Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review.

Osonuga, Ayokunle; Olawade, David; Gore, Manisha; Rotifa, Oluwayomi B; Egbon, Eghosasere; Chinwah, Viviane; Boussios, Stergios

Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review.

Osonuga, Ayokunle, Olawade, David ORCID: https://orcid.org/0000-0003-0188-9836, Gore, Manisha, Rotifa, Oluwayomi B, Egbon, Eghosasere, Chinwah, Viviane and Boussios, Stergios ORCID: https://orcid.org/0000-0002-2512-6131 (2025) Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review. Annals of translational medicine, 13 (5). p. 59.

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Abstract

Background and objectiveDiabetes mellitus (DM), particularly type 2 diabetes (T2D), represents a significant global health crisis, often complicated by severe and progressive conditions such as retinopathy, neuropathy, and cardiovascular disease. Traditional diagnostic approaches frequently detect these complications at advanced stages, limiting the opportunity for early, effective intervention. This review aims to examine how recent advancements in generative artificial intelligence (AI), particularly large language models (LLMs), can transform diabetes management by enabling earlier detection and more personalized interventions.MethodsA narrative review was conducted to evaluate the current literature on the application of generative AI and LLMs in diabetes care. The review focused on how these technologies analyse multi-dimensional datasets, including medical imaging, electronic health records (EHRs), genetic profiles, and lifestyle factors, and how they process both structured and unstructured data to enhance predictive analytics and risk stratification for diabetes complications.Key content and findingsGenerative AI models have demonstrated significant promise in detecting hidden trends and early risk factors for complications such as diabetic retinopathy and neuropathy, often before clinical symptoms manifest. LLMs enhance predictive performance by synthesising unstructured data sources, such as physician notes and patient-reported outcomes, with clinical datasets. Despite limitations concerning data quality, model transparency, and ethical concerns surrounding data privacy, these technologies offer powerful tools for proactive disease monitoring and personalized care.ConclusionsGenerative AI and LLMs are poised to redefine diabetes management by enabling earlier detection of complications and personalised treatment strategies. Their integration into clinical decision support systems (CDSS) and precision medicine frameworks may reduce the global burden of diabetes, improve patient outcomes, and shift care from reactive to preventative.