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Regulations/Good AI Practice
Good AI Practice · regulatory evidence-readiness

Good AI Practice turns model use into accountable evidence.

This workspace connects Good AI Practice to regulated work: problem formulation, data fitness, model development, validation, human accountability, transparency, lifecycle monitoring, and communication with regulators or reviewers.

Source basis: Guiding principles for Good AI Practice in drug development and related lifecycle guidanceUse: evidence-readinessBoundary: not legal advice
Good AI Practice TRACE FDA/EMA GOOD AI PREMA/HMA AI LIFECFDA AI DRUG DEVEGMLP MEDICAL-DEV
Workspace scope

What this workspace makes usable.

Good AI Practice

Guiding principles for Good AI Practice in drug development and related lifecycle guidance is mapped here for regulated life-sciences, health-tech, AI governance, and operational quality work. Regulator-facing principles for AI use across drug development, medicinal product lifecycle, and related health-product evidence contexts.

These principles are not a single binding regulation. iFeed treats them as source-backed governance direction and evidence-readiness input.

Work object 01

Context of use

AI evidence starts with a precise problem, intended use, and decision context.

Work object 02

Data relevance and quality

Data provenance, representativeness, completeness, and bias need review.

Work object 03

Model development

Development choices should be documented and linked to intended use.

Work object 04

Validation and performance

Performance evidence must fit context, population, endpoint, and risk.

Work object 05

Human accountability

AI systems should not erase responsibility for decisions.

Work object 06

Transparency

Users and reviewers need understandable information about limits and use.

Work object 07

Lifecycle monitoring

Models and workflows need monitoring after deployment or adoption.

Work object 08

Regulatory engagement

Novel AI uses may need early discussion with regulators or qualified reviewers.

Current source anchors

Primary references stay visible.

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Official FDA page

FDA guiding principles for Good AI Practice in drug development

2026-01-14 · FDA page for common Good AI Practice principles in drug development.

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Official EMA news

EMA and FDA common AI principles news

2026-01-14 · EU-facing publication context for the common principles.

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Official EMA/HMA reflection paper

EMA/HMA AI in medicinal product lifecycle

2024-09-30 · Broader medicinal-product lifecycle reflection layer for AI use and governance.

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Official regulator principles

FDA/Health Canada/MHRA GMLP principles

2021-10-27 · Ten guiding principles for Good Machine Learning Practice for medical device development.

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Foundation route

The workspace can grow without breaking the source trail.

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Structure

The ten subchapters are fixed surfaces for future public assets.

Overview · Source Library · Articles & Clauses · Applicability · Interpretation · Evidence Readiness · AI Governance · Operational Flow · Actors & Services · Updates & Assets

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