LifeSciencesAIAcademy

Track 09Applied GxP SkillsLocked by prerequisiteOptional

AI Validation Awareness and Model Credibility

Build a working understanding of what credibility evidence an AI use requires, why the answer depends on model influence and decision consequence, and what a non-specialist must be able to ask and evidence.

6 lessons · 5 frameworks · 4–5 hours · 0/6 complete · 0/6 exercises recorded

Locked by prerequisite

This track opens once its prerequisites are complete. Prerequisites are sequenced deliberately: each one supplies a competency this track assumes you already hold.

Track 01 — AI Fundamentals
Track 02 — AI Governance & Regulatory Expectations
Track 05 — Risk Assessment & Risk Recognition
Track 06 — Context of Use

Orientation

Why this matters

Validation of an AI-enabled system is not a single gate that is either passed or failed. It is a proportionate body of credibility evidence, and everyone who relies on the output should understand what that evidence covers and where it stops.

What you will be able to do (5)

Explain how AI validation differs from traditional computerised-system validation
Apply a risk-based credibility approach to a proposed AI use
Assess model influence and decision consequence to determine the evidence required
Identify the credibility evidence appropriate to a given use
Recognise performance drift and the need for ongoing monitoring

Aligned with (5)

FDA — credibility assessment framework for AI in regulatory decision-makingGAMP 5 (2nd edition) — risk-based computerised-system approachICH Q9(R1) — quality risk managementEU GMP Annex 11 and draft Annex 22 — computerised systems and AINIST AI RMF — measure function

Maps to published expectations. Competency demonstrated through assessment.

Regulatory alignment indicates that curriculum topics map to published regulatory expectations. It does not constitute agency approval, certification, legal advice or a determination of organizational compliance.

View source evidence, status and applicability

FDA — credibility assessment framework for AI in regulatory decision-making

Draft regulatory guidanceUnited States

Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products ↗

Relevant provisions: Seven-step risk-based credibility assessment framework
Status and date: January 2025. Draft; not for implementation and non-binding.
Why it maps: Maps the lesson to FDA's context-of-use and credibility concepts without treating draft recommendations as law.
Applicability limit: Applies to AI used to produce information or data supporting FDA regulatory decisions for drugs and biological products. It is not a general AI-use mandate.

Primary source last verified 2026-08-24

GAMP 5 (2nd edition) — risk-based computerised-system approach

Industry guidanceInternational industry practice

ISPE GAMP 5 — A Risk-Based Approach to Compliant GxP Computerized Systems, 2nd ed. ↗

Relevant provisions: Lifecycle, intended use, critical thinking, supplier involvement and risk-based assurance
Status and date: July 2022. Non-binding unless adopted by an organisation, contract or authority.
Why it maps: Supplies a recognised management or assurance practice; it is identified as non-binding unless separately adopted.
Applicability limit: Widely used industry guidance. It must not be described as legislation or a regulator-issued mandate.

Primary source last verified 2026-08-24

ICH Q9(R1) — quality risk management

Harmonised guidelineICH regions; implemented through regional frameworks

ICH Q9(R1) — Quality Risk Management ↗

Relevant provisions: Sections 4–6 and Annexes I–II
Status and date: Step 4, 18 January 2023. Implementation depends on the relevant regional authority and regulated activity.
Why it maps: Supports the track's stated mapping to ICH Q9(R1) — quality risk management without transferring duties beyond the source's scope.
Applicability limit: Supplies quality-risk principles. It does not independently classify an AI system or prescribe one universal control set.

Primary source last verified 2026-08-24

EU GMP Annex 11 and draft Annex 22 — computerised systems and AI

GMP requirementEuropean Union GMP

EudraLex Volume 4, Annex 11 — Computerised Systems ↗

Relevant provisions: Sections 1, 4, 7–9, 11–13 and 16–17
Status and date: Revision January 2011. Current Annex 11; came into operation 30 June 2011.
Why it maps: Supports the track's stated mapping to EU GMP Annex 11 and draft Annex 22 — computerised systems and AI without transferring duties beyond the source's scope.
Applicability limit: Applies to computerised systems used as part of GMP-regulated activities. Applicability follows the regulated process and intended use.

Primary source last verified 2026-08-24

Draft regulatory guidanceEuropean Union GMP

Proposed EudraLex Volume 4, Annex 22 — Artificial Intelligence ↗

Relevant provisions: Draft lifecycle, data, model performance, change and human-oversight expectations
Status and date: Stakeholder consultation opened July 2025. Draft consultation text; not effective as of 24 August 2026.
Why it maps: Included to teach prospective change control; the track must preserve its draft, non-effective status.
Applicability limit: Used only as a prospective signal. It must not be presented as a current binding GMP requirement unless and until adopted and applicable.

Primary source last verified 2026-08-24

NIST AI RMF — measure function

Voluntary frameworkNon-sector-specific; international use

NIST AI Risk Management Framework 1.0 ↗

Relevant provisions: MEASURE function
Status and date: 26 January 2023. Voluntary; AI RMF 1.0 is under revision as of August 2026.
Why it maps: Provides a voluntary operating structure for the risk-management decisions practised in the lesson.
Applicability limit: Provides risk-management outcomes and practices. It does not create a legal mandate unless adopted through contract, policy or another authority.

Primary source last verified 2026-08-24

Full source register and editorial method →

Credential

AI Model Credibility and Validation Awareness Badge

Duration

4–5 hours

Audience

Quality, validation and regulatory professionals
System owners and process owners
Anyone relying on AI output for GxP decisions

Prerequisites

Track 01
Track 02
Track 05
Track 06

Behaviours practised (3)

UnderstandVerifyDocument

A team asks whether an AI tool is 'validated'. The vendor supplies a certificate. The certificate covers the software's functional behaviour in the vendor's test environment — not the model's performance on this organisation's data, for this decision, at this level of influence.

Badge requirements (5)

Complete all six lessons and their knowledge checks
Achieve at least 80% across the final badge assessment
Answer every safety-critical question correctly
Classify every filtering or triage use as high influence
Produce a credibility assessment and a monitoring plan with escalation triggers

Lessons (6)

Final badge assessment

AI Model Credibility and Validation Awareness Badge

Credibility assessment sequence
Ordering workflow stepsSafety-critical
Order the steps of a risk-based credibility assessment for a proposed AI use.
Influence and consequence classification
Scenario classificationSafety-critical
Classify model influence and decision consequence across six AI uses.
Evidence adequacy
Identify the unsupported statementSafety-critical
Assess four evidence summaries and identify which do not support their stated credibility goal.
Practical exercise — credibility assessment and monitoring plan
Practical exercise
Produce a credibility assessment with acceptance criteria and an ongoing monitoring plan for one proposed use.

Next recommended track

Track 10 — Documentation & Audit Trails