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Publications

Journal Articles

  • Heinke, A., Huang, L., Simpkins, K. U., Kalaw, F. G. P., Karsolia, A., Singh, K., Soundarajan, S., Panny, B., Nebeker, C., Baxter, S. L., Lee, C. S., Lee, A. Y., Patel, B., & AI-READI Consortium. (2026). Dataset documentation for responsible AI: Analysis of suitability and usage for health datasets. npj Digital Medicine. https://doi.org/10.1038/s41746-026-02714-2
  • 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
  • Owsley, C., Matthies, D. S., McGwin, G., Edberg, J. C., Baxter, S. L., Zangwill, L. M., Owen, J. P., Lee, C. S., & AI-READI Consortium. (2025). Cross-sectional design and protocol for Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READI). BMJ Open, 15(2), e097449. https://doi.org/10.1136/bmjopen-2024-097449
  • 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

Preprints

  • Matthies, D. S.; Edberg, J. C.; Baxter, S. L.; Lee, A. Y.; Lee, C. S.; McGwin Jr., G.; Owen, J. P.; Zangwill, L. M.; Owsley, C.; the AI-READI Consortium (2026). A Manual of Procedures for the Generation of the AI-Ready and Exploratory Atlas for Diabetes Insights (AI-READI) Database. medRxiv. https://doi.org/10.64898/2026.03.30.26349552
  • 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
  • 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
  • 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
  • Clark, T., Caufield, H., Parker, J. A., Al Manir, S., Amorim, E., Eddy, J., Gim, N., Gow, B., Goar, W., Haendel, M., Hansen, J. N., Harris, N., Hermjakob, H., Joachimiak, M., Jordan, G., Lee, I.-H., McWeeney, S. K., Nebeker, C., Nikolov, M., Shaffer, J., Sheffield, N., Sheynkman, G., Stevenson, J., Chen, J. Y., Mungall, C., Wagner, A., Kong, S. W., Ghosh, S. S., 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

Reports

  • 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
  • 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
  • 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
  • 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

Posters

  • 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

Software

Webinars/Lectures

Dataset 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. Projects that publish using our datasets are required to cite both our marker paper and dataset. Below, we provide a list of those citations, as tracked by Google Scholar.

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)

  1. 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

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

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