Mastering AI-Driven Software Compliance for Medical Devices
You're not just building software. You're building trust, safety, and regulatory certainty in one of the most high-stakes industries on earth. Every line of code you approve could mean the difference between patient safety and system failure. The pressure is real-and so is the risk of non-compliance, audit exposure, and delayed market entry. Right now, you may be navigating a maze of evolving standards: FDA's SaMD guidance, EU MDR, IEC 62304, and new AI-specific frameworks from ISO and IMDRF. With artificial intelligence being embedded into diagnostic tools, monitoring systems, and adaptive therapies, the compliance bar is rising faster than your team can adapt. One missed traceability link or undocumented training data decision could halt your entire project. Mastering AI-Driven Software Compliance for Medical Devices is not another theoretical overview. It’s your end-to-end implementation blueprint to confidently align AI-integrated medical software with global regulatory expectations, reduce audit risk, and accelerate time-to-approval-all while building internal credibility as a compliance leader. This course is designed to take you from overwhelmed and reactive to proactive and prepared. In just 21 days of structured learning, you’ll develop a complete AI compliance package: documented risk files, algorithmic validation protocols, data governance workflows, and a submission-ready technical file section that auditors trust. Take it from Elena R., Principal Software QA at a Class III device manufacturer: “We were three months behind on our 510(k) submission because our AI validation evidence was fragmented. After completing this course, I rebuilt our entire software life cycle documentation in four weeks. Our auditor signed off on the first review. I got a promotion.” You don’t need more theory. You need actionable clarity. Here’s how this course is structured to help you get there.Course Format & Delivery Details Flexible, Self-Paced, and Always Up-to-Date
This course is 100% self-paced, with immediate online access upon enrollment. There are no fixed dates, no live sessions, and no time zone conflicts. Whether you're working from Zurich, Boston, or Singapore, you control when and how you engage. Most learners complete the core curriculum in 15–21 days with just 1–2 hours per day of focused work. You’ll gain lifetime access to all materials, including future updates as regulatory standards evolve. AI compliance is not static. Neither is this course. Any changes to FDA, EU, or ISO requirements are reflected in updated content at no additional cost to you. Designed for Real-World Access
The entire platform is mobile-friendly and accessible 24/7 across devices. Review checklist templates on your tablet during a flight or reference risk classification flowcharts from your phone between meetings. Everything is structured for just-in-time learning when pressure is high and time is short. Instructor Support That Delivers Confidence
You’re not navigating this alone. Receive direct guidance from our compliance architects-experts with decades of experience in FDA-cleared AI devices and notified body audits. Ask specific questions, submit draft documentation for feedback, and get actionable insights tailored to your device class, risk level, and jurisdictional focus. Certificate of Completion from The Art of Service
Upon finishing, you’ll receive a Certificate of Completion issued by The Art of Service-a globally recognised credential trusted by regulatory professionals, hiring managers, and quality assurance teams. This certificate validates your mastery of AI compliance integration and demonstrates your commitment to patient safety and regulatory excellence. Transparent, All-Inclusive Pricing
Pricing is straightforward with no hidden fees. One payment unlocks everything. No subscriptions, no upsells, no surprise charges. We accept Visa, Mastercard, and PayPal-secure and fast. Zero-Risk Enrollment: Satisfied or Refunded
We stand behind this course with a firm promise: if it doesn’t meet your expectations, you’re fully refunded. No questions, no complications. You’re protected by our 30-day satisfied or refunded guarantee, which removes all financial risk from your decision. What Happens After You Enroll?
After registration, you’ll receive a confirmation email. Once the course materials are fully activated, your access details will be sent in a separate email. This ensures a smooth, error-free onboarding experience for every learner. This Course Works for You-Even If…
- You’ve never led a full AI software validation effort before
- Your device uses third-party AI models or open-source libraries
- You’re transitioning from traditional software to machine learning systems
- Your team lacks a formal data governance strategy
- You work in a small medtech startup without dedicated regulatory staff
Senior Regulatory Affairs Managers at Class II and III manufacturers have used this same methodology to secure CE marks and FDA clearances. If you’re responsible for ensuring that AI-driven software meets the highest compliance standards, then this course is engineered for your success. Your confidence is our priority. With lifetime access, expert support, and complete risk reversal, you’re positioned to succeed before you even begin.
Module 1: Foundations of AI in Medical Devices - Defining AI, machine learning, and deep learning in the context of regulated software
- Understanding the regulatory shift: Why AI demands a new compliance mindset
- Classification of AI-based medical devices under FDA, EU MDR, and IMDRF
- Differentiating between SaMD and SiMD: Implications for compliance strategy
- Core risks of AI in medical applications: Drift, bias, interpretability, and transparency
- Overview of key international regulatory bodies and their AI positions
- Mapping AI functionality to patient impact: Risk-based decision trees
- Introduction to lifecycle thinking for AI models in medical contexts
- Key differences between traditional and AI-driven software development
- Establishing the business case for early compliance integration
Module 2: Regulatory Frameworks and Compliance Landscapes - Deep dive into FDA’s Artificial Intelligence/Machine Learning (AI/ML) Action Plan
- EU MDR and IVDR: AI-specific requirements for device classification
- IEC 62304: Software lifecycle processes and AI extensions
- ISO 13485: Quality management systems for AI-integrated devices
- Overview of ISO 81001-1: Health software and AI governance
- Understanding IMDRF’s guidance on SaMD and AI adaptation
- Global alignment and divergence: Comparing US, EU, Canada, and Japan
- Notified body expectations for AI validation and documentation
- The role of the EU’s AI Act in shaping future medical device regulation
- Preparing for upcoming changes: Tracking regulatory roadmaps and drafts
- Interpreting FDA’s Predetermined Change Control Plan (PCCP)
- How MHRA’s AI regulatory initiative impacts UK market entry
- Mapping AI components to existing regulatory submission pathways
- Defining “locked” vs. “adaptive” AI models from a regulatory standpoint
- Using International Medical Device Regulators Forum (IMDRF) guidelines to align submission strategies
Module 3: Risk Management for AI-Driven Systems - Applying ISO 14971 to AI algorithms: New failure mode considerations
- Defining AI-specific hazards: Overfitting, data scarcity, concept drift
- Linking algorithmic decisions to patient harm: Risk estimation workflows
- Building AI-assisted FMEA and FTA frameworks
- Integrating risk management with software design inputs
- Using risk matrices tailored for dynamic AI behaviour
- Documenting risk control measures for training, validation, and inference phases
- Residual risk evaluation for black-box AI models
- Risk documentation traceability from user needs to test results
- Handling uncertainty in probabilistic AI outputs
- Creating risk decision logs for audit readiness
- Assigning responsibility for AI risk ownership across teams
- Integrating post-market risk feedback into development cycles
- Automating risk tracking using AI-aware quality systems
- Validating risk mitigation through simulation and stress testing
Module 4: Software Lifecycle and Development Control - Adapting IEC 62304 stages for AI model development
- Integrating machine learning workflows into traditional software planning
- Defining AI development environment controls and versioning
- Establishing model development, training, and retraining procedures
- Software of Unknown Provenance (SOUP) handling for pre-trained AI models
- Managing dependencies: Libraries, frameworks, and containerisation
- Source code and model version control using Git-like systems
- Configuration management for AI models and datasets
- Branching and merging strategies for parallel development
- Change control processes for AI parameter tuning and hyperparameter updates
- Managing collaborative AI development across global teams
- Documenting software architecture for transparency in AI systems
- Secure coding practices for AI inference engines
- Handling model obsolescence and deprecation planning
- Integrating DevSecOps principles into medical AI pipelines
Module 5: Data Governance and Quality Assurance - Principles of medical data integrity in AI: ALCOA+ applied to datasets
- Defining data provenance and lineage tracking systems
- Establishing data curation SOPs for training, validation, and test sets
- Managing bias in training data: Detection and correction techniques
- Data anonymisation and GDPR/HIPAA compliance for AI training
- Designing balanced datasets for multi-class diagnostic models
- Handling missing, corrupted, and edge-case medical data
- Documenting dataset composition and selection rationale
- Versioning datasets as critical software assets
- Secure storage and access controls for medical datasets
- Data augmentation strategies and their audit implications
- Third-party data licensing and usage rights
- Creating data specification documents for regulatory submission
- Monitoring data drift over time in production environments
- Establishing data quality KPIs and metrics
Module 6: AI Model Development and Training - Selecting appropriate algorithms for diagnostic, predictive, and monitoring use cases
- Defining model development goals aligned with clinical endpoints
- Feature engineering for medical data: Signals, images, and time series
- Supervised, unsupervised, and reinforcement learning in regulated contexts
- Hyperparameter tuning and optimisation within compliance boundaries
- Training documentation: Logs, metrics, and configuration snapshots
- Ensuring reproducibility of AI training runs
- Managing compute infrastructure for regulated AI training
- Using containerisation (Docker) for environment consistency
- Training on synthetic vs. real-world medical data
- Handling imbalanced datasets in rare disease prediction
- Quantifying uncertainty in AI model outputs
- Model interpretability requirements: SHAP, LIME, and built-in explainability
- Planning for model retraining and drift adaptation
- Documenting algorithm selection rationale for regulatory review
Module 7: Verification, Validation, and Testing - Differentiating verification and validation in AI software
- Creating V&V plans specific to AI model performance
- Defining acceptance criteria for sensitivity, specificity, and AUC
- Statistical validation methods for AI outputs
- Test case design for edge conditions and rare inputs
- Cross-validation strategies and their documentation
- Stress testing models under suboptimal data conditions
- Monte Carlo simulation for probabilistic model validation
- Confirming model generalisability across populations
- Black-box vs. white-box testing for AI systems
- Developing regression tests for AI updates
- Integrating AI validation into system-level testing
- Automating test pipelines for continuous validation
- Creating test reports that meet regulatory auditor standards
- Managing test environment controls and data sanitisation
Module 8: Clinical Evaluation and Performance Assessment - Linking AI model metrics to clinical performance goals
- Designing clinical evaluation plans for AI SaMD
- Defining clinical endpoints and reference standards
- Retrospective vs. prospective study designs for AI validation
- Using real-world evidence (RWE) in AI performance claims
- Conducting bias studies across demographic variables
- Documenting clinical investigation protocols for regulatory submission
- Handling off-label use and unintended AI behaviour
- Translating algorithmic accuracy to clinical utility
- Reporting clinical performance in technical documentation
- Navigating IRB and ethics committee approvals for AI studies
- Post hoc analysis and subgroup validation
- Establishing clinical safety thresholds for AI decision support
- Integrating clinician feedback into AI performance refinement
- Preparing for FDA’s Real-World Performance (RWP) programme
Module 9: Documentation and Submission Readiness - Creating a unified AI compliance documentation structure
- Mapping AI evidence to FDA’s Software Pre-Cert 2.0 framework
- Building technical file chapters for AI software components
- Documenting algorithm development in Design History Files
- Integrating traceability matrices from requirement to testing
- Writing clear summary reports for notified body reviewers
- Preparing the Software Description Document (SDD) for AI
- Developing the Algorithms and Data Specifications (ADS) section
- Creating Validation Summary Reports with AI-specific metrics
- Structuring the Risk Management File with AI hazard analysis
- Preparing for FDA’s 510(k) AI documentation expectations
- Class III device addenda: Additional AI submission requirements
- Using eSubmission standards: eSTAR, CDRH Electronic Submission Template
- Preparation checklist for EU Technical Documentation (Annex II and III)
- Assembling a submission-ready AI compliance portfolio
Module 10: Cybersecurity and Data Protection - Applying IEC 81001-5-1 to AI-driven medical devices
- Threat modelling for AI inference and data pipelines
- Securing model weights and preventing adversarial attacks
- Data encryption in transit and at rest for AI systems
- Access controls for model training and update permissions
- Audit logging of AI decision events and input data
- Vulnerability management for open-source AI libraries
- Penetration testing strategies for AI-enabled devices
- Secure update mechanisms for adaptive AI models
- Privacy impact assessments for AI data processing
- Ensuring compliance with HIPAA, GDPR, and PIPEDA
- Mitigating model inversion and membership inference risks
- Designing for data minimisation in AI workflows
- Handling cybersecurity disclosures in technical documentation
- Integrating UDI and SBOM into AI software transparency reports
Module 11: Post-Market Surveillance and Ongoing Compliance - Designing feedback loops from real-world performance
- Monitoring AI model performance in live clinical environments
- Creating key performance indicators (KPIs) for AI stability
- Implementing anomaly detection in AI output patterns
- Handling model degradation and drift detection protocols
- Establishing retraining triggers based on performance thresholds
- Updating AI models under a Predetermined Change Control Plan
- Change notification requirements for notified bodies and FDA
- Managing version control and traceability in post-market
- Reporting AI-related incidents in MDR and FDA MAUDE systems
- Conducting periodic benefit-risk reassessments for AI SaMD
- Updating clinical evaluation reports with real-world data
- Planning for sunset and deprecation of AI models
- Archiving models, datasets, and documentation for audits
- Ensuring continuity of support for legacy AI systems
Module 12: Audit Preparation and Regulatory Engagement - Conducting internal audits of AI compliance documentation
- Preparing for notified body audits: Common AI pitfalls
- Responding to FDA audit questions on AI validation
- Creating a compliant audit trail for model development
- Presenting AI evidence to regulatory reviewers clearly
- Role-playing audit simulations with regulator-style questioning
- Documenting all assumptions, limitations, and uncertainties
- Handling requests for source code and training data access
- Preparing for ISO 13485 certification with AI components
- Building a regulatory Q&A repository for your team
- Engaging with regulators proactively on AI innovation
- Submitting pre-submission packages for AI SaMD
- Negotiating acceptable risk levels for probabilistic AI decisions
- Using audit findings to improve future AI development cycles
- Developing a culture of continuous compliance excellence
Module 13: Implementation Projects and Real-World Applications - Project 1: Develop a compliance package for an AI-based ECG classifier
- Project 2: Create a risk file for a diabetic retinopathy screening app
- Project 3: Build a technical documentation section for an adaptive insulin dosing model
- Project 4: Design a data governance framework for a multi-site AI training initiative
- Project 5: Assemble a V&V plan for a radiology decision support tool
- Project 6: Draft a Predetermined Change Control Plan for an evolving AI model
- Project 7: Conduct a clinical evaluation simulation for an arrhythmia detector
- Project 8: Create a cybersecurity report for an AI-powered patient monitor
- Project 9: Develop a post-market surveillance dashboard for AI performance
- Project 10: Prepare a mock audit response package for a notified body inquiry
- Using templates to scale compliance across multiple AI products
- Integrating AI compliance into existing quality management systems
- Aligning cross-functional teams: Engineering, QA, Regulatory, and Clinical
- Establishing governance committees for AI review and approval
- Creating standard operating procedures (SOPs) for future AI projects
Module 14: Certification, Career Advancement, and Next Steps - Final review and quality check of your completed AI compliance portfolio
- Submitting your project for Certificate of Completion eligibility
- Receiving your official Certificate of Completion from The Art of Service
- Understanding how this credential strengthens your professional profile
- Adding your certification to LinkedIn, CV, and job applications
- Leveraging your new expertise for promotions and leadership roles
- Networking with other certified AI compliance professionals
- Accessing exclusive job boards and industry partnerships
- Continuing education pathways in AI governance and digital health
- Staying current with new regulatory updates through member resources
- Using your certification as a differentiator in regulatory interviews
- Presenting your AI compliance framework to executive leadership
- Mentoring colleagues using your structured implementation knowledge
- Building a legacy of compliant, patient-safe AI innovation
- Final checklist: From learning to real-world impact
- Defining AI, machine learning, and deep learning in the context of regulated software
- Understanding the regulatory shift: Why AI demands a new compliance mindset
- Classification of AI-based medical devices under FDA, EU MDR, and IMDRF
- Differentiating between SaMD and SiMD: Implications for compliance strategy
- Core risks of AI in medical applications: Drift, bias, interpretability, and transparency
- Overview of key international regulatory bodies and their AI positions
- Mapping AI functionality to patient impact: Risk-based decision trees
- Introduction to lifecycle thinking for AI models in medical contexts
- Key differences between traditional and AI-driven software development
- Establishing the business case for early compliance integration
Module 2: Regulatory Frameworks and Compliance Landscapes - Deep dive into FDA’s Artificial Intelligence/Machine Learning (AI/ML) Action Plan
- EU MDR and IVDR: AI-specific requirements for device classification
- IEC 62304: Software lifecycle processes and AI extensions
- ISO 13485: Quality management systems for AI-integrated devices
- Overview of ISO 81001-1: Health software and AI governance
- Understanding IMDRF’s guidance on SaMD and AI adaptation
- Global alignment and divergence: Comparing US, EU, Canada, and Japan
- Notified body expectations for AI validation and documentation
- The role of the EU’s AI Act in shaping future medical device regulation
- Preparing for upcoming changes: Tracking regulatory roadmaps and drafts
- Interpreting FDA’s Predetermined Change Control Plan (PCCP)
- How MHRA’s AI regulatory initiative impacts UK market entry
- Mapping AI components to existing regulatory submission pathways
- Defining “locked” vs. “adaptive” AI models from a regulatory standpoint
- Using International Medical Device Regulators Forum (IMDRF) guidelines to align submission strategies
Module 3: Risk Management for AI-Driven Systems - Applying ISO 14971 to AI algorithms: New failure mode considerations
- Defining AI-specific hazards: Overfitting, data scarcity, concept drift
- Linking algorithmic decisions to patient harm: Risk estimation workflows
- Building AI-assisted FMEA and FTA frameworks
- Integrating risk management with software design inputs
- Using risk matrices tailored for dynamic AI behaviour
- Documenting risk control measures for training, validation, and inference phases
- Residual risk evaluation for black-box AI models
- Risk documentation traceability from user needs to test results
- Handling uncertainty in probabilistic AI outputs
- Creating risk decision logs for audit readiness
- Assigning responsibility for AI risk ownership across teams
- Integrating post-market risk feedback into development cycles
- Automating risk tracking using AI-aware quality systems
- Validating risk mitigation through simulation and stress testing
Module 4: Software Lifecycle and Development Control - Adapting IEC 62304 stages for AI model development
- Integrating machine learning workflows into traditional software planning
- Defining AI development environment controls and versioning
- Establishing model development, training, and retraining procedures
- Software of Unknown Provenance (SOUP) handling for pre-trained AI models
- Managing dependencies: Libraries, frameworks, and containerisation
- Source code and model version control using Git-like systems
- Configuration management for AI models and datasets
- Branching and merging strategies for parallel development
- Change control processes for AI parameter tuning and hyperparameter updates
- Managing collaborative AI development across global teams
- Documenting software architecture for transparency in AI systems
- Secure coding practices for AI inference engines
- Handling model obsolescence and deprecation planning
- Integrating DevSecOps principles into medical AI pipelines
Module 5: Data Governance and Quality Assurance - Principles of medical data integrity in AI: ALCOA+ applied to datasets
- Defining data provenance and lineage tracking systems
- Establishing data curation SOPs for training, validation, and test sets
- Managing bias in training data: Detection and correction techniques
- Data anonymisation and GDPR/HIPAA compliance for AI training
- Designing balanced datasets for multi-class diagnostic models
- Handling missing, corrupted, and edge-case medical data
- Documenting dataset composition and selection rationale
- Versioning datasets as critical software assets
- Secure storage and access controls for medical datasets
- Data augmentation strategies and their audit implications
- Third-party data licensing and usage rights
- Creating data specification documents for regulatory submission
- Monitoring data drift over time in production environments
- Establishing data quality KPIs and metrics
Module 6: AI Model Development and Training - Selecting appropriate algorithms for diagnostic, predictive, and monitoring use cases
- Defining model development goals aligned with clinical endpoints
- Feature engineering for medical data: Signals, images, and time series
- Supervised, unsupervised, and reinforcement learning in regulated contexts
- Hyperparameter tuning and optimisation within compliance boundaries
- Training documentation: Logs, metrics, and configuration snapshots
- Ensuring reproducibility of AI training runs
- Managing compute infrastructure for regulated AI training
- Using containerisation (Docker) for environment consistency
- Training on synthetic vs. real-world medical data
- Handling imbalanced datasets in rare disease prediction
- Quantifying uncertainty in AI model outputs
- Model interpretability requirements: SHAP, LIME, and built-in explainability
- Planning for model retraining and drift adaptation
- Documenting algorithm selection rationale for regulatory review
Module 7: Verification, Validation, and Testing - Differentiating verification and validation in AI software
- Creating V&V plans specific to AI model performance
- Defining acceptance criteria for sensitivity, specificity, and AUC
- Statistical validation methods for AI outputs
- Test case design for edge conditions and rare inputs
- Cross-validation strategies and their documentation
- Stress testing models under suboptimal data conditions
- Monte Carlo simulation for probabilistic model validation
- Confirming model generalisability across populations
- Black-box vs. white-box testing for AI systems
- Developing regression tests for AI updates
- Integrating AI validation into system-level testing
- Automating test pipelines for continuous validation
- Creating test reports that meet regulatory auditor standards
- Managing test environment controls and data sanitisation
Module 8: Clinical Evaluation and Performance Assessment - Linking AI model metrics to clinical performance goals
- Designing clinical evaluation plans for AI SaMD
- Defining clinical endpoints and reference standards
- Retrospective vs. prospective study designs for AI validation
- Using real-world evidence (RWE) in AI performance claims
- Conducting bias studies across demographic variables
- Documenting clinical investigation protocols for regulatory submission
- Handling off-label use and unintended AI behaviour
- Translating algorithmic accuracy to clinical utility
- Reporting clinical performance in technical documentation
- Navigating IRB and ethics committee approvals for AI studies
- Post hoc analysis and subgroup validation
- Establishing clinical safety thresholds for AI decision support
- Integrating clinician feedback into AI performance refinement
- Preparing for FDA’s Real-World Performance (RWP) programme
Module 9: Documentation and Submission Readiness - Creating a unified AI compliance documentation structure
- Mapping AI evidence to FDA’s Software Pre-Cert 2.0 framework
- Building technical file chapters for AI software components
- Documenting algorithm development in Design History Files
- Integrating traceability matrices from requirement to testing
- Writing clear summary reports for notified body reviewers
- Preparing the Software Description Document (SDD) for AI
- Developing the Algorithms and Data Specifications (ADS) section
- Creating Validation Summary Reports with AI-specific metrics
- Structuring the Risk Management File with AI hazard analysis
- Preparing for FDA’s 510(k) AI documentation expectations
- Class III device addenda: Additional AI submission requirements
- Using eSubmission standards: eSTAR, CDRH Electronic Submission Template
- Preparation checklist for EU Technical Documentation (Annex II and III)
- Assembling a submission-ready AI compliance portfolio
Module 10: Cybersecurity and Data Protection - Applying IEC 81001-5-1 to AI-driven medical devices
- Threat modelling for AI inference and data pipelines
- Securing model weights and preventing adversarial attacks
- Data encryption in transit and at rest for AI systems
- Access controls for model training and update permissions
- Audit logging of AI decision events and input data
- Vulnerability management for open-source AI libraries
- Penetration testing strategies for AI-enabled devices
- Secure update mechanisms for adaptive AI models
- Privacy impact assessments for AI data processing
- Ensuring compliance with HIPAA, GDPR, and PIPEDA
- Mitigating model inversion and membership inference risks
- Designing for data minimisation in AI workflows
- Handling cybersecurity disclosures in technical documentation
- Integrating UDI and SBOM into AI software transparency reports
Module 11: Post-Market Surveillance and Ongoing Compliance - Designing feedback loops from real-world performance
- Monitoring AI model performance in live clinical environments
- Creating key performance indicators (KPIs) for AI stability
- Implementing anomaly detection in AI output patterns
- Handling model degradation and drift detection protocols
- Establishing retraining triggers based on performance thresholds
- Updating AI models under a Predetermined Change Control Plan
- Change notification requirements for notified bodies and FDA
- Managing version control and traceability in post-market
- Reporting AI-related incidents in MDR and FDA MAUDE systems
- Conducting periodic benefit-risk reassessments for AI SaMD
- Updating clinical evaluation reports with real-world data
- Planning for sunset and deprecation of AI models
- Archiving models, datasets, and documentation for audits
- Ensuring continuity of support for legacy AI systems
Module 12: Audit Preparation and Regulatory Engagement - Conducting internal audits of AI compliance documentation
- Preparing for notified body audits: Common AI pitfalls
- Responding to FDA audit questions on AI validation
- Creating a compliant audit trail for model development
- Presenting AI evidence to regulatory reviewers clearly
- Role-playing audit simulations with regulator-style questioning
- Documenting all assumptions, limitations, and uncertainties
- Handling requests for source code and training data access
- Preparing for ISO 13485 certification with AI components
- Building a regulatory Q&A repository for your team
- Engaging with regulators proactively on AI innovation
- Submitting pre-submission packages for AI SaMD
- Negotiating acceptable risk levels for probabilistic AI decisions
- Using audit findings to improve future AI development cycles
- Developing a culture of continuous compliance excellence
Module 13: Implementation Projects and Real-World Applications - Project 1: Develop a compliance package for an AI-based ECG classifier
- Project 2: Create a risk file for a diabetic retinopathy screening app
- Project 3: Build a technical documentation section for an adaptive insulin dosing model
- Project 4: Design a data governance framework for a multi-site AI training initiative
- Project 5: Assemble a V&V plan for a radiology decision support tool
- Project 6: Draft a Predetermined Change Control Plan for an evolving AI model
- Project 7: Conduct a clinical evaluation simulation for an arrhythmia detector
- Project 8: Create a cybersecurity report for an AI-powered patient monitor
- Project 9: Develop a post-market surveillance dashboard for AI performance
- Project 10: Prepare a mock audit response package for a notified body inquiry
- Using templates to scale compliance across multiple AI products
- Integrating AI compliance into existing quality management systems
- Aligning cross-functional teams: Engineering, QA, Regulatory, and Clinical
- Establishing governance committees for AI review and approval
- Creating standard operating procedures (SOPs) for future AI projects
Module 14: Certification, Career Advancement, and Next Steps - Final review and quality check of your completed AI compliance portfolio
- Submitting your project for Certificate of Completion eligibility
- Receiving your official Certificate of Completion from The Art of Service
- Understanding how this credential strengthens your professional profile
- Adding your certification to LinkedIn, CV, and job applications
- Leveraging your new expertise for promotions and leadership roles
- Networking with other certified AI compliance professionals
- Accessing exclusive job boards and industry partnerships
- Continuing education pathways in AI governance and digital health
- Staying current with new regulatory updates through member resources
- Using your certification as a differentiator in regulatory interviews
- Presenting your AI compliance framework to executive leadership
- Mentoring colleagues using your structured implementation knowledge
- Building a legacy of compliant, patient-safe AI innovation
- Final checklist: From learning to real-world impact
- Applying ISO 14971 to AI algorithms: New failure mode considerations
- Defining AI-specific hazards: Overfitting, data scarcity, concept drift
- Linking algorithmic decisions to patient harm: Risk estimation workflows
- Building AI-assisted FMEA and FTA frameworks
- Integrating risk management with software design inputs
- Using risk matrices tailored for dynamic AI behaviour
- Documenting risk control measures for training, validation, and inference phases
- Residual risk evaluation for black-box AI models
- Risk documentation traceability from user needs to test results
- Handling uncertainty in probabilistic AI outputs
- Creating risk decision logs for audit readiness
- Assigning responsibility for AI risk ownership across teams
- Integrating post-market risk feedback into development cycles
- Automating risk tracking using AI-aware quality systems
- Validating risk mitigation through simulation and stress testing
Module 4: Software Lifecycle and Development Control - Adapting IEC 62304 stages for AI model development
- Integrating machine learning workflows into traditional software planning
- Defining AI development environment controls and versioning
- Establishing model development, training, and retraining procedures
- Software of Unknown Provenance (SOUP) handling for pre-trained AI models
- Managing dependencies: Libraries, frameworks, and containerisation
- Source code and model version control using Git-like systems
- Configuration management for AI models and datasets
- Branching and merging strategies for parallel development
- Change control processes for AI parameter tuning and hyperparameter updates
- Managing collaborative AI development across global teams
- Documenting software architecture for transparency in AI systems
- Secure coding practices for AI inference engines
- Handling model obsolescence and deprecation planning
- Integrating DevSecOps principles into medical AI pipelines
Module 5: Data Governance and Quality Assurance - Principles of medical data integrity in AI: ALCOA+ applied to datasets
- Defining data provenance and lineage tracking systems
- Establishing data curation SOPs for training, validation, and test sets
- Managing bias in training data: Detection and correction techniques
- Data anonymisation and GDPR/HIPAA compliance for AI training
- Designing balanced datasets for multi-class diagnostic models
- Handling missing, corrupted, and edge-case medical data
- Documenting dataset composition and selection rationale
- Versioning datasets as critical software assets
- Secure storage and access controls for medical datasets
- Data augmentation strategies and their audit implications
- Third-party data licensing and usage rights
- Creating data specification documents for regulatory submission
- Monitoring data drift over time in production environments
- Establishing data quality KPIs and metrics
Module 6: AI Model Development and Training - Selecting appropriate algorithms for diagnostic, predictive, and monitoring use cases
- Defining model development goals aligned with clinical endpoints
- Feature engineering for medical data: Signals, images, and time series
- Supervised, unsupervised, and reinforcement learning in regulated contexts
- Hyperparameter tuning and optimisation within compliance boundaries
- Training documentation: Logs, metrics, and configuration snapshots
- Ensuring reproducibility of AI training runs
- Managing compute infrastructure for regulated AI training
- Using containerisation (Docker) for environment consistency
- Training on synthetic vs. real-world medical data
- Handling imbalanced datasets in rare disease prediction
- Quantifying uncertainty in AI model outputs
- Model interpretability requirements: SHAP, LIME, and built-in explainability
- Planning for model retraining and drift adaptation
- Documenting algorithm selection rationale for regulatory review
Module 7: Verification, Validation, and Testing - Differentiating verification and validation in AI software
- Creating V&V plans specific to AI model performance
- Defining acceptance criteria for sensitivity, specificity, and AUC
- Statistical validation methods for AI outputs
- Test case design for edge conditions and rare inputs
- Cross-validation strategies and their documentation
- Stress testing models under suboptimal data conditions
- Monte Carlo simulation for probabilistic model validation
- Confirming model generalisability across populations
- Black-box vs. white-box testing for AI systems
- Developing regression tests for AI updates
- Integrating AI validation into system-level testing
- Automating test pipelines for continuous validation
- Creating test reports that meet regulatory auditor standards
- Managing test environment controls and data sanitisation
Module 8: Clinical Evaluation and Performance Assessment - Linking AI model metrics to clinical performance goals
- Designing clinical evaluation plans for AI SaMD
- Defining clinical endpoints and reference standards
- Retrospective vs. prospective study designs for AI validation
- Using real-world evidence (RWE) in AI performance claims
- Conducting bias studies across demographic variables
- Documenting clinical investigation protocols for regulatory submission
- Handling off-label use and unintended AI behaviour
- Translating algorithmic accuracy to clinical utility
- Reporting clinical performance in technical documentation
- Navigating IRB and ethics committee approvals for AI studies
- Post hoc analysis and subgroup validation
- Establishing clinical safety thresholds for AI decision support
- Integrating clinician feedback into AI performance refinement
- Preparing for FDA’s Real-World Performance (RWP) programme
Module 9: Documentation and Submission Readiness - Creating a unified AI compliance documentation structure
- Mapping AI evidence to FDA’s Software Pre-Cert 2.0 framework
- Building technical file chapters for AI software components
- Documenting algorithm development in Design History Files
- Integrating traceability matrices from requirement to testing
- Writing clear summary reports for notified body reviewers
- Preparing the Software Description Document (SDD) for AI
- Developing the Algorithms and Data Specifications (ADS) section
- Creating Validation Summary Reports with AI-specific metrics
- Structuring the Risk Management File with AI hazard analysis
- Preparing for FDA’s 510(k) AI documentation expectations
- Class III device addenda: Additional AI submission requirements
- Using eSubmission standards: eSTAR, CDRH Electronic Submission Template
- Preparation checklist for EU Technical Documentation (Annex II and III)
- Assembling a submission-ready AI compliance portfolio
Module 10: Cybersecurity and Data Protection - Applying IEC 81001-5-1 to AI-driven medical devices
- Threat modelling for AI inference and data pipelines
- Securing model weights and preventing adversarial attacks
- Data encryption in transit and at rest for AI systems
- Access controls for model training and update permissions
- Audit logging of AI decision events and input data
- Vulnerability management for open-source AI libraries
- Penetration testing strategies for AI-enabled devices
- Secure update mechanisms for adaptive AI models
- Privacy impact assessments for AI data processing
- Ensuring compliance with HIPAA, GDPR, and PIPEDA
- Mitigating model inversion and membership inference risks
- Designing for data minimisation in AI workflows
- Handling cybersecurity disclosures in technical documentation
- Integrating UDI and SBOM into AI software transparency reports
Module 11: Post-Market Surveillance and Ongoing Compliance - Designing feedback loops from real-world performance
- Monitoring AI model performance in live clinical environments
- Creating key performance indicators (KPIs) for AI stability
- Implementing anomaly detection in AI output patterns
- Handling model degradation and drift detection protocols
- Establishing retraining triggers based on performance thresholds
- Updating AI models under a Predetermined Change Control Plan
- Change notification requirements for notified bodies and FDA
- Managing version control and traceability in post-market
- Reporting AI-related incidents in MDR and FDA MAUDE systems
- Conducting periodic benefit-risk reassessments for AI SaMD
- Updating clinical evaluation reports with real-world data
- Planning for sunset and deprecation of AI models
- Archiving models, datasets, and documentation for audits
- Ensuring continuity of support for legacy AI systems
Module 12: Audit Preparation and Regulatory Engagement - Conducting internal audits of AI compliance documentation
- Preparing for notified body audits: Common AI pitfalls
- Responding to FDA audit questions on AI validation
- Creating a compliant audit trail for model development
- Presenting AI evidence to regulatory reviewers clearly
- Role-playing audit simulations with regulator-style questioning
- Documenting all assumptions, limitations, and uncertainties
- Handling requests for source code and training data access
- Preparing for ISO 13485 certification with AI components
- Building a regulatory Q&A repository for your team
- Engaging with regulators proactively on AI innovation
- Submitting pre-submission packages for AI SaMD
- Negotiating acceptable risk levels for probabilistic AI decisions
- Using audit findings to improve future AI development cycles
- Developing a culture of continuous compliance excellence
Module 13: Implementation Projects and Real-World Applications - Project 1: Develop a compliance package for an AI-based ECG classifier
- Project 2: Create a risk file for a diabetic retinopathy screening app
- Project 3: Build a technical documentation section for an adaptive insulin dosing model
- Project 4: Design a data governance framework for a multi-site AI training initiative
- Project 5: Assemble a V&V plan for a radiology decision support tool
- Project 6: Draft a Predetermined Change Control Plan for an evolving AI model
- Project 7: Conduct a clinical evaluation simulation for an arrhythmia detector
- Project 8: Create a cybersecurity report for an AI-powered patient monitor
- Project 9: Develop a post-market surveillance dashboard for AI performance
- Project 10: Prepare a mock audit response package for a notified body inquiry
- Using templates to scale compliance across multiple AI products
- Integrating AI compliance into existing quality management systems
- Aligning cross-functional teams: Engineering, QA, Regulatory, and Clinical
- Establishing governance committees for AI review and approval
- Creating standard operating procedures (SOPs) for future AI projects
Module 14: Certification, Career Advancement, and Next Steps - Final review and quality check of your completed AI compliance portfolio
- Submitting your project for Certificate of Completion eligibility
- Receiving your official Certificate of Completion from The Art of Service
- Understanding how this credential strengthens your professional profile
- Adding your certification to LinkedIn, CV, and job applications
- Leveraging your new expertise for promotions and leadership roles
- Networking with other certified AI compliance professionals
- Accessing exclusive job boards and industry partnerships
- Continuing education pathways in AI governance and digital health
- Staying current with new regulatory updates through member resources
- Using your certification as a differentiator in regulatory interviews
- Presenting your AI compliance framework to executive leadership
- Mentoring colleagues using your structured implementation knowledge
- Building a legacy of compliant, patient-safe AI innovation
- Final checklist: From learning to real-world impact
- Principles of medical data integrity in AI: ALCOA+ applied to datasets
- Defining data provenance and lineage tracking systems
- Establishing data curation SOPs for training, validation, and test sets
- Managing bias in training data: Detection and correction techniques
- Data anonymisation and GDPR/HIPAA compliance for AI training
- Designing balanced datasets for multi-class diagnostic models
- Handling missing, corrupted, and edge-case medical data
- Documenting dataset composition and selection rationale
- Versioning datasets as critical software assets
- Secure storage and access controls for medical datasets
- Data augmentation strategies and their audit implications
- Third-party data licensing and usage rights
- Creating data specification documents for regulatory submission
- Monitoring data drift over time in production environments
- Establishing data quality KPIs and metrics
Module 6: AI Model Development and Training - Selecting appropriate algorithms for diagnostic, predictive, and monitoring use cases
- Defining model development goals aligned with clinical endpoints
- Feature engineering for medical data: Signals, images, and time series
- Supervised, unsupervised, and reinforcement learning in regulated contexts
- Hyperparameter tuning and optimisation within compliance boundaries
- Training documentation: Logs, metrics, and configuration snapshots
- Ensuring reproducibility of AI training runs
- Managing compute infrastructure for regulated AI training
- Using containerisation (Docker) for environment consistency
- Training on synthetic vs. real-world medical data
- Handling imbalanced datasets in rare disease prediction
- Quantifying uncertainty in AI model outputs
- Model interpretability requirements: SHAP, LIME, and built-in explainability
- Planning for model retraining and drift adaptation
- Documenting algorithm selection rationale for regulatory review
Module 7: Verification, Validation, and Testing - Differentiating verification and validation in AI software
- Creating V&V plans specific to AI model performance
- Defining acceptance criteria for sensitivity, specificity, and AUC
- Statistical validation methods for AI outputs
- Test case design for edge conditions and rare inputs
- Cross-validation strategies and their documentation
- Stress testing models under suboptimal data conditions
- Monte Carlo simulation for probabilistic model validation
- Confirming model generalisability across populations
- Black-box vs. white-box testing for AI systems
- Developing regression tests for AI updates
- Integrating AI validation into system-level testing
- Automating test pipelines for continuous validation
- Creating test reports that meet regulatory auditor standards
- Managing test environment controls and data sanitisation
Module 8: Clinical Evaluation and Performance Assessment - Linking AI model metrics to clinical performance goals
- Designing clinical evaluation plans for AI SaMD
- Defining clinical endpoints and reference standards
- Retrospective vs. prospective study designs for AI validation
- Using real-world evidence (RWE) in AI performance claims
- Conducting bias studies across demographic variables
- Documenting clinical investigation protocols for regulatory submission
- Handling off-label use and unintended AI behaviour
- Translating algorithmic accuracy to clinical utility
- Reporting clinical performance in technical documentation
- Navigating IRB and ethics committee approvals for AI studies
- Post hoc analysis and subgroup validation
- Establishing clinical safety thresholds for AI decision support
- Integrating clinician feedback into AI performance refinement
- Preparing for FDA’s Real-World Performance (RWP) programme
Module 9: Documentation and Submission Readiness - Creating a unified AI compliance documentation structure
- Mapping AI evidence to FDA’s Software Pre-Cert 2.0 framework
- Building technical file chapters for AI software components
- Documenting algorithm development in Design History Files
- Integrating traceability matrices from requirement to testing
- Writing clear summary reports for notified body reviewers
- Preparing the Software Description Document (SDD) for AI
- Developing the Algorithms and Data Specifications (ADS) section
- Creating Validation Summary Reports with AI-specific metrics
- Structuring the Risk Management File with AI hazard analysis
- Preparing for FDA’s 510(k) AI documentation expectations
- Class III device addenda: Additional AI submission requirements
- Using eSubmission standards: eSTAR, CDRH Electronic Submission Template
- Preparation checklist for EU Technical Documentation (Annex II and III)
- Assembling a submission-ready AI compliance portfolio
Module 10: Cybersecurity and Data Protection - Applying IEC 81001-5-1 to AI-driven medical devices
- Threat modelling for AI inference and data pipelines
- Securing model weights and preventing adversarial attacks
- Data encryption in transit and at rest for AI systems
- Access controls for model training and update permissions
- Audit logging of AI decision events and input data
- Vulnerability management for open-source AI libraries
- Penetration testing strategies for AI-enabled devices
- Secure update mechanisms for adaptive AI models
- Privacy impact assessments for AI data processing
- Ensuring compliance with HIPAA, GDPR, and PIPEDA
- Mitigating model inversion and membership inference risks
- Designing for data minimisation in AI workflows
- Handling cybersecurity disclosures in technical documentation
- Integrating UDI and SBOM into AI software transparency reports
Module 11: Post-Market Surveillance and Ongoing Compliance - Designing feedback loops from real-world performance
- Monitoring AI model performance in live clinical environments
- Creating key performance indicators (KPIs) for AI stability
- Implementing anomaly detection in AI output patterns
- Handling model degradation and drift detection protocols
- Establishing retraining triggers based on performance thresholds
- Updating AI models under a Predetermined Change Control Plan
- Change notification requirements for notified bodies and FDA
- Managing version control and traceability in post-market
- Reporting AI-related incidents in MDR and FDA MAUDE systems
- Conducting periodic benefit-risk reassessments for AI SaMD
- Updating clinical evaluation reports with real-world data
- Planning for sunset and deprecation of AI models
- Archiving models, datasets, and documentation for audits
- Ensuring continuity of support for legacy AI systems
Module 12: Audit Preparation and Regulatory Engagement - Conducting internal audits of AI compliance documentation
- Preparing for notified body audits: Common AI pitfalls
- Responding to FDA audit questions on AI validation
- Creating a compliant audit trail for model development
- Presenting AI evidence to regulatory reviewers clearly
- Role-playing audit simulations with regulator-style questioning
- Documenting all assumptions, limitations, and uncertainties
- Handling requests for source code and training data access
- Preparing for ISO 13485 certification with AI components
- Building a regulatory Q&A repository for your team
- Engaging with regulators proactively on AI innovation
- Submitting pre-submission packages for AI SaMD
- Negotiating acceptable risk levels for probabilistic AI decisions
- Using audit findings to improve future AI development cycles
- Developing a culture of continuous compliance excellence
Module 13: Implementation Projects and Real-World Applications - Project 1: Develop a compliance package for an AI-based ECG classifier
- Project 2: Create a risk file for a diabetic retinopathy screening app
- Project 3: Build a technical documentation section for an adaptive insulin dosing model
- Project 4: Design a data governance framework for a multi-site AI training initiative
- Project 5: Assemble a V&V plan for a radiology decision support tool
- Project 6: Draft a Predetermined Change Control Plan for an evolving AI model
- Project 7: Conduct a clinical evaluation simulation for an arrhythmia detector
- Project 8: Create a cybersecurity report for an AI-powered patient monitor
- Project 9: Develop a post-market surveillance dashboard for AI performance
- Project 10: Prepare a mock audit response package for a notified body inquiry
- Using templates to scale compliance across multiple AI products
- Integrating AI compliance into existing quality management systems
- Aligning cross-functional teams: Engineering, QA, Regulatory, and Clinical
- Establishing governance committees for AI review and approval
- Creating standard operating procedures (SOPs) for future AI projects
Module 14: Certification, Career Advancement, and Next Steps - Final review and quality check of your completed AI compliance portfolio
- Submitting your project for Certificate of Completion eligibility
- Receiving your official Certificate of Completion from The Art of Service
- Understanding how this credential strengthens your professional profile
- Adding your certification to LinkedIn, CV, and job applications
- Leveraging your new expertise for promotions and leadership roles
- Networking with other certified AI compliance professionals
- Accessing exclusive job boards and industry partnerships
- Continuing education pathways in AI governance and digital health
- Staying current with new regulatory updates through member resources
- Using your certification as a differentiator in regulatory interviews
- Presenting your AI compliance framework to executive leadership
- Mentoring colleagues using your structured implementation knowledge
- Building a legacy of compliant, patient-safe AI innovation
- Final checklist: From learning to real-world impact
- Differentiating verification and validation in AI software
- Creating V&V plans specific to AI model performance
- Defining acceptance criteria for sensitivity, specificity, and AUC
- Statistical validation methods for AI outputs
- Test case design for edge conditions and rare inputs
- Cross-validation strategies and their documentation
- Stress testing models under suboptimal data conditions
- Monte Carlo simulation for probabilistic model validation
- Confirming model generalisability across populations
- Black-box vs. white-box testing for AI systems
- Developing regression tests for AI updates
- Integrating AI validation into system-level testing
- Automating test pipelines for continuous validation
- Creating test reports that meet regulatory auditor standards
- Managing test environment controls and data sanitisation
Module 8: Clinical Evaluation and Performance Assessment - Linking AI model metrics to clinical performance goals
- Designing clinical evaluation plans for AI SaMD
- Defining clinical endpoints and reference standards
- Retrospective vs. prospective study designs for AI validation
- Using real-world evidence (RWE) in AI performance claims
- Conducting bias studies across demographic variables
- Documenting clinical investigation protocols for regulatory submission
- Handling off-label use and unintended AI behaviour
- Translating algorithmic accuracy to clinical utility
- Reporting clinical performance in technical documentation
- Navigating IRB and ethics committee approvals for AI studies
- Post hoc analysis and subgroup validation
- Establishing clinical safety thresholds for AI decision support
- Integrating clinician feedback into AI performance refinement
- Preparing for FDA’s Real-World Performance (RWP) programme
Module 9: Documentation and Submission Readiness - Creating a unified AI compliance documentation structure
- Mapping AI evidence to FDA’s Software Pre-Cert 2.0 framework
- Building technical file chapters for AI software components
- Documenting algorithm development in Design History Files
- Integrating traceability matrices from requirement to testing
- Writing clear summary reports for notified body reviewers
- Preparing the Software Description Document (SDD) for AI
- Developing the Algorithms and Data Specifications (ADS) section
- Creating Validation Summary Reports with AI-specific metrics
- Structuring the Risk Management File with AI hazard analysis
- Preparing for FDA’s 510(k) AI documentation expectations
- Class III device addenda: Additional AI submission requirements
- Using eSubmission standards: eSTAR, CDRH Electronic Submission Template
- Preparation checklist for EU Technical Documentation (Annex II and III)
- Assembling a submission-ready AI compliance portfolio
Module 10: Cybersecurity and Data Protection - Applying IEC 81001-5-1 to AI-driven medical devices
- Threat modelling for AI inference and data pipelines
- Securing model weights and preventing adversarial attacks
- Data encryption in transit and at rest for AI systems
- Access controls for model training and update permissions
- Audit logging of AI decision events and input data
- Vulnerability management for open-source AI libraries
- Penetration testing strategies for AI-enabled devices
- Secure update mechanisms for adaptive AI models
- Privacy impact assessments for AI data processing
- Ensuring compliance with HIPAA, GDPR, and PIPEDA
- Mitigating model inversion and membership inference risks
- Designing for data minimisation in AI workflows
- Handling cybersecurity disclosures in technical documentation
- Integrating UDI and SBOM into AI software transparency reports
Module 11: Post-Market Surveillance and Ongoing Compliance - Designing feedback loops from real-world performance
- Monitoring AI model performance in live clinical environments
- Creating key performance indicators (KPIs) for AI stability
- Implementing anomaly detection in AI output patterns
- Handling model degradation and drift detection protocols
- Establishing retraining triggers based on performance thresholds
- Updating AI models under a Predetermined Change Control Plan
- Change notification requirements for notified bodies and FDA
- Managing version control and traceability in post-market
- Reporting AI-related incidents in MDR and FDA MAUDE systems
- Conducting periodic benefit-risk reassessments for AI SaMD
- Updating clinical evaluation reports with real-world data
- Planning for sunset and deprecation of AI models
- Archiving models, datasets, and documentation for audits
- Ensuring continuity of support for legacy AI systems
Module 12: Audit Preparation and Regulatory Engagement - Conducting internal audits of AI compliance documentation
- Preparing for notified body audits: Common AI pitfalls
- Responding to FDA audit questions on AI validation
- Creating a compliant audit trail for model development
- Presenting AI evidence to regulatory reviewers clearly
- Role-playing audit simulations with regulator-style questioning
- Documenting all assumptions, limitations, and uncertainties
- Handling requests for source code and training data access
- Preparing for ISO 13485 certification with AI components
- Building a regulatory Q&A repository for your team
- Engaging with regulators proactively on AI innovation
- Submitting pre-submission packages for AI SaMD
- Negotiating acceptable risk levels for probabilistic AI decisions
- Using audit findings to improve future AI development cycles
- Developing a culture of continuous compliance excellence
Module 13: Implementation Projects and Real-World Applications - Project 1: Develop a compliance package for an AI-based ECG classifier
- Project 2: Create a risk file for a diabetic retinopathy screening app
- Project 3: Build a technical documentation section for an adaptive insulin dosing model
- Project 4: Design a data governance framework for a multi-site AI training initiative
- Project 5: Assemble a V&V plan for a radiology decision support tool
- Project 6: Draft a Predetermined Change Control Plan for an evolving AI model
- Project 7: Conduct a clinical evaluation simulation for an arrhythmia detector
- Project 8: Create a cybersecurity report for an AI-powered patient monitor
- Project 9: Develop a post-market surveillance dashboard for AI performance
- Project 10: Prepare a mock audit response package for a notified body inquiry
- Using templates to scale compliance across multiple AI products
- Integrating AI compliance into existing quality management systems
- Aligning cross-functional teams: Engineering, QA, Regulatory, and Clinical
- Establishing governance committees for AI review and approval
- Creating standard operating procedures (SOPs) for future AI projects
Module 14: Certification, Career Advancement, and Next Steps - Final review and quality check of your completed AI compliance portfolio
- Submitting your project for Certificate of Completion eligibility
- Receiving your official Certificate of Completion from The Art of Service
- Understanding how this credential strengthens your professional profile
- Adding your certification to LinkedIn, CV, and job applications
- Leveraging your new expertise for promotions and leadership roles
- Networking with other certified AI compliance professionals
- Accessing exclusive job boards and industry partnerships
- Continuing education pathways in AI governance and digital health
- Staying current with new regulatory updates through member resources
- Using your certification as a differentiator in regulatory interviews
- Presenting your AI compliance framework to executive leadership
- Mentoring colleagues using your structured implementation knowledge
- Building a legacy of compliant, patient-safe AI innovation
- Final checklist: From learning to real-world impact
- Creating a unified AI compliance documentation structure
- Mapping AI evidence to FDA’s Software Pre-Cert 2.0 framework
- Building technical file chapters for AI software components
- Documenting algorithm development in Design History Files
- Integrating traceability matrices from requirement to testing
- Writing clear summary reports for notified body reviewers
- Preparing the Software Description Document (SDD) for AI
- Developing the Algorithms and Data Specifications (ADS) section
- Creating Validation Summary Reports with AI-specific metrics
- Structuring the Risk Management File with AI hazard analysis
- Preparing for FDA’s 510(k) AI documentation expectations
- Class III device addenda: Additional AI submission requirements
- Using eSubmission standards: eSTAR, CDRH Electronic Submission Template
- Preparation checklist for EU Technical Documentation (Annex II and III)
- Assembling a submission-ready AI compliance portfolio
Module 10: Cybersecurity and Data Protection - Applying IEC 81001-5-1 to AI-driven medical devices
- Threat modelling for AI inference and data pipelines
- Securing model weights and preventing adversarial attacks
- Data encryption in transit and at rest for AI systems
- Access controls for model training and update permissions
- Audit logging of AI decision events and input data
- Vulnerability management for open-source AI libraries
- Penetration testing strategies for AI-enabled devices
- Secure update mechanisms for adaptive AI models
- Privacy impact assessments for AI data processing
- Ensuring compliance with HIPAA, GDPR, and PIPEDA
- Mitigating model inversion and membership inference risks
- Designing for data minimisation in AI workflows
- Handling cybersecurity disclosures in technical documentation
- Integrating UDI and SBOM into AI software transparency reports
Module 11: Post-Market Surveillance and Ongoing Compliance - Designing feedback loops from real-world performance
- Monitoring AI model performance in live clinical environments
- Creating key performance indicators (KPIs) for AI stability
- Implementing anomaly detection in AI output patterns
- Handling model degradation and drift detection protocols
- Establishing retraining triggers based on performance thresholds
- Updating AI models under a Predetermined Change Control Plan
- Change notification requirements for notified bodies and FDA
- Managing version control and traceability in post-market
- Reporting AI-related incidents in MDR and FDA MAUDE systems
- Conducting periodic benefit-risk reassessments for AI SaMD
- Updating clinical evaluation reports with real-world data
- Planning for sunset and deprecation of AI models
- Archiving models, datasets, and documentation for audits
- Ensuring continuity of support for legacy AI systems
Module 12: Audit Preparation and Regulatory Engagement - Conducting internal audits of AI compliance documentation
- Preparing for notified body audits: Common AI pitfalls
- Responding to FDA audit questions on AI validation
- Creating a compliant audit trail for model development
- Presenting AI evidence to regulatory reviewers clearly
- Role-playing audit simulations with regulator-style questioning
- Documenting all assumptions, limitations, and uncertainties
- Handling requests for source code and training data access
- Preparing for ISO 13485 certification with AI components
- Building a regulatory Q&A repository for your team
- Engaging with regulators proactively on AI innovation
- Submitting pre-submission packages for AI SaMD
- Negotiating acceptable risk levels for probabilistic AI decisions
- Using audit findings to improve future AI development cycles
- Developing a culture of continuous compliance excellence
Module 13: Implementation Projects and Real-World Applications - Project 1: Develop a compliance package for an AI-based ECG classifier
- Project 2: Create a risk file for a diabetic retinopathy screening app
- Project 3: Build a technical documentation section for an adaptive insulin dosing model
- Project 4: Design a data governance framework for a multi-site AI training initiative
- Project 5: Assemble a V&V plan for a radiology decision support tool
- Project 6: Draft a Predetermined Change Control Plan for an evolving AI model
- Project 7: Conduct a clinical evaluation simulation for an arrhythmia detector
- Project 8: Create a cybersecurity report for an AI-powered patient monitor
- Project 9: Develop a post-market surveillance dashboard for AI performance
- Project 10: Prepare a mock audit response package for a notified body inquiry
- Using templates to scale compliance across multiple AI products
- Integrating AI compliance into existing quality management systems
- Aligning cross-functional teams: Engineering, QA, Regulatory, and Clinical
- Establishing governance committees for AI review and approval
- Creating standard operating procedures (SOPs) for future AI projects
Module 14: Certification, Career Advancement, and Next Steps - Final review and quality check of your completed AI compliance portfolio
- Submitting your project for Certificate of Completion eligibility
- Receiving your official Certificate of Completion from The Art of Service
- Understanding how this credential strengthens your professional profile
- Adding your certification to LinkedIn, CV, and job applications
- Leveraging your new expertise for promotions and leadership roles
- Networking with other certified AI compliance professionals
- Accessing exclusive job boards and industry partnerships
- Continuing education pathways in AI governance and digital health
- Staying current with new regulatory updates through member resources
- Using your certification as a differentiator in regulatory interviews
- Presenting your AI compliance framework to executive leadership
- Mentoring colleagues using your structured implementation knowledge
- Building a legacy of compliant, patient-safe AI innovation
- Final checklist: From learning to real-world impact
- Designing feedback loops from real-world performance
- Monitoring AI model performance in live clinical environments
- Creating key performance indicators (KPIs) for AI stability
- Implementing anomaly detection in AI output patterns
- Handling model degradation and drift detection protocols
- Establishing retraining triggers based on performance thresholds
- Updating AI models under a Predetermined Change Control Plan
- Change notification requirements for notified bodies and FDA
- Managing version control and traceability in post-market
- Reporting AI-related incidents in MDR and FDA MAUDE systems
- Conducting periodic benefit-risk reassessments for AI SaMD
- Updating clinical evaluation reports with real-world data
- Planning for sunset and deprecation of AI models
- Archiving models, datasets, and documentation for audits
- Ensuring continuity of support for legacy AI systems
Module 12: Audit Preparation and Regulatory Engagement - Conducting internal audits of AI compliance documentation
- Preparing for notified body audits: Common AI pitfalls
- Responding to FDA audit questions on AI validation
- Creating a compliant audit trail for model development
- Presenting AI evidence to regulatory reviewers clearly
- Role-playing audit simulations with regulator-style questioning
- Documenting all assumptions, limitations, and uncertainties
- Handling requests for source code and training data access
- Preparing for ISO 13485 certification with AI components
- Building a regulatory Q&A repository for your team
- Engaging with regulators proactively on AI innovation
- Submitting pre-submission packages for AI SaMD
- Negotiating acceptable risk levels for probabilistic AI decisions
- Using audit findings to improve future AI development cycles
- Developing a culture of continuous compliance excellence
Module 13: Implementation Projects and Real-World Applications - Project 1: Develop a compliance package for an AI-based ECG classifier
- Project 2: Create a risk file for a diabetic retinopathy screening app
- Project 3: Build a technical documentation section for an adaptive insulin dosing model
- Project 4: Design a data governance framework for a multi-site AI training initiative
- Project 5: Assemble a V&V plan for a radiology decision support tool
- Project 6: Draft a Predetermined Change Control Plan for an evolving AI model
- Project 7: Conduct a clinical evaluation simulation for an arrhythmia detector
- Project 8: Create a cybersecurity report for an AI-powered patient monitor
- Project 9: Develop a post-market surveillance dashboard for AI performance
- Project 10: Prepare a mock audit response package for a notified body inquiry
- Using templates to scale compliance across multiple AI products
- Integrating AI compliance into existing quality management systems
- Aligning cross-functional teams: Engineering, QA, Regulatory, and Clinical
- Establishing governance committees for AI review and approval
- Creating standard operating procedures (SOPs) for future AI projects
Module 14: Certification, Career Advancement, and Next Steps - Final review and quality check of your completed AI compliance portfolio
- Submitting your project for Certificate of Completion eligibility
- Receiving your official Certificate of Completion from The Art of Service
- Understanding how this credential strengthens your professional profile
- Adding your certification to LinkedIn, CV, and job applications
- Leveraging your new expertise for promotions and leadership roles
- Networking with other certified AI compliance professionals
- Accessing exclusive job boards and industry partnerships
- Continuing education pathways in AI governance and digital health
- Staying current with new regulatory updates through member resources
- Using your certification as a differentiator in regulatory interviews
- Presenting your AI compliance framework to executive leadership
- Mentoring colleagues using your structured implementation knowledge
- Building a legacy of compliant, patient-safe AI innovation
- Final checklist: From learning to real-world impact
- Project 1: Develop a compliance package for an AI-based ECG classifier
- Project 2: Create a risk file for a diabetic retinopathy screening app
- Project 3: Build a technical documentation section for an adaptive insulin dosing model
- Project 4: Design a data governance framework for a multi-site AI training initiative
- Project 5: Assemble a V&V plan for a radiology decision support tool
- Project 6: Draft a Predetermined Change Control Plan for an evolving AI model
- Project 7: Conduct a clinical evaluation simulation for an arrhythmia detector
- Project 8: Create a cybersecurity report for an AI-powered patient monitor
- Project 9: Develop a post-market surveillance dashboard for AI performance
- Project 10: Prepare a mock audit response package for a notified body inquiry
- Using templates to scale compliance across multiple AI products
- Integrating AI compliance into existing quality management systems
- Aligning cross-functional teams: Engineering, QA, Regulatory, and Clinical
- Establishing governance committees for AI review and approval
- Creating standard operating procedures (SOPs) for future AI projects