DFU) management through enhanced classification, prediction, and detection. AI-powered smartphone apps offer cost-effective remote monitoring and diagnosis for DFUs.

Keywords:

artificial intelligence diabetic foot ulcers generative AI machine learning mobile applications

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Area of Science:

Medical Imaging
Artificial Intelligence
Computational Biology

Background:

Diabetic foot ulcers (DFUs) pose a significant challenge in diabetes management, leading to severe complications and increased healthcare costs.
Traditional DFU management relies on resource-intensive manual assessments and diagnostic tools.
Artificial intelligence (AI) and generative AI present innovative solutions for DFU care.

Purpose of the Study:

To systematically review the applications of AI and generative AI in diabetic foot ulcer classification, prediction, segmentation, and detection.
To explore generative AI's role in addressing data scarcity for DFU management.
To assess the potential of AI-driven smartphone applications for remote DFU monitoring and diagnosis.

Main Methods:

A systematic literature review adhering to PRISMA guidelines.
Identification of relevant studies published between 2020 and 2025 from major scientific databases (PubMed, IEEE Xplore, Scopus, Web of Science).
Focused review on AI and generative AI applications specifically for DFUs, excluding non-DFU medical imaging research.

Main Results:

AI models have demonstrated significant improvements in DFU classification accuracy, early detection rates, and predictive modeling.
Generative AI techniques, including GANs and diffusion models, show promise in overcoming dataset limitations by creating synthetic DFU images.
AI-enabled smartphone applications offer a cost-effective approach to DFU monitoring, potentially enhancing diagnostic capabilities.

Conclusions:

AI and generative AI are revolutionizing diabetic foot ulcer management by improving diagnostic accuracy and predictive power.
Future research should emphasize explainable AI frameworks and the use of diverse datasets to promote wider clinical adoption of AI in DFU care.