Facts and interpretation stay separate.
auditable readingSource fact
Regulators describe principles for AI use, but not one unified binding Good AI Practice rule.
iFeed reading
The practical bridge is evidence: context, data, model, validation, human accountability, and lifecycle monitoring.
Operational meaning
AI use dossiers should show why the model is fit for this regulated decision context.
Do not overclaim
Do not collapse medicines AI principles into medical-device GMLP or claim certification.
The useful question is what work this creates.
iFeed meaningContext of use
AI evidence starts with a precise problem, intended use, and decision context.
Data relevance and quality
Data provenance, representativeness, completeness, and bias need review.
Model development
Development choices should be documented and linked to intended use.
Validation and performance
Performance evidence must fit context, population, endpoint, and risk.
Human accountability
AI systems should not erase responsibility for decisions.
Transparency
Users and reviewers need understandable information about limits and use.
Current public sources for Good AI Practice.
official firstThese links are the public source anchors for this workspace. Interpretation, checklists, and future assets should point back here before being reused outside iFeed.
FDA guiding principles for Good AI Practice in drug development
2026-01-14 · FDA page for common Good AI Practice principles in drug development.
EMA and FDA common AI principles news
2026-01-14 · EU-facing publication context for the common principles.
EMA/HMA AI in medicinal product lifecycle
2024-09-30 · Broader medicinal-product lifecycle reflection layer for AI use and governance.
FDA/Health Canada/MHRA GMLP principles
2021-10-27 · Ten guiding principles for Good Machine Learning Practice for medical device development.