Cross-sectional design and protocol for Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READI)
AI-READI aims to establish standards, best practices and guidelines for collection, preparation and sharing of the data for the purposes of AI/ML, including guidance from bioethicists. Following Findable, Accessible, Interoperable, Reusable principles, AI-READI can be viewed as a model for future efforts to develop other medical/health data sets targeted for AI/ML. AI-READI opens the door for novel insights in understanding T2DM salutogenesis. The AI-READI Consortium are disseminating the principles and processes of designing and implementing the AI-READI data set through publications. Those who download and use AI-READI data are encouraged to publish their results in the scientific literature.
Authors
- Julia P. Owen (ORCID: https://orcid.org/0000-0003-4137-3834)
- Cynthia Owsley (ORCID: https://orcid.org/0000-0003-3424-011X)
- Linda M. Zangwill (ORCID: https://orcid.org/0000-0002-1143-5224)
- Cecilia S. Lee (ORCID: https://orcid.org/0000-0003-1994-7213)
- Sally L. Baxter (ORCID: https://orcid.org/0000-0002-5271-7690)
- Jeffrey C. Edberg
- Gerald McGwin
- Dawn S Matthies
Institutions
- University of Washington (US)
- University of Alabama at Birmingham (US)
- University of California San Diego (US)
Publication Details
- Journal
- BMJ Open
- Published
- 2025-02-01
- DOI
- https://doi.org/10.1136/bmjopen-2024-097449
- Citations
- 8
- Primary Topic
- Health, psychology, and well-being
- Type
- article
- Field-Weighted Citation Impact
- 22.52
Funders
- Research to Prevent Blindness
- National Institutes of Health