Biomedical Data Manifest: A lightweight data documentation mapping to increase transparency for AI/ML

Abstract Biomedical machine learning (ML) models raise critical concerns about embedded assumptions influencing clinical decision-making, necessitating robust documentation frameworks for datasets that are shared via external repositories. Fairness-aware algorithm effectiveness hinges on users’ prio...

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Autors principals:	Daniel Bottomly, Christopher G. Suciu, Benjamin Cordier, Nathaniel Evans, Alfonso Poire, Christina Zheng, The ARTNet Consortium, Jeffrey W. Tyner, Alan Hutson, Shannon K. McWeeney
Format:	Artigo
Idioma:	Inglês
Publicat:	Nature Portfolio 2026-02-01
Col·lecció:	Scientific Data
Accés en línia:	https://doi.org/10.1038/s41597-026-06670-0
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