About
The AI-READI dataset is accessible from FAIRhub at fairhub.io. The dataset's landing page on FAIRhub contains various information about the dataset. Additional details are available in the dataset documentation at docs.aireadi.org
Dataset snapshot
Citations
Projects that publish using our datasets are required to cite both our marker paper and dataset. Below, we provide a list of those citations.
AI-READI Consortium. 2024. AI-READI: rethinking AI data collection, preparation and sharing in diabetes research and beyond. Nature metabolism. https://doi.org/10.1038/s42255-024-01165-x
Citing resources (27)
JE Evangelista, DJB Clarke, AI Byrd. 2026. The CFDE Workbench: integrating metadata and processed data from Common Fund programs. Elsevier. https://www.sciencedirect.com/science/article/pii/S0022283626000045
S Hallaj, A Heinke, FGP Kalaw, N Gim, M Blazes. 2026. Navigating open data sharing and privacy in the age of clinical AI research: from reidentification to pseudo-reidentification. thelancet.com. https://www.thelancet.com/journals/eclinm/article/PIIS2589-5370(25)00664-9/fulltext
E Voultsiou, L Moussiades. 2026. A Systematic Review of Large Language Models in Mental Health: Opportunities, Challenges, and Future Directions. mdpi.com. https://www.mdpi.com/2079-9292/15/3/524
M Wagner, CJ Leutloff. 2026. A Simulation Procedure for Stereological Correction of Early AMD Lesion Sizes Observed in Single OCT-B-Scans. tvst.arvojournals.org. https://tvst.arvojournals.org/article.aspx?articleid=2811366
SB Soumma, A Arefeen, SM Carpenter. 2026. Counterfactual Modeling with Fine-Tuned LLMs for Health Intervention Design and Sensor Data Augmentation. arxiv.org. https://arxiv.org/abs/2601.14590
A Gangwal, A Lavecchia. 2025. Artificial intelligence in preclinical research: enhancing digital twins and organ-on-chip to reduce animal testing. Elsevier. https://www.sciencedirect.com/science/article/pii/S135964462500073X
S An, K Teo, MV McConnell, J Marshall. 2025. AI explainability in oculomics: How it works, its role in establishing trust, and what still needs to be addressed. Elsevier. https://www.sciencedirect.com/science/article/pii/S1350946225000254
JA Rodriguez, NE Palermo, W Song. 2025. Lack of association between hemoglobin A1c and continuous glucose monitor metrics among individuals with prediabetes and normoglycemia. liebertpub.com. https://www.liebertpub.com/doi/abs/10.1177/15209156251379506
E Farahmand, RR Azghan, NT Chatrudi, E Kim. 2025. Attengluco: Multimodal transformer-based blood glucose forecasting on ai-readi dataset. arxiv.org. https://arxiv.org/abs/2502.09919
S Hallaj, A Heinke, FGP Kalaw, N Gim. 2025. Open Data Sharing in Clinical Research and Participants Privacy: Challenges and Opportunities in the Era of Artificial Intelligence. arxiv.org. https://arxiv.org/abs/2508.01140
AI-READI Consortium. Flagship dataset of type 2 diabetes from the AI-READI project. FAIRhub
- v1.0.0 (2024): https://doi.org/10.60775/fairhub.1
- v2.0.0 (2024): https://doi.org/10.60775/fairhub.2
- v3.0.0 (2025): https://doi.org/10.60775/fairhub.3
Citing resources (10)
S Hallaj, A Heinke, FGP Kalaw, N Gim, M Blazes. 2026. Navigating open data sharing and privacy in the age of clinical AI research: from reidentification to pseudo-reidentification. thelancet.com. https://www.thelancet.com/journals/eclinm/article/PIIS2589-5370(25)00664-9/fulltext
S Hallaj, A Heinke, FGP Kalaw, N Gim. 2025. Open Data Sharing in Clinical Research and Participants Privacy: Challenges and Opportunities in the Era of Artificial Intelligence. arxiv.org. https://arxiv.org/abs/2508.01140
E Farahmand, RR Azghan, NT Chatrudi, E Kim. 2025. Attengluco: Multimodal transformer-based blood glucose forecasting on ai-readi dataset. arxiv.org. https://arxiv.org/abs/2502.09919
E Farahmand, RR Azghan, NT Chatrudi. 2025. GluMind: Multimodal Parallel Attention and Knowledge Retention for Robust Cross-Population Blood Glucose Forecasting. arxiv.org. https://arxiv.org/abs/2509.18457
TW Nishihara, FGP Kalaw, A Engmann. 2025. Fostering Multidisciplinary Collaboration in Artificial Intelligence and Machine Learning Education: Tutorial Based on the AI-READI Bootcamp. mededu.jmir.org. https://mededu.jmir.org/2025/1/e83154
DH Bui, P Siirtola, S Tamminen. 2025. Machine Learning–Driven Forecasting of Glucose Drops in Exercise. dl.acm.org. https://dl.acm.org/doi/abs/10.1145/3714394.3756340
I Singh, D Singh, A Aggarwal. 2025. Predicting Type 2 Diabetes Mellitus Using Fundus Eye Scans: A Deep Learning Approach. ieeexplore.ieee.org. https://ieeexplore.ieee.org/abstract/document/11181411/
G De Pablo Laguna. 2025. Predicción multimodal de niveles de glucosa en sangre mediante redes neuronales profundas a partir de sensores mcg, actividad física e imágenes de retina. titula.universidadeuropea.com. https://titula.universidadeuropea.com/handle/20.500.12880/13594
A Alavi, K Cha, DP Esfarjani, B Patel, JLP Than. 2024. Perspective on Harnessing Large Language Models to Uncover Insights in Diabetes Wearable Data. medrxiv.org. https://www.medrxiv.org/content/10.1101/2024.07.29.24310315.abstract
A Alavi, K Cha, DP Esfarjani, B Patel, JLP Than. 2024. Harnessing Large Language Models to Uncover Insights in Diabetes Wearable Data. researchsquare.com. https://www.researchsquare.com/article/rs-4966049/latest