Work

Generative artificial intelligence in diabetes healthcare

Year: 2025

Type: review

Abstract: The rapid advancement of generative artificial intelligence (AI) has been fueled by breakthroughs in large language models and applications across diverse domains, from creative content to scientific discovery. Its strength lies in modeling, simulating, and generating high-fidelity data. In diabetes care, generative AI enables solutions to challenges such as data scarcity, patient variability, and personalization. This article explores key deep generative models, including variational autoencoders, generative adversarial networks, transformers, and diffusion models applied to tabular, time series, image, and text data. These models enable synthetic patient data generation, dataset augmentation, glucose-insulin dynamics simulation, and the development of virtual coaches and digital twins. Despite these advances, challenges persist, including model instability, high data requirements, and output interpretability. This article reviews the current literature and outlines opportunities, limitations, and ethical considerations for the use of generative AI in diabetes healthcare.

Source: iScience

DOI: 10.1016/j.isci.2025.113051

Authors: Josep Vehi, Omer Mujahid, Aleix Beneyto, Ivan Contreras

Institutions: Universitat de Girona, Centro de Investigación Biomédica en Red Diabetes y Enfermedades Metabólicas Asociadas

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,240