ArticleNature communications2024
AI-enhanced integration of genetic and medical imaging data for risk assessment of Type 2 diabetes.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in diabetes management: transformative potential, challenges, and opportunities in healthcare.Hormones (Athens, Greece) · 2025Pooled it
- Exploring the genomic and transcriptomic profiles of glycemic traits and drug repurposing.Journal of biomedical science · 2025Pooled it
- Shared mechanisms of musculoskeletal dysfunction in type 2 diabetes mellitus: Insights into future therapeutic directions (Review).International journal of molecular medicine · 2026Review
- Application of AI and digital health tools in public health management of T2DM: from mechanism prediction to personalized treatment.Frontiers in public health · 2026Review
- Artificial Intelligence in Diabetes Care: Applications, Challenges, and Opportunities Ahead.Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists · 2025Review
- Artificial Intelligence in Personalized Medicine for Diabetes Mellitus: A Narrative Review.Cureus · 2025Review
- Pathway insights and predictive modeling for type 2 diabetes using polygenic risk scores.Scientific reports · 2025Article
- AI-driven preclinical disease risk assessment using imaging in UK biobank.NPJ digital medicine · 2025Article
- Artificial intelligence-based diabetes risk prediction from longitudinal DXA bone measurements.Scientific reports · 2025Article
- The future of multimodal artificial intelligence models for integrating imaging and clinical metadata: a narrative reviewDiagnostic and interventional radiology (Ankara, Turkey) · 2025Review
- New discoveries in therapeutic targets and drug development pathways for type 2 diabetes mellitus under the guidance of precision medicine.European journal of medical research · 2025Review
- Multifactorial Risk Stratification in Patients with Heart Failure, Chronic Kidney Disease, and Atrial Fibrillation: A Comprehensive Analysis.Life (Basel, Switzerland) · 2025Article
- AI-driven high-throughput droplet screening of cell-free gene expression.Nature communications · 2025Article
- DNA sequence classification for diabetes mellitus using NuSVC and XGBoost: A comparative.PloS one · 2025Article
- Causal Characteristics of Immune Cells Associated with Aortic Dissection: A Mendelian Randomisation Analysis.European cardiology · 2025Article
- Targeting macrophages and ion homeostasis in T2D: new genes and therapeutic pathways identified.Frontiers in immunology · 2025Article
- Na+/K+-ATPase: a multifunctional target in type 2 diabetes and pancreatic islets.Frontiers in immunology · 2025Review
- Federated multimodal AI for precision-equitable diabetes care.Frontiers in digital health · 2025Review
- Gender-specific genetic influence of rs1111875 on diabetes risk: Insights from the Taiwan biobank study.Journal of diabetes investigation · 2025Article
- Artificial intelligence in diabetes care: from predictive analytics to generative AI and implementation challenges.Frontiers in endocrinology · 2025Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Type 2 diabetes (T2D) presents a formidable global health challenge, highlighted by its escalating prevalence, underscoring the critical need for precision health strategies and early detection initiatives. Leveraging artificial intelligence, particularly eXtreme Gradient Boosting (XGBoost), we devise robust risk assessment models for T2D. Drawing upon comprehensive genetic and medical imaging datasets from 68,911 individuals in the Taiwan Biobank, our models integrate Polygenic Risk Scores (PRS), Multi-image Risk Scores (MRS), and demographic variables, such as age, sex, and T2D family history. Here, we show that our model achieves an Area Under the Receiver Operating Curve (AUC) of 0.94, effectively identifying high-risk T2D subgroups. A streamlined model featuring eight key variables also maintains a high AUC of 0.939. This high accuracy for T2D risk assessment promises to catalyze early detection and preventive strategies. Moreover, we introduce an accessible online risk assessment tool for T2D, facilitating broader applicability and dissemination of our findings.
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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.