| Autor: |
Clark T; University of Virginia., Caufield H; Lawrence Berkeley National Laboratory., Parker JA; University of California San Diego., Al Manir S; University of Virginia., Amorim E; University of California San Francisco., Eddy J; Avantiqor., Gim N; University of Washington., Gow B; Massachusetts Institute of Technology., Goar W; Nationwide Children's Hospital., Haendel M; University of North Carolina at Chapel Hill., Hansen JN; Stanford University., Harris N; Lawrence Berkeley National Laboratory., Hermjakob H; European Molecular Biology Laboratory - European Bioinformatics Institute., Joachimiak M; Lawrence Berkeley National Laboratory., Jordan G; Sage Bionetworks., Lee IH; Boston Children's Hospital., McWeeney SK; Oregon Health and Science University., Nebeker C; University of California San Diego., Nikolov M; Sage Bionetworks., Shaffer J; University of Washington., Sheffield N; University of Virginia., Sheynkman G; University of Virginia., Stevenson J; Nationwide Children's Hospital., Chen JY; University of Alabama at Birmingham., Mungall C; Lawrence Berkeley National Laboratory., Wagner A; Nationwide Children's Hospital., Kong SW; Boston Children's Hospital., Ghosh SS; Massachusetts Institute of Technology., Patel B; California Medical Innovations Institute., Williams A; Tufts University., Munoz-Torres MC; University of Colorado Anschutz Medical Campus. |
| Abstrakt: |
Biomedical research and clinical practice are in the midst of a transition toward significantly increased use of artificial intelligence (AI) and machine learning (ML) methods. These advances promise to enable qualitatively deeper insight into complex challenges formerly beyond the reach of analytic methods and human intuition while placing increased demands on ethical and explainable artificial intelligence (XAI), given the opaque nature of many deep learning methods. The U.S. National Institutes of Health (NIH) has initiated a significant research and development program, Bridge2AI, aimed at producing new "flagship" datasets designed to support AI/ML analysis of complex biomedical challenges, elucidate best practices, develop tools and standards in AI/ML data science, and disseminate these datasets, tools, and methods broadly to the biomedical community. An essential set of concepts to be developed and disseminated in this program along with the data and tools produced are criteria for AI-readiness of data, including critical considerations for XAI and ethical, legal, and social implications (ELSI) of AI technologies. NIH Bridge to Artificial Intelligence (Bridge2AI) Standards Working Group members prepared this article to present methods for assessing the AI-readiness of biomedical data and the data standards perspectives and criteria we have developed throughout this program. While the field is rapidly evolving, these criteria are foundational for scientific rigor and the ethical design and application of biomedical AI methods. |
