SynthesisHormones (Athens, Greece)2025
Artificial intelligence in diabetes management: transformative potential, challenges, and opportunities in healthcare.
Synthesis in Hormones (Athens, Greece), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Nanostructured electrode materials and flexible-substrate engineering for wearable multi-analyte biosensors in diabetes monitoring and personalized care: a comprehensive review.Journal of materials science. Materials in medicine · 2026Review
- Integration of artificial intelligence and wearable technology in the management of diabetes and prediabetes.NPJ digital medicine · 2025Article
- Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review.Annals of translational medicine · 2025Review
- Personalized Nutrition in the Era of Digital Health: A New Frontier for Managing Diabetes and Obesity.Food science & nutrition · 2025Review
- The Effect of Diabetes Mellitus on Mandibular Growth.Turkish journal of orthodontics · 2025Article
- GLP-1 agonists on a roll: from obesity to psoriasis - an elixir to watch!Hormones (Athens, Greece) · 2025Article
- AI-driven predictive modelling of orthodontic relapse using retainer compliance and patient factors.Bioinformation · 2025Article
- Artificial intelligence in diabetes care: from predictive analytics to generative AI and implementation challenges.Frontiers in endocrinology · 2025Review
- Artificial intelligence in chronic disease self-management: current applications and future directions.Frontiers in public health · 2025Review
- Harnessing gut-derived bioactives and AI diagnostics for the next generation of type 2 diabetes solutions.Frontiers in endocrinology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDiabetes, a chronic metabolic disorder characterized by ineffective blood sugar regulation, affects millions of people worldwide, with its prevalence projected to more than double in the next 30 years. Diabetes-related complications are severe and sometimes life-threatening, including cardiovascular disease, kidney failure, and blindness, this posing a significant challenge, especially in low- and middle-income countries. This study explored the integration of artificial intelligence (AI) into diabetes management, emphasizing its transformative potential in healthcare.
objectivesTo evaluate the role of AI in enhancing diabetes management and to identify the challenges and opportunities associated with its implementation.
methodsA systematic review following the PRISMA guidelines was conducted by analyzing the literature published from January 2020 to May 2024. This review focused on the application of AI in diabetes diagnosis, personalization of treatment, and predictive analytics.
resultsThe ability of AI to analyze large datasets and identify complex patterns shows promise in improving diabetes management. AI-assisted diagnostic tools enhance diagnostic accuracy, enable early detection, and support personalized treatment plans, thereby reducing human error. AI has also facilitated research breakthroughs in genomics and drug discovery. Furthermore, AI-powered predictive analytics enhances clinical decision-making and supports precision medicine. Despite these advancements, challenges remain in such issues as data quality, technical infrastructure, and ethical considerations, emphasizing the need for responsible AI development that focuses on patient privacy and transparency.
conclusionsAI has significant potential to revolutionize diabetes management and healthcare delivery. Combining AI's analytical processes with clinical expertise can substantially improve the quality of care. Addressing data, technology, and ethical challenges is crucial for fully harnessing AI's potential, thereby enhancing patient well-being and healthcare outcomes.
Indexed as
Identifiers
40116992What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.