Generative artificial intelligence in diabetes healthcare
Open accessAbstract
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.
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LLMs can comprehend unstructured text such as physician notes, which often contain critical insights about a patient's condition not captured in structured data fields.
“One of the key advantages of LLMs is their ability to comprehend unstructured text, such as physician notes, which often contain critical insights about a patient’s condition that may not be captured in structured data fields.”
From Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review (2025) · cites this paper as [68]
The Introduction describes LLMs as capable of natural-language understanding, and the opening section lists physician notes among clinical data sources. The copy does not say that LLMs extract information from those notes or that the notes contain critical insights absent from structured data fields. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThese models can analyze retinal images in greater detail than human specialists, identifying patterns that precede visible symptoms of retinopathy.
“These models can analyze retinal images in greater detail than human specialists, identifying patterns that precede the visible symptoms of retinopathy (68).”
From Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review (2025) · cites this paper as [68]
The “Density estimation using generative artificial intelligence” section (page unavailable) describes models extracting retinal-image features, detecting anomalies, and aiding diabetic-retinopathy diagnosis. It does not say they analyze images in greater detail than human specialists or identify patterns before visible symptoms appear. (LLM, conf=0.86)
Experimental — accuracy not yet measuredGenerative AI models, particularly those using deep learning algorithms, have significantly enhanced the ability to detect diabetic retinopathy at much earlier stages.
“A study mentioned that generative AI models, particularly those using deep learning algorithms, have significantly enhanced the ability to detect diabetic retinopathy at much earlier stages.”
From Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review (2025) · cites this paper as [68]
The “Density estimation using generative artificial intelligence” section describes GANs used for diabetic retinopathy diagnosis from images and for fundus-image synthesis supporting grading and lesion segmentation. It does not say these models significantly improved detection or enabled detection at much earlier stages. (LLM, conf=0.86)
Experimental — accuracy not yet measuredGenerative AI and LLMs are emerging as critical tools in revolutionizing diabetes care.
“Generative AI and LLMs are emerging as critical tools in revolutionizing diabetes care (68).”
From Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review (2025) · cites this paper as [68]
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Research works printed but not linked: 2
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- Authors
- Vehi, J., Mujahid, O., Beneyto, A., & Contreras, I.
- Affiliations
- Universitat de Girona (ES)
- Venue
- iScience
- Publisher
- Elsevier BV
- Published
- 2025-08
- Volume
- 28
- Issue
- 8
- Pages
- 113051
- ISSN
- 2589-0042 (ISSN-L)
- PMC
- PMC12284359
- OpenAlex
- W4412027081
- Cited by
- 12
- References
- 63
- Citations by year
- 2026: 10 · 2025: 2
- FWCI
- 0.71
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- iScience (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- España Ministerio de Ciencia Innovación y Universidades (PID2023-149837OB-I00)
- Generalitat de Catalunya (2017SGR1551, 2024-PROD-00051)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- generative AI, diabetes management, data augmentation, virtual coach, digital twin, variational autoencoder, generative adversarial networks, diffusion models, transformer models, deep generative models, ethical considerations, interpretability
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