Journal Article Ahead of print
PubMed: 42864796 DOI: 10.2174/0115733998484763260928100503
Harnessing Artificial Intelligence-driven Approaches in the Diagnosis and Management of Diabetes and Diabetes-Associated Complications.
Curr Diabetes Rev
Authors: Yachana Mishra, Rajeev Kanday, Simran Kauts, Sheetu Wadhwa, Aditi Chattaraj, Alaa A A Aljabali, Altijana Hromić-Jahjefendić, Vladimir N Uversky, Vijay Mishra
Affiliations
- School of Bioengineering and Biosciences, Lovely Professional University, Phagwara-144411 (Punjab), India.
- School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, 144411, India.
- Drug Discovery and Development Division, Patanjali Research Foundation, Haridwar, Uttarakhand, 249405, India.
- School of Pharmaceutical Sciences, Lovely Professional University, Phagwara, 144411 (Punjab), India.
- Faculty of Pharmacy, Department of Pharmaceutics and Pharmaceutical Technology, Yarmouk University, Irbid, Jordan.
- Department of Genetics and Bioengineering, Faculty of Engineering and Natural Sciences, International University of Sarajevo, Hrasnicka cesta 15, 71000, Sarajevo, Bosnia and Herzegovina.
- Department of Molecular Medicine and USF Health Byrd Alzheimer's Research Institute, Morsani College of Medicine, University of South Florida, Tampa, FL33612, USA.
Abstract
Diabetes mellitus is a global health issue with escalating metabolic dysfunction, clinical heterogeneity, and rising microvascular and macrovascular consequences. Traditional diabetes diagnosis, risk assessment, treatment selection, and complication surveillance rely on periodic clinical measures and population-level risk models, which may not fully capture dynamic, individualized disease pathways. Precision diabetes treatment is being transformed by Artificial intelligence (AI), including machine learning (ML), deep learning (DL), and multimodal data analytics. This review critically examines AI-driven approaches to diabetes management, including early disease prediction, diagnostic support, glycemic monitoring, treatment-response prediction, and the prevention and detection of diabetes-associated complications. The US Food and Drug Administration (USFDA) has sanctioned multiple AI/ML-based medical devices for automated retinal screening, clinical diagnostic assistance, and patient self-management applications. For clinically meaningful patterns and disease progression prediction, AI-enabled analysis of continuous glucose monitoring data, retinal images, electronic health records, wearable device signals, laboratory parameters, and multi-omics datasets is prioritized. The use of AI for detecting diabetic retinopathy, kidney disease, cardiovascular disease, neuropathy, and foot disorders is also highlighted.Data quality, algorithmic bias, interpretability, privacy, cybersecurity, model drift, regulatory monitoring, clinical validation, and equitable access prevent widespread application despite promising results. This study offers a thorough and critical assessment of the developing roles of AI, specifically ML and DL, in the prediction, diagnosis, and management of diabetes and its related complications. Real-world validation, standardized evaluation frameworks, trustworthy AI, and effective human- AI collaboration will drive advancement. AI could transform diabetes management into predictive, tailored, continuous, and complication-preventive care.
MeSH
Diabetes Mellitus, Intelligence, Insulin, Cardiovascular Diseases, Multiple Chronic Conditions, Machine Learning, Deep Learning, United States Food And Drug Administration, Diagnosis, Therapeutics, Learning, Artificial Intelligence, Retinaldehyde, Diabetic Retinopathy