Black-box data: a new paradigm for biomedicine in the AI era

Journal article : Review

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.

Authors: Naef, L; Bronstein, M

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Royal Society of Chemistry
• Journal: Chemical Science
• Publication date: 2026-04-08
• Acceptance date: 2026-03-31
• DOI: 10.1039/d6sc01189f
• EISSN: 2041-6539
• ISSN: 2041-6520
• Language: English
• Keywords: Biomedicine; Cornerstone; Field (mathematics); Paradigm shift; Transition (genetics)
• Subtype: Review