In this section
@article{Naef:2026:10.1039/d6sc01189f,
author = {Naef, L and Bronstein, M},
doi = {10.1039/d6sc01189f},
journal = {Chem Sci},
pages = {8327--8344},
title = {Black-box data: a new paradigm for biomedicine in the AI era.},
url = {http://dx.doi.org/10.1039/d6sc01189f},
volume = {17},
year = {2026}
}
TY - JOUR
AB - As artificial Intelligence cements its role as a cornerstone of scientific discovery, the field is undergoing a fundamental shift beyond the current transition from "white-box" first-principles models to "black-box" deep learning. We argue that a parallel, necessary transformation is emerging in data generation: the rise of "black-box data." These data sources are intentionally optimized for machine consumption rather than human intuition-a trade-off we contend is essential to achieving the scale required for high-capacity biological foundation models. This article defines the "black-box data" paradigm, explores the necessity of this shift for the future of AI-driven science, and provides a unifying taxonomy illustrated by both historical precedents and contemporary breakthroughs.
AU - Naef,L
AU - Bronstein,M
DO - 10.1039/d6sc01189f
EP - 8344
PY - 2026///
SN - 2041-6520
SP - 8327
TI - Black-box data: a new paradigm for biomedicine in the AI era.
T2 - Chem Sci
UR - http://dx.doi.org/10.1039/d6sc01189f
UR - https://www.ncbi.nlm.nih.gov/pubmed/41971240
VL - 17
ER -