A tailored course, built for your situation
Governance for AI in Regulated Medical Devices: Aligning Innovation with Compliance
Implementation-grade systems for deploying AI in FDA-regulated environments without compliance lag
$199 one-time
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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.
Last-minute rework of audit packages due to misaligned model and documentation versioning
The situation this course is for
Technical leaders face recurring delays when releasing AI-powered medical devices because model versions, training data snapshots, and quality documentation fall out of sync, forcing rework during audit or pre-submission reviews.
Who this is for
Head of IT, Security, or Engineering in companies developing AI-driven medical devices requiring FDA or equivalent regulatory clearance
Who this is not for
Teams working on non-regulated AI applications or those not involved in release sign-off or compliance coordination
What you walk away with
- Define which team owns final sign-off on model-to-documentation traceability before submission
- Establish decision rights over whether a model version qualifies as 'audit-ready' without escalation
- Control when QA testing begins based on freeze criteria for training data lineage
- Determine if edge-case retraining invalidates prior risk classification without cross-functional review
- Own the go/no-go call on patch deployments when minor updates affect documented performance thresholds
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Governance in Medical Device Regulation
Establish the core requirements linking AI development to FDA 21 CFR Part 820 and ISO 13485 standards.
12 chapters in this module
- Mapping AI lifecycle stages to quality system obligations
- Understanding predicate device implications for algorithm updates
- Regulatory scope: what counts as a modification requiring new review
- Key differences between SaMD and embedded AI in hardware devices
- Role of clinical evaluation plans in validating AI performance claims
- Integrating post-market surveillance into model monitoring design
- Defining validation boundaries for adaptive vs static models
- FDA guidance on transparency and explainability expectations
- Establishing risk categories for AI-driven diagnostic support
- Documentation hierarchy from design inputs to deployment records
- Linking software bill of materials to model dependencies
- Setting baseline compliance criteria before prototype development
Module 2. Model Development with Built-In Compliance Controls
Embed governance checks directly into ML development workflows to prevent downstream rework.
12 chapters in this module
- Version control strategies for datasets, code, and model weights
- Automated tagging of training runs with regulatory metadata
- Implementing change freeze gates aligned with sprint cycles
- Capturing rationale for hyperparameter selection decisions
- Enforcing reproducibility through containerized training environments
- Logging feature engineering choices for audit reconstruction
- Tracking data provenance from source to processed form
- Validating random seed consistency across environments
- Documenting negative results and abandoned experiments
- Integrating peer review checkpoints into PR workflows
- Setting thresholds for statistical significance in training outcomes
- Maintaining separation between development and validation sets
Module 3. Quality System Integration for AI Workflows
Align AI development processes with existing quality management systems (QMS) without slowing innovation.
12 chapters in this module
- Adapting design history files for machine learning projects
- Mapping user needs to algorithmic performance specifications
- Translating clinical validation objectives into test metrics
- Incorporating human-in-the-loop review protocols
- Managing configuration items across software and model repositories
- Establishing deviation reporting procedures for model drift
- Handling non-conformances related to dataset contamination
- Integrating CAPA processes with model retraining triggers
- Defining release criteria for model updates within QMS
- Auditing model performance against intended use claims
- Linking risk management files to failure mode analysis for AI components
- Maintaining traceability matrices from requirements to validation
Module 4. Regulatory Submission Readiness Packaging
Prepare complete, consistent, and defensible submission packages that minimize reviewer back-and-forth.
12 chapters in this module
- Structuring the AI component summary for regulatory reviewers
- Compiling performance benchmarks across diverse patient populations
- Documenting bias assessment methodologies and results
- Creating model cards tailored to clinical review audiences
- Preparing technical appendices for computational reproducibility
- Summarizing limitations and known failure modes transparently
- Including representative case examples from validation testing
- Linking adverse event reporting mechanisms to model updates
- Describing update policies for post-deployment learning
- Validating user interface outputs for clinician interpretability
- Ensuring consistency between marketing claims and validated capabilities
- Finalizing version locks for all dependencies prior to submission
Module 5. Cross-Functional Alignment Mechanisms
Coordinate engineering, clinical, regulatory, and quality teams around shared milestones and deliverables.
12 chapters in this module
- Designing joint review gates between R&D and QA teams
- Facilitating alignment sessions on acceptable performance thresholds
- Creating shared dashboards for model development progress
- Establishing escalation paths for conflicting priority calls
- Running dry-run mock audits with internal stakeholders
- Synchronizing sprint planning with regulatory milestone dates
- Developing common vocabulary across technical and clinical roles
- Hosting pre-submission alignment workshops with regulators
- Coordinating documentation freezes across parallel workstreams
- Managing handoffs between data science and verification engineers
- Resolving discrepancies in interpretation of regulatory feedback
- Maintaining centralized issue logs accessible to all functions
Module 6. Real-Time Monitoring and Post-Market Oversight
Deploy continuous monitoring systems that maintain compliance after launch.
12 chapters in this module
- Setting up automated alerts for statistically significant performance shifts
- Capturing real-world usage patterns for ongoing benefit-risk analysis
- Logging clinician override events for safety signal detection
- Monitoring demographic distribution of patients affected by predictions
- Updating model performance reports for periodic regulatory filings
- Triggering re-evaluation cycles based on accumulated real-world data
- Managing version deprecation notices for end-of-life models
- Handling emergency patches while maintaining audit trails
- Integrating feedback loops from customer support interactions
- Assessing impact of EHR integration changes on model inputs
- Validating recalibrated models against fresh holdout datasets
- Reporting unexpected use cases emerging in production environments
Module 7. Change Management and Update Protocols
Define clear pathways for updating AI models without triggering full re-submission requirements.
12 chapters in this module
- Classifying update types: minor, moderate, major based on risk impact
- Determining when a new clinical study is required for an update
- Establishing threshold rules for automatic vs manual review
- Documenting rationale for not revalidating certain changes
- Managing patch releases for bug fixes affecting output stability
- Handling infrastructure migrations without altering model behavior
- Updating documentation for deprecated features or inputs
- Communicating changes to end users and supporting staff
- Archiving superseded model versions with access controls
- Conducting regression testing across backward-compatible updates
- Obtaining necessary approvals based on change classification
- Recording decision trails for future audit reconstruction
Module 8. Vendor and Third-Party Risk Oversight
Maintain compliance when leveraging external tools, platforms, or data sources.
12 chapters in this module
- Assessing vendor adherence to medical device development standards
- Reviewing third-party model cards for completeness and accuracy
- Negotiating contractual terms for audit rights and source access
- Validating cloud platform configurations meet data residency rules
- Ensuring API contracts preserve input/output consistency guarantees
- Evaluating open-source library licenses for commercial use risks
- Checking pre-trained models for undocumented biases or constraints
- Monitoring vendor update schedules for potential compatibility breaks
- Maintaining inventory of externally sourced components
- Requiring reproducibility packages from external development partners
- Conducting security scans on all third-party dependencies
- Assigning ownership for ongoing monitoring of outsourced elements
Module 9. Data Governance for Training and Validation
Ensure data integrity, representativeness, and traceability throughout the AI lifecycle.
12 chapters in this module
- Defining inclusion and exclusion criteria for training cohorts
- Documenting data anonymization techniques applied to PHI
- Verifying institutional review board approvals for data use
- Tracking geographic and demographic coverage in datasets
- Assessing temporal validity of historical data sources
- Handling missing data points with transparent imputation methods
- Preserving raw data copies before preprocessing transformations
- Labeling consistency checks across multiple annotators
- Detecting and correcting data leakage between splits
- Storing data dictionaries with field-level definitions
- Logging data refresh cycles and version transitions
- Auditing access permissions for sensitive health information
Module 10. Security and Privacy Safeguards for AI Systems
Protect patient data and system integrity in AI-powered medical devices.
12 chapters in this module
- Hardening inference endpoints against adversarial attacks
- Encrypting model parameters during storage and transmission
- Implementing role-based access controls for model management
- Preventing unauthorized extraction of training data via queries
- Detecting anomalous input patterns suggesting probing attempts
- Securing APIs used for real-time prediction serving
- Validating identity and authorization for update deployments
- Maintaining tamper-evident logs for all system actions
- Applying differential privacy techniques where appropriate
- Conducting penetration testing focused on AI-specific vectors
- Managing cryptographic keys for secure enclaves
- Responding to security incidents involving model manipulation
Module 11. Human Factors and Clinical Usability Assurance
Design AI interfaces that support safe and effective clinician decision-making.
12 chapters in this module
- Conducting usability testing with target clinical specialties
- Presenting uncertainty estimates in clinically actionable formats
- Avoiding automation bias through balanced display design
- Providing clear explanations for AI-generated recommendations
- Testing alert fatigue thresholds in simulated workflows
- Incorporating clinician feedback loops into interface iterations
- Validating alarm prioritization logic with subject matter experts
- Ensuring accessibility standards are met for diverse users
- Assessing impact of AI suggestions on diagnostic independence
- Measuring time savings versus cognitive load increases
- Documenting observed behavioral changes during use studies
- Updating user guides to reflect evolving interaction patterns
Module 12. Sustained Compliance and Organizational Learning
Build institutional knowledge and processes that evolve with regulatory expectations.
12 chapters in this module
- Establishing center of excellence for AI governance practices
- Creating internal certification programs for project teams
- Maintaining living libraries of lessons learned from past submissions
- Benchmarking performance against peer organizations’ public disclosures
- Tracking regulatory trend signals across global jurisdictions
- Updating internal standards in response to new guidance documents
- Sharing best practices across therapeutic area divisions
- Onboarding new hires with standardized governance orientation
- Running quarterly compliance maturity assessments
- Recognizing teams for exemplary documentation and traceability
- Publishing internal white papers on novel technical approaches
- Contributing to industry consortia on responsible AI in healthcare
How this maps to your situation
- Pre-submission readiness
- Cross-functional alignment
- Post-market oversight
- Change control execution
Before vs. after
Before
Audit packages assembled last-minute, version mismatches between models and docs, repeated clarification requests from reviewers
After
Locked version trails, automated evidence generation, submission packages ready weeks ahead of deadline
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters)
- 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: Approximately 6, 8 hours total, designed for completion in short sessions across two weekends.
If nothing changes
Without structured governance, even high-performing models face delayed clearances, avoidable rework, and increased exposure during inspections.
How this compares to the alternatives
Unlike generic AI ethics courses or academic lectures, this program delivers implementation-grade systems used by technical leaders in FDA-regulated device companies to ship compliant AI features on schedule.
Frequently asked
Is this course focused on FDA regulations only?
While FDA 21 CFR Part 820 is a primary reference, the frameworks apply globally including EU MDR, Health Canada, PMDA Japan, and other major jurisdictions.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Are there video lessons or live sessions?
No. The course is text-based with detailed written explanations, templates, and checklists optimized for practitioner reference.
$199 one-time. Approximately 6, 8 hours total, designed for completion in short sessions across two weekends..
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