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GEN0740 Mastering ICH GCP for Data Science & AI Leaders in RWD

$199.00
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A tailored course, built for your situation

Mastering ICH GCP for Data Science & AI Leaders in RWD

Build defensible, audit-ready AI applications in real-world data with confidence and clarity

$199 one-time
24-hour access provisioning 30-day money-back guarantee Hand-built implementation playbook
12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
AI in real-world data is moving fast, but without defensible design, it doesn’t survive review

The situation this course is for

Models get challenged. Assumptions get questioned. Without documented alignment to clinical development standards, even high-performing AI systems lose credibility during audits or cross-functional reviews.

Who this is for

Senior data science and AI leaders in pharma who are building reusable, scalable components for real-world data and need to justify design choices under scrutiny

Who this is not for

Entry-level data analysts, non-clinical AI practitioners, or teams not working with regulated health data or clinical development workflows

What you walk away with

  • Articulate the clinical development rationale behind data and model choices using ICH GCP principles
  • Document design decisions with reference to protocol-aligned standards and inspection expectations
  • Respond confidently to cross-functional or regulatory challenges with precedent-backed reasoning
  • Build reusable templates that embed compliance into AI development cycles
  • Strengthen internal credibility by demonstrating depth, not just speed or accuracy

The 12 modules (with all 144 chapters)

Module 1. Understanding ICH GCP in the Context of AI-Driven RWD
Ground your AI work in the clinical trial principles that regulators expect. Learn how ICH E6 and E9 apply to real-world data pipelines and model validation timelines.
12 chapters in this module
  1. How ICH GCP differs from general data science ethics
  2. When real-world data triggers clinical trial expectations
  3. Key sections of ICH E6 relevant to AI model development
  4. ICH E9 and the relevance of estimands to model outcomes
  5. Regulatory scrutiny thresholds for AI in clinical development
  6. Common misalignments between AI outputs and protocol intent
  7. Case study: AI model rejected for lacking protocol traceability
  8. The role of sponsor accountability in AI validation
  9. Why ALCOA+ applies beyond source data to model inputs
  10. How audit trails support defensible AI pipelines
  11. Defining 'primary endpoint' equivalence in predictive models
  12. Mapping model decisions to clinical development stages
Module 2. Establishing Protocol-Aligned AI Objectives
Ensure your AI use cases align with clinical development goals by grounding them in protocol-level intent.
12 chapters in this module
  1. Translating clinical trial objectives into model design
  2. Identifying primary vs secondary AI endpoints
  3. Documenting medical context for predictive targets
  4. Aligning model scope with study population definitions
  5. Avoiding post-hoc justification of AI use cases
  6. Using protocol language to justify data inclusion
  7. How to reference ICH E9 estimands in model goals
  8. Defining success metrics with clinical input
  9. When to involve medical monitors in AI scoping
  10. Capturing intent before model architecture begins
  11. Preventing scope creep with protocol boundaries
  12. Template: AI objective alignment checklist
Module 3. Data Provenance and ALCOA+ for AI Inputs
Apply audit-ready data principles to the inputs of machine learning models.
12 chapters in this module
  1. Why raw model inputs are subject to GCP expectations
  2. Ensuring data is attributable in AI pipelines
  3. Demonstrating legibility of preprocessing steps
  4. Maintaining contemporaneous records in batch jobs
  5. Original data source requirements for model inputs
  6. Ensuring accuracy in derived variables used by models
  7. Completeness checks for training data sets
  8. How consistency applies across model versions
  9. End-to-end traceability from source to inference
  10. Documenting transformations without losing ALCOA+
  11. Using metadata to preserve context in AI training
  12. Template: Data lineage map for AI systems
Module 4. Model Validation Timing and Clinical Relevance
Align model validation schedules with clinical development timelines and inspection cycles.
12 chapters in this module
  1. Why staggered validation beats post-hoc assessment
  2. Defining validation stages using clinical milestones
  3. Synchronizing model updates with protocol amendments
  4. Validation frequency based on data drift thresholds
  5. ICH E6 principles for change control in AI models
  6. When to revalidate after data or feature changes
  7. Linking model performance to safety monitoring
  8. Using DSMB-like review cycles for AI updates
  9. Documenting validation rationale with references
  10. Avoiding validation drift in production models
  11. Template: Model validation schedule aligned to milestones
  12. Audit preparation for model version history
Module 5. Risk-Based Approach to AI Oversight
Implement a proportionate review process based on clinical impact.
12 chapters in this module
  1. Classifying AI models by patient risk level
  2. Tiering oversight based on clinical severity
  3. When to require full validation vs spot checks
  4. Defining escalation paths for high-risk models
  5. Using ICH Q9 principles for AI risk assessment
  6. Documenting risk rationale with clinical input
  7. Avoiding over-engineering low-impact models
  8. Balancing agility with accountability
  9. Risk review frequency by model tier
  10. Cross-functional participation in risk decisions
  11. Template: AI risk classification matrix
  12. Audit trail for risk-based decisions
Module 6. Documenting Model Rationale with GCP Expectations
Create clear, inspection-ready documentation that stands up to peer review.
12 chapters in this module
  1. Why model cards alone aren't sufficient
  2. Including clinical rationale in technical docs
  3. Referencing protocol language in methodology
  4. Version-controlled documentation for AI models
  5. Linking assumptions to clinical context
  6. How to document data exclusions and trade-offs
  7. Building a defensible model narrative
  8. Avoiding vague terms like 'best available data'
  9. Using controlled vocabularies in documentation
  10. Incorporating audit trail references
  11. Template: Model rationale statement
  12. Preparing for follow-up questions from reviewers
Module 7. Cross-Functional Review and Sponsor Accountability
Navigate internal processes with clarity on ownership and escalation.
12 chapters in this module
  1. Defining AI sponsor roles in matrix teams
  2. When data science leads must defer to medical
  3. Creating joint review checklists with clinical
  4. Documenting resolution of cross-functional disputes
  5. Escalation paths for scientific disagreements
  6. Ensuring pharmacovigilance awareness of AI outputs
  7. Legal input on AI-generated safety signals
  8. Finance alignment on resource commitments
  9. Template: Cross-functional AI review agenda
  10. Meeting minutes that satisfy sponsor accountability
  11. Regulatory input in model lifecycle planning
  12. Managing differing interpretations of GCP
Module 8. Audit Trail Design for AI Systems
Build systems that provide complete, verifiable records of model decisions.
12 chapters in this module
  1. Beyond timestamps: capturing decision context
  2. Logging model training with version control
  3. Recording rationale for hyperparameter choices
  4. Preserving data version lineage
  5. Automating audit trail generation in pipelines
  6. Access restrictions and user accountability
  7. Ensuring data integrity in distributed systems
  8. Retaining records for inspection timelines
  9. Validating audit trail completeness
  10. Testing recovery of historical model states
  11. Template: Audit trail requirements for AI
  12. Integrating with enterprise logging systems
Module 9. Change Control in Machine Learning Pipelines
Implement structured governance for updates without slowing innovation.
12 chapters in this module
  1. When a model update triggers change control
  2. Defining minor vs major changes in AI systems
  3. Impact assessment for data and code changes
  4. Documentation requirements for model updates
  5. Approval workflows aligned with risk tier
  6. Using version control to support change tracking
  7. Avoiding uncontrolled shadow pipelines
  8. Training requirements for updated models
  9. Revalidation thresholds for performance drift
  10. Template: AI change control form
  11. Audit preparation for change history
  12. Managing urgent fixes within GCP
Module 10. Training and Competency for AI Teams
Ensure team members understand their roles in GCP-compliant AI.
12 chapters in this module
  1. Defining roles in AI model lifecycle
  2. Competency requirements for data scientists
  3. Training on protocol alignment for AI
  4. Assessing understanding of clinical context
  5. Documenting training completion
  6. Role-specific GCP expectations
  7. Onboarding checklist for new AI staff
  8. Continuing education on regulatory updates
  9. Auditing team competency during inspections
  10. Cross-training between clinical and data teams
  11. Template: AI team training plan
  12. Managing contractor compliance
Module 11. Third-Party Vendor Oversight for AI
Maintain accountability when outsourcing model development.
12 chapters in this module
  1. Defining sponsor responsibilities with vendors
  2. Assessing vendor GCP readiness
  3. Contractual requirements for AI deliverables
  4. Audit rights and transparency clauses
  5. Reviewing vendor documentation quality
  6. Managing data transfer under GCP
  7. Oversight of cloud-based AI platforms
  8. Ensuring vendor change control alignment
  9. Template: Vendor AI oversight checklist
  10. Handling vendor non-compliance
  11. Joint risk assessments with external partners
  12. Preparing for vendor audits
Module 12. Inspection Readiness for AI-Driven RWD
Prepare for regulatory questions with confidence and clarity.
12 chapters in this module
  1. Common AI-related questions from inspectors
  2. Preparing model documentation packages
  3. Rehearsing responses to clinical rationale queries
  4. Organizing audit trails for rapid access
  5. Anticipating follow-up on data choices
  6. Demonstrating protocol alignment in review
  7. Handling requests for raw data and code
  8. Responding to questions about model drift
  9. Presenting risk-based decisions clearly
  10. Template: AI inspection readiness checklist
  11. Internal mock inspection process
  12. Post-inspection improvement planning

How this maps to your situation

  • Establishing defensible AI models in real-world data
  • Preparing for internal and external scrutiny
  • Aligning innovation with clinical development standards
  • Strengthening cross-functional credibility

Before vs. after

Before
AI models are built quickly but questioned often , lacking clear alignment to clinical development standards or audit expectations.
After
Every model includes documented rationale, traceable decisions, and inspection-ready materials , making follow-up questions a chance to demonstrate depth, not defend existence.

What's included with your purchase

  • 12 modules with 12 chapters each (144 chapters total)
  • Downloadable templates and worked examples for every module
  • Hand-built implementation playbook delivered alongside course access
  • 30-day money-back guarantee

Delivery and format

  • Course and learning environment access provisioned within 24 hours of purchase
  • Hand-built implementation playbook delivered alongside course access

Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.

Time investment: 90 minutes total, designed for completion in a single focused session.

If nothing changes
Without defensible design, even high-performing models get sidelined during reviews, delaying adoption and weakening trust in AI-led innovation.

How this compares to the alternatives

Generic compliance courses teach GCP in isolation. This course integrates it directly into AI development workflows , so you don’t just know the rules, you build with them.

Frequently asked

How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me respond to internal audit questions?
Yes , you'll gain specific examples, precedent-backed reasoning, and a clear mapping to inspection expectations.
Is this relevant if I’m not in clinical development?
Yes , if your AI models touch regulated health data or inform clinical decisions, defensibility matters.
$199 one-time. 90 minutes total, designed for completion in a single focused session..

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.

30-day money-back guarantee· 144 chapters· Hand-built playbook included· Account access within 24 hours
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