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 (34)
Evangelista, J.E., Clarke, D.J.B., Byrd, A.I.. 2026. The CFDE Workbench: integrating metadata and processed data from Common Fund programs. Elsevier. https://www.sciencedirect.com/science/article/pii/S0022283626000045
Hallaj, S., Heinke, A., Kalaw, F.G.P., Gim, N., Blazes, M.. 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
Sarwar, M.A., Damaševičius, R., Belousovienė, E.. 2026. Systematic review of Artificial Intelligence-based methods for glycemic control and risk prediction in intensive care units. Elsevier. https://www.sciencedirect.com/science/article/pii/S0933365726000618
Soumma, S.B., Arefeen, A., Carpenter, S.M.. 2026. Counterfactual Modeling with Fine-Tuned LLMs for Health Intervention Design and Sensor Data Augmentation. arxiv.org. https://arxiv.org/abs/2601.14590
Voultsiou, E., Moussiades, L.. 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
Nguyen, T.T., Cruz, M.J., Chokshi, T., Potter, V.L.. 2026. Factors Associated with Machine Learning-Based Predictions of Retinal Aging using Teleretinal Screening Images from Patients with Diabetes. Elsevier. https://www.sciencedirect.com/science/article/pii/S2666914526000503
Um, K.M., Kim, B.J., Ying, G.S.. 2026. Association of diabetes severity with cognitive function in US adults: a cross-sectional analysis of the AI-READI multicentre cohort. bmjopen.bmj.com. https://bmjopen.bmj.com/content/16/3/e110831.abstract
Zhou, P., Dong, Z., Lee, I., Zhang, A., Dick, R.. 2026. Report for NSF Workshop on Algorithm-Hardware Co-design for Medical Applications. arxiv.org. https://arxiv.org/abs/2603.10976
McCarthy, A., Eltemsah, L., Cui, A., Diamond, M.E.. 2026. Patient perceptions of artificial intelligence in ophthalmology: a cross-sectional survey study. bjo.bmj.com. https://bjo.bmj.com/content/early/2026/02/26/bjo-2025-328498.abstract
Wagner, M., Leutloff, C.J.. 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
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 (14)
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
TT Nguyen, MJ Cruz, T Chokshi, VL Potter. 2026. Factors Associated with Machine Learning-Based Predictions of Retinal Aging using Teleretinal Screening Images from Patients with Diabetes. Elsevier. https://www.sciencedirect.com/science/article/pii/S2666914526000503
J Park, I Gaynanova. 2026. Fr\'echet regression of multivariate distributions with nonparanormal transport. arxiv.org. https://arxiv.org/abs/2603.07014
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. 2025. Attengluco: Multimodal transformer-based blood glucose forecasting on ai-readi dataset. ieeexplore.ieee.org. https://ieeexplore.ieee.org/abstract/document/11251776/
C Owsley, DS Matthies, G McGwin, JC Edberg. 2025. Cross-sectional design and protocol for artificial intelligence ready and equitable atlas for diabetes insights (AI-READI). bmjopen.bmj.com. https://bmjopen.bmj.com/content/15/2/e097449.abstract
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/