ReviewInternational journal of obesity (2005)2026
Artificial intelligence in cardio-kidney-metabolic care: Transforming integrated disease management through data-informed innovation.
Review in International journal of obesity (2005), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
Artificial intelligence (AI) is rapidly transforming the landscape of chronic medical conditions, such as cardio-kidney-metabolic (CKM) issues linked to type 2 diabetes and obesity. It creates new opportunities to shift from reactive to proactive, data-driven care. Recent advances include predictive algorithms for hypoglycemia and hyperglycemia, decision-support tools for insulin titration, and generative and agentic AI applications that can enhance patient engagement, streamline clinical workflows, and provide personalized education. For individuals with chronic conditions, AI-powered technologies offer hope in reducing disease burden, supporting self-management, and improving quality of life. For clinicians, AI offers opportunities to analyze and interpret large amounts of glucose, medication, and behavioral data, thus supporting personalized care and freeing more time to focus on psychosocial and lifestyle factors. Despite these benefits, challenges remain, such as ensuring equitable access, integrating AI into primary care, building trust among clinicians and patients, and addressing ethical issues related to data use. This review will synthesize current evidence on AI's impact on diabetes and CKM care and education, highlight opportunities for interdisciplinary teams to utilize AI tools, and outline future directions for research and clinical practice. By examining AI's potential and limitations, this article aims to equip clinicians with the knowledge needed to adopt AI-enabled approaches to better manage chronic diseases.
Indexed as
Identifiers
42225950What Socratic 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 Socratic graph.