ReviewCell metabolism2024
Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management.
Review in Cell metabolism, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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
25 citing papers in PubMed.
- Trial
- Precision medicine in low-income settings and small island developing states.Nature reviews. Endocrinology · 2026Review
- Gene-Air Pollution Interaction in Cardiovascular Disease: Lights and Shadows in a Tangled Risk Factor Network.International journal of molecular sciences · 2026Review
- Personalized prevention for all: changing how we approach the future of prevention.Health affairs scholar · 2026Article
- Revolutionizing Multimorbidity Care: A Narrative Review on Artificial Intelligence Applications.Health science reports · 2026Article
- Automated Report Generation in Ophthalmology: Integrating Artificial Intelligence, Multimodal Imaging, and Clinical Data.Ophthalmology and therapy · 2026Review
- Large language model detects previously undiagnosed heart failure with preserved ejection fraction in patients with metabolic-associated fatty liver disease: A multicenter cohort study.PLOS digital health · 2026Article
- Artificial intelligence-driven metabolomics of the retinal nerve fiber layer to profile risks of mortality and cardiometabolic diseases.Annals of medicine and surgery (2012) · 2026Article
- Asia at the Epicenter of the Global Cardiometabolic Shift.JACC. Asia · 2026Review
- Reimagining cardiac care with AI, LLMs, blockchain, and metaverse.Global cardiology science & practice · 2026Review
- From Exposure to Atherosclerosis: Mechanistic Insights into Phthalate-Driven Ischemic Heart Disease and Prevention Strategies.Life (Basel, Switzerland) · 2026Review
- Risk stratification in diabetic kidney disease: a review of prediction models for methodological advances and clinical application.Journal of translational medicine · 2026Review
- Impact of Insulin Resistance and Preclinical Atherosclerosis Parameters in Long-Term Prediction of Cardiovascular Events: A Seven-Year Prospective Study.Journal of clinical medicine · 2026Article
- Beyond glucose: wearable and implantable biosensors for continuous monitoring of metabolic, hormonal, and inflammatory biomarkers in personalized cardiometabolic care.Frontiers in bioengineering and biotechnology · 2026Review
- Redefining cardiometabolic biomarkers in the big data era: toward a personalized medicine-centered reconstruction of risk prediction models.Frontiers in cardiovascular medicine · 2026Article
- A Paradigm Shift in Congenital Heart Disease: A Scientometric Portrait of the Rise of Computational Intelligence.Pediatric cardiology · 2025Review
- Utilizing multimodal artificial intelligence to advance cardiovascular diseases.Precision clinical medicine · 2025Review
- When time is of the essence: ethical reconsideration of XAI in time-sensitive environments.Journal of medical ethics · 2025Article
- Haematometabolism rewiring in atherosclerotic cardiovascular disease.Nature reviews. Cardiology · 2025Review
- Advancing Cardiovascular, Kidney, and Metabolic Medicine: A Narrative Review of Insights and Innovations for the Future.Diabetes therapy : research, treatment and education of diabetes and related disorders · 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
Abstract
The rise of artificial intelligence (AI) has revolutionized various scientific fields, particularly in medicine, where it has enabled the modeling of complex relationships from massive datasets. Initially, AI algorithms focused on improved interpretation of diagnostic studies such as chest X-rays and electrocardiograms in addition to predicting patient outcomes and future disease onset. However, AI has evolved with the introduction of transformer models, allowing analysis of the diverse, multimodal data sources existing in medicine today. Multimodal AI holds great promise in more accurate disease risk assessment and stratification as well as optimizing the key driving factors in cardiometabolic disease: blood pressure, sleep, stress, glucose control, weight, nutrition, and physical activity. In this article we outline the current state of medical AI in cardiometabolic disease, highlighting the potential of multimodal AI to augment personalized prevention and treatment strategies in cardiometabolic disease.
Indexed as
Identifiers
What 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.