Overview
AI-READI is one of the data generation projects funded by the National Institutes of Health (NIH)'s Bridge2AI Program. The AI-READI project is structured into six modules: Data Acquisition, Ethics, Standards, Teaming, Tools, and Skills & Workforce Development. The FAIR Data Innovations Hub is leading the Tools module.
AI-READI Project Goal
The AI-READI project aims to build a flagship AI-ready, ethically sourced dataset to advance research on type 2 diabetes (T2DM) and generate insights into pathways toward improved health.
AI-Readiness Strategy for Project Data
The AI-READI project data will be made FAIR to optimize reuse by humans and machines (i.e., AI/ML program). The data will additionally be shared according to applicable ethical guidelines to enhance AI-readiness.
Role of the FAIR Data Innovations Hub
Our team will lead the development of fairhub.io, a web platform with tools to help researchers manage, curate, and share FAIR, AI-ready datasets.
Impact of AI-READI
Snapshot of key metrics
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Data types to be collected (vitals, electrocardiogram, etc.)
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Team members contributing to the project
Funding
The AI-READI project is funded by the National Institutes of Health (NIH)'s Bridge2AI program.
Research partners
The AI-READI project is led by multiple institutions. In addition to the FAIR Data Innovations Hub, other institutions collaborating on the AI-READI project include: University of Washington, Oregon Health & Science University, Johns Hopkins University, University of California at San Diego, Stanford University, Native BioData Consortium, University of Alabama at Birmingham, and Microsoft.
Timeline
Year 3 development
September 2024 - Aug 2025
Development approach
All software and tools from the AI-READI project, including fairhub.io, are developed under an MIT License from the AI-READI organization on GitHub.
Team
Members
Researchers, engineers, and collaborators behind this project.
Impact related to this project
Showing 18 publications
- 2026Journal Articles
Hallaj, S., Heinke, A., Kalaw, F. G. P., Gim, N., Blazes, M., Owen, J., Dysinger, E., Benton, E. S., Cordier, B. A., Evans, N. G., Li-Pook-Than, J., Snyder, M. P., Nebeker, C., Zangwill, L. M., Baxter, S. L., McWeeney, S., Lee, C. S., Lee, A. Y., Patel, B., & AI-READI Consortium. (2026). Navigating open data sharing and privacy in the age of clinical AI research: From reidentification to pseudo-reidentification. eClinicalMedicine, 91, 103729. https://doi.org/10.1016/j.eclinm.2025.103729
- 2025Preprints
Caufield, H., Ghosh, S., Kong, S. W., Parker, J., Sheffield, N., Patel, B., Williams, A., Clark, T., & Munoz-Torres, M. C. (2025). Standards in the Preparation of Biomedical Research Metadata: A Bridge2AI Perspective. arXiv. https://doi.org/10.48550/arXiv.2508.01141
- 2025Preprints
Heinke, A., Huang, L., Simpkins, K. U., Kalaw, F. G. P., Karsolia, A., Singh, K., Soundarajan, S., Nebeker, C., Baxter, S. L., Lee, C. S., Lee, A. Y., & Patel, B. (2025). Dataset Documentation for Responsible AI: Analysis of Suitability and Usage for Health Datasets. bioRxiv. https://doi.org/10.1101/2025.11.18.689064
- 2025Preprints
Hallaj, S., Heinke, A., Kalaw, F. G. P., Gim, N., Blazes, M., Owen, J., Dysinger, E., Benton, E. S., Cordier, B. A., Evans, N. G., Li-Pook-Than, J., Snyder, M. P., Nebeker, C., Zangwill, L. M., Baxter, S. L., McWeeney, S., Lee, C. S., Lee, A. Y., Patel, B., & on behalf of the AI-READI Consortium. (2025). Open Data Sharing in Clinical Research and Participants Privacy: Challenges and Opportunities in the Era of Artificial Intelligence. ArXiv. https://doi.org/10.48550/arXiv.2508.01140
- 2024Journal Articles
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
- 2024Preprints
Clark, T., Caufield, H., Mohan, J. A., Al, S. M., Amorim, E., Eddy, J., Gim, N., ... Patel, B., Williams, A., & Munoz-Torres, M. C. (2024). AI-readiness for Biomedical Data: Bridge2AI Recommendations. BioRxiv. https://doi.org/10.1101/2024.10.23.619844
- 2024Preprints
Alavi, A., Cha, K., Esfarjani, D. P., Patel, B., Than, J. L. P., Lee, A. Y., Nebeker, C., Snyder, M., & Bahmani, A. (2024). Perspective on Harnessing Large Language Models to Uncover Insights in Diabetes Wearable Data. MedRxiv. https://doi.org/10.1101/2024.07.29.24310315
- 2024Posters
Patel, B., Soundarajan, S., Gasimova, A., Gim, N., Shaffer, J., & Lee, A. (2024). Clinical Dataset Structure: A Universal Standard for Structuring Clinical Research Data and Metadata (Poster) (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.13984769
- 2024Webinars/Lectures
Lee, C., Patel, B., & Baxter, S. (2024). Introduction to AI-READI, Studying Salutogenesis in T2DM (dkNET Presentation) (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.13984710
- 2024Webinars/Lectures
Lee, C., Patel, B., & Baxter, S. (2024). Introduction to AI-READI, Studying Salutogenesis in T2DM (Bridge2AI Lecture Series) (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.13984755
- 2024Reports
Lee, A., Owen, J., Patel, B., Nebeker, C., Lee, C., Zangwill, L., Hurst, S., Singer, S., Li-Pook-Than, J., & Matthews, D. (2024). AI-READI Code of Conduct (2.0). Zenodo. https://zenodo.org/records/13328255
- 2024Reports
Contreras, J., Evans, B., Hurst, S., Patel, B., Mcweeney, S., Lee, C., & Lee, A. (2024). License terms for reusing the AI-READI dataset (1.0). Zenodo. https://doi.org/10.5281/zenodo.10642459
- 2023Reports
Lee, A., Owen, J., Patel, B., Nebeker, C., Lee, C., Zangwill, L., Hurst, S., & Singer, S. (2023). AI-READI Steering Committee Charter (1.0). Zenodo. https://doi.org/10.5281/zenodo.7641684
- 2022Software
FAIRhub study management platform. (started 2022). https://github.com/AI-READI/fairhub-app (Development status: Active)
- 2022Software
FAIRhub data portal. (started 2022). https://github.com/AI-READI/fairhub-portal (Development status: Active)
- 2022Software
pyfairdatatools. (started 2022). https://github.com/AI-READI/pyfairdatatools (Development status: Active)
- 2022Datasets
AI-READI Consortium. (2022). Flagship Dataset of Type 2 Diabetes from the AI-READI Project (1.0.0). FAIRhub. https://doi.org/10.60775/fairhub.1
- 2022Reports
Patel, B., Soundarajan, S., McWeeney, S., Cordier, B. A., & Benton, E. S. (2022). Software Development Best Practices of the AI-READI Project (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.7363102