FAIRSCAPE: An Evolving AI-readiness Framework for Biomedical Research
Author Information
Author(s): Al Manir Sadnan, Levinson Maxwell Adam, Niestroy Justin, Churas Christopher, Parker Jillian A., Clark Timothy
Primary Institution: University of Virginia School of Medicine
Hypothesis
The FAIRSCAPE framework aims to enhance AI-readiness in biomedical datasets by providing transparency and explainability.
Conclusion
The FAIRSCAPE framework successfully generates and integrates critical metadata for ethical and FAIR AI applications in biomedical research.
Supporting Evidence
- The FAIRSCAPE framework was developed to provide transparency in AI applications for biomedical datasets.
- It supports twenty-two of the twenty-eight AI-readiness criteria recommended by the NIH Bridge2AI Program.
- The framework is open-source and licensed under the MIT license.
Takeaway
FAIRSCAPE helps scientists make sure their data is ready for AI by providing clear information about how the data was collected and used.
Methodology
The framework uses Python packages to create, manage, and distribute Research Object (RO)-Crate packages with detailed provenance graphs.
Digital Object Identifier (DOI)
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