ArticleBMJ open2025
Cross-sectional design and protocol for Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READI).
Article in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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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
12 citing papers in PubMed.
- Characterising Continuous Glucose Monitoring Metrics Across Normoglycaemia, Prediabetes and Type 2 Diabetes.Diabetes, obesity & metabolism · 2026Article
- Deep Learning-Derived Retinal Age Detects Cognitive Impairment.Ophthalmology science · 2026Article
- Deep learning strategies for estimating retinal thickness from fundus images: a comparative study with multi-device data.Scientific reports · 2026Article
- Factors Associated with Machine Learning-Based Predictions of Retinal Aging Using Teleretinal Screening Images from Patients with Diabetes.Ophthalmology science · 2026Article
- Association of diabetes severity with cognitive function in US adults: a cross-sectional analysis of the AI-READI multicentre cohort.BMJ open · 2026Article
- Application of AI and digital health tools in public health management of T2DM: from mechanism prediction to personalized treatment.Frontiers in public health · 2026Review
- Navigating open data sharing and privacy in the age of clinical AI research: from reidentification to pseudo-reidentification.EClinicalMedicine · 2026Review
- Computational strategic recruitment for representation and coverage studied in the All of Us Research Program.NPJ digital medicine · 2025Article
- Oculomics meets exposomics: a roadmap for applying multi-modal ocular biomarkers in precision environmental health research.Exposome · 2025Review
- AI-READI: rethinking AI data collection, preparation and sharing in diabetes research and beyond.Nature metabolism · 2024Article
- Adaptive Recruitment Resource Allocation to Improve Cohort Representativeness in Participatory Biomedical Datasets.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- A review of ophthalmology education in the era of generative artificial intelligence.Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
introductionArtificial Intelligence Ready and Equitable for Diabetes Insights (AI-READI) is a data collection project on type 2 diabetes mellitus (T2DM) to facilitate the widespread use of artificial intelligence and machine learning (AI/ML) approaches to study salutogenesis (transitioning from T2DM to health resilience). The fundamental rationale for promoting health resilience in T2DM stems from its high prevalence of 10.5% of the world's adult population and its contribution to many adverse health events.
methodsAI-READI is a cross-sectional study whose target enrollment is 4000 people aged 40 and older, triple-balanced by self-reported race/ethnicity (Asian, black, Hispanic, white), T2DM (no diabetes, pre-diabetes and lifestyle-controlled diabetes, diabetes treated with oral medications or non-insulin injections and insulin-controlled diabetes) and biological sex (male, female) (Clinicaltrials.org approval number STUDY00016228). Data are collected in a multivariable protocol containing over 10 domains, including vitals, retinal imaging, electrocardiogram, cognitive function, continuous glucose monitoring, physical activity, home air quality, blood and urine collection for laboratory testing and psychosocial variables including social determinants of health. There are three study sites: Birmingham, Alabama; San Diego, California; and Seattle, Washington. ETHICS AND DISSEMINATION: AI-READI aims to establish standards, best practices and guidelines for collection, preparation and sharing of the data for the purposes of AI/ML, including guidance from bioethicists. Following Findable, Accessible, Interoperable, Reusable principles, AI-READI can be viewed as a model for future efforts to develop other medical/health data sets targeted for AI/ML. AI-READI opens the door for novel insights in understanding T2DM salutogenesis. The AI-READI Consortium are disseminating the principles and processes of designing and implementing the AI-READI data set through publications. Those who download and use AI-READI data are encouraged to publish their results in the scientific literature.
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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.