Read about the exciting AI-READI data generation project in this paper published in Nature Metaboiism.
Cecilia and Aaron Lee are co-principal investigators of the Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READI) project, which has the goal of developing a multimodal dataset for AI-based research on type 2 diabetes mellitus (T2DM). Currently AI_READi is well underway, and is collecting a range of health data from participants with diverse racial/ethnic backgrounds who represent all stages of T2DM. This will allow researchers to better investigate health outcomes associated with T2DM in previously understudied populations, many of which have been significantly impacted by this disease
The project also aims to create a roadmap for ethical and equitable research and is structured with cross-disciplinary modules that focus on different aspects of the project, including data collection, team building, ethical oversight, development of a skilled diverse workforce, and the creation of data collection tools and standards.
AI_READI year 2 data is now live for download with >165,000 files (2TB of data) from more than 1000 participants. You can request access to the data at airead.org.
Read more about this exciting project here on the AI-READI website, and also on the NIH Common Fund Bridge to Artificial Intelligence (Bridge2AI) website.
AI-READI Consortium. AI-READI: rethinking AI data collection, preparation and sharing in diabetes research and beyond. Nat Metab (2024). https://doi.org/10.1038/s42255-024-01165-x