Integrating Predictive Analytics into Diabetic Wound Care: A Path Toward Personalized Medicine. A Narrative Review
Year: 2025
Type: review
Abstract: ABSTRACT Diabetic foot ulcers (DFUs) are a severe and costly complication of diabetes, often leading to infections, amputations, and diminished quality of life. Traditional wound care remains reactive, relying on subjective assessments and lacking predictive capabilities. This narrative review explores the integration of predictive analytics—powered by machine learning (ML) and artificial intelligence (AI)—into diabetic wound care as a pathway toward personalized medicine. We synthesized literature from 2014 to 2024, focusing on ML applications, predictive models, and their integration with electronic health records (EHRs), wearables, and imaging technologies. Key findings reveal that models such as logistic regression (interpretable for binary outcomes), random forests (robust for structured data), convolutional neural networks (superior for image analysis), and long short-term memory networks (effective for temporal data) enable risk stratification, healing trajectory prediction, and personalized interventions. Integration with wearables and EHRs facilitates real-time monitoring and early detection of complications, while smartphone-based AI tools enhance remote care. However, challenges persist, including data interoperability, clinician trust in “black-box” models, ethical concerns around bias, and infrastructural barriers in low-resource settings. The review underscores the potential of predictive analytics to transition diabetic wound management from reactive to proactive, precision-based care, reducing hospitalizations and amputations. Future directions emphasize federated learning for privacy-preserving collaboration, genomic integration for tailored therapies, and addressing gaps in multimodal datasets and external validation. Successful implementation requires interdisciplinary collaboration, standardized data practices, and ethical frameworks to ensure equitable, transparent, and clinically validated AI solutions. This synthesis highlights the transformative promise of predictive analytics in reshaping diabetic wound care while outlining critical steps for its ethical and effective adoption.
Author: Lufulwabo, Aime
Language: English
Subfield: Endocrinology, Diabetes and Metabolism
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W6893163794
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