Preparing clinical research data for artificial intelligence readiness: insights from the National Institute of Diabetes and Digestive and Kidney Diseases data centric challenge
We present a generalizable framework for preparing clinical research data for AI applications. The resulting datasets lay a strong foundation for downstream AI/ML applications, setting the stage for a new era of data-driven discoveries.
Authors
- Roberto Rodríguez (ORCID: https://orcid.org/0000-0001-7454-2863)
- Alexander Pilozzi
- Michael A. Keller (ORCID: https://orcid.org/0000-0003-0721-9753)
- Courtney D Shelley (ORCID: https://orcid.org/0000-0002-5497-7453)
- S. D. Lawlor (ORCID: https://orcid.org/0000-0003-3211-067X)
- Marcin J. Domagalski (ORCID: https://orcid.org/0000-0002-2579-9133)
- Alicia Williamson (ORCID: https://orcid.org/0000-0003-3545-2114)
- Ratna R. Thangudu (ORCID: https://orcid.org/0000-0001-6765-0401)
- Padmini Chilappagari
- Lu Yin (ORCID: https://orcid.org/0009-0008-3327-9788)
- Emma Luker
- Anya Dabic
Institutions
- Booz Allen Hamilton (United States) (US)
- National Institute of Diabetes and Digestive and Kidney Diseases (US)
- ICF International (United States) (US)
Publication Details
- Journal
- Journal of the American Medical Informatics Association
- Published
- 2025-07-24
- DOI
- https://doi.org/10.1093/jamia/ocaf114
- Citations
- 2
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 5.88
Funders
- National Institutes of Health
- Centers for Disease Control and Prevention
- National Institute of Allergy and Infectious Diseases
- National Institute of Diabetes and Digestive and Kidney Diseases
- National Institute of Child Health and Human Development