Generative artificial intelligence in diabetes healthcare.

Area of Science:

• Artificial Intelligence
• Biomedical Informatics
• Diabetes Technology

Background:

• Generative artificial intelligence (AI) models, particularly large language models, have advanced rapidly, impacting various fields.
• Generative AI excels at modeling, simulating, and generating high-fidelity data, addressing challenges like data scarcity and patient variability.
• Applications in diabetes care are emerging, promising personalized solutions and improved patient outcomes.

Purpose of the Study:

• To explore the application of deep generative models in diabetes healthcare.
• To review key generative AI models and their use in different data types relevant to diabetes.
• To outline the opportunities, limitations, and ethical considerations of generative AI in this domain.

Main Methods:

• Review of current literature on deep generative models.
• Exploration of models including variational autoencoders, generative adversarial networks, transformers, and diffusion models.
• Application analysis across tabular, time series, image, and text data in diabetes care.

Main Results:

• Generative AI models facilitate synthetic patient data generation and dataset augmentation.
• These models enable simulation of glucose-insulin dynamics and development of virtual coaches and digital twins.
• Key models discussed include VAEs, GANs, transformers, and diffusion models for diverse data types.

Conclusions:

• Generative AI presents significant opportunities for advancing diabetes care through data synthesis and simulation.
• Challenges such as model instability, data requirements, and interpretability need to be addressed.
• Ethical considerations are paramount for the responsible implementation of generative AI in diabetes healthcare.