Assessing the Efficacy of AI Models in Medical Imaging Analysis for the Early Identification of Diabetic Foot Ulcers and Gangrene: A Review

Theodor Bilharz Research Institute

Bibliographic Information

Authors: Fahmy A.M.

Journal: Premier Journal of Science

Publisher: Premier Science

Publication Date: 30 July 2025

Volume / Issue: Volume 12

Article No.: 100093

ISSN: 30499011

DOI: 10.70389/PJS.100093

Scopus: View on Scopus

Document Type: Review

Access: All Open Access; Hybrid Gold Open Access

Authors and Affiliations

Fahmy A.M., Theodor Bilharz Research Institute, Giza, Egypt

Abstract

Artificial intelligence (AI) possesses the ability to transform diabetic foot care through enhancing the early identification, categorization, and management of diabetic foot ulcers and gangrene. Incorporation of multimodal sensors, privacy-preserving federated learning, and hybrid convolutional neural network– support vector machine frameworks are some of the AIdriven approaches synthesized in this narrative review. Improving diagnostic accuracy, real-time assessment of risks, and potentially scalable, cost-effective implementation within both urban and rural areas are all included in the clinical potential of AI technology, which the literature review highlighted. Still, there are numerous obstacles to overcome—including a lack of data, inconsistent annotations, outdated technology, biased algorithms—and the need for algorithms that are both explainable and ethically acceptable. In order to cross the gap between breakthrough technology and real-world clinical significance, collaborative attempts that involve standard protocols, compliance with regulations, health care provider commitment, and multidisciplinary teamwork are needed. By addressing these issues and promoting ethical, inclusive, and transparent AI applications, the field has the opportunity to advance in the direction of fair and reliable diabetic foot care, subsequently reducing morbidity and improving patient outcomes on a global basis. © 2025, Premier Science. All rights reserved.

Keywords

Convolutional neural networks; Diabetic foot ulcers; Explainable AI; Federated learning; Gangrene detection

Citation Information

Scopus Citations: 2

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