LifeSciencesAIAcademy

Track 01FoundationalAvailableRequired

AI Fundamentals

Build accurate mental models of what AI systems do, where they are already used in life sciences, and why their failure modes demand human governance.

5 lessons · 5 frameworks · 3–4 hours · 0/5 complete · 0/5 exercises recorded

Orientation

Why this matters

Most AI errors in regulated work are not caused by exotic model behaviour. They are caused by people who did not understand what the tool was actually doing — treating a fluent paragraph as a verified fact, or assuming a system that performs well on average performs well on their batch, their patient or their document.

What you will be able to do (5)

Explain in plain language what an AI system is producing and on what basis
Recognise the difference between deterministic software and probabilistic AI output
Identify where AI is already embedded in life-sciences workflows
Name the characteristic failure modes of generative AI and their consequences in GxP work
State why human governance is a requirement rather than a preference

Aligned with (5)

FDA — AI in regulatory decision-making (published expectations)EMA reflection paper on AI in the medicinal product lifecycleNIST AI Risk Management Framework 1.0ISO/IEC 42001 — AI management systemsICH Q9(R1) — Quality risk management

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 — AI in regulatory decision-making (published expectations)

Draft regulatory guidanceUnited States

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

Relevant provisions: Risk-based credibility assessment framework and context-of-use analysis
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 Draft; not for implementation and non-binding.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

EMA reflection paper on AI in the medicinal product lifecycle

Regulatory guidanceEuropean Union medicines regulation

Reflection paper on the use of AI in the medicinal product lifecycle ↗

Relevant provisions: Sections 2.2–2.8: risk, context of use, data, performance and human oversight
Status and date: Adopted September 2024. Current reflection paper; read with applicable EU law and GxP requirements.
Why it maps: Supports the track's stated mapping to EMA reflection paper on AI in the medicinal product lifecycle without transferring duties beyond the source's scope.
Applicability limit: Describes regulatory considerations across the medicinal-product lifecycle. It does not replace binding legislation or GxP requirements.

Primary source last verified 2026-08-24

NIST AI Risk Management Framework 1.0

Voluntary frameworkNon-sector-specific; international use

NIST AI Risk Management Framework 1.0 ↗

Relevant provisions: GOVERN, MAP, MEASURE and MANAGE functions
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

ISO/IEC 42001 — AI management systems

Consensus standardInternational consensus standard

ISO/IEC 42001:2023 — Artificial intelligence management system ↗

Relevant provisions: Clauses 4–10 and Annex A controls
Status and date: December 2023. Voluntary unless adopted by contract, policy, certification scheme or applicable authority.
Why it maps: Supplies a recognised management or assurance practice; it is identified as non-binding unless separately adopted.
Applicability limit: Specifies requirements for an AI management system. Use of the standard does not itself establish regulatory compliance.

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

Full source register and editorial method →

Credential

Life Sciences AI Foundations Badge

Duration

3–4 hours

Audience

All personnel in GxP-adjacent functions
New starters before AI tool access is granted
Functional leaders setting expectations for their teams

Prerequisites

None

Behaviours practised (3)

UnderstandVerifyEscalate

A CMC writer in your organisation asks a general-purpose assistant to summarise stability data for a regulatory response. The summary is fluent, well structured and contains a temperature excursion that never happened. Nothing in the tool warned the writer. This track is about why that happens and what you are expected to do about it.

Badge requirements (5)

Complete all five lessons and their knowledge checks
Achieve at least 80% across the final badge assessment
Answer every safety-critical question correctly
Complete the human-review record exercise with all required evidence elements
Correctly identify the escalation route for an observed AI limitation

Start this track

Lessons (5)

Final badge assessment

Life Sciences AI Foundations Badge

AI capability and limitation knowledge check
Mixed multiple-choice and select-all
Fifteen questions covering model behaviour, failure modes and life-sciences applications.
GxP relevance classification
Scenario classificationSafety-critical
Classify six proposed AI uses by GxP relevance and by the decision each output would influence.
Unsupported-statement identification
Identify the unsupported statementSafety-critical
Given an AI-drafted pharmaceutical paragraph, identify every claim that cannot be supported without further evidence.
Practical exercise — human-review record
Practical exercise
Produce a complete human-review record for an AI-assisted output, including verification evidence and corrections.

Go to the badge assessment

Next recommended track

Track 02 — AI Governance & Regulatory Expectations