GMA
The authors argue that model interpretability remains useful but is not sufficient for trust in autonomous science. The core requirement is a complete, reopenable record of what the system reasoned, did and measured.
Well-designed provenance makes it possible to reconstruct a path, identify errors, repeat an analysis and correct decisions. In nutraceutical work, the same principle links a conclusion to the original paper, extracted datum, transformation and human review.
The comment defines a direction rather than a ready-to-use standard. Interoperable formats, version management, access, intellectual-property protection and accountability for errors still need shared infrastructure and practice.
For nutraceutical scouting and dossiers, provenance can turn AI output into a verifiable, correctable chain that supports quality, compliance and R&D collaboration.
A conceptual comment rather than experimental validation of a standard. Interoperability, cost, record security and responsibility remain unresolved.
Editorial note. This content is intended for industry professionals. It does not constitute medical advice, therapeutic guidance or regulatory advice. Read our editorial method.
Read the original source · Nature Computational Science ↗