Healthcare AI Validation & Regulatory Compliance Training Course

Course Overview

Learning Objectives

• Interpret health regulator obligations for organizations developing, buying or using AI.
• Determine when an AI tool is a medical device and how its risk class is assigned.
• Define intended use, users and operating limits as the basis for validation.
• Design pre-deployment validation plans with local data, acceptance criteria and decision rules.
• Apply bias and fairness testing across patient subgroups, languages and care settings.
• Assess explainability, human oversight and fallback arrangements for AI-supported decisions.
• Conduct vendor due diligence on evidence, data handling, security and updates.
• Build contract, data protection and accountability requirements into health AI procurement.
• Establish post-deployment monitoring for performance, drift, safety signals and user overrides.
• Maintain audit trails, model inventories and documentation that withstand regulatory inspection.
• Manage model changes, retraining and AI incidents through controlled review and reporting.
• Prepare an AI assurance and compliance plan for the organization's health AI portfolio.

Who Should Attend

Learning Outcomes

• By the end of this course, participants will be able to:
• Explain the obligations regulators set for developing, buying and using health AI.
• Classify AI tools against medical device definitions and risk categories.
• Draft intended use statements that define users, settings and operating limits.
• Design validation studies with local data, sound metrics and agreed acceptance criteria.
• Test AI performance for bias across relevant patient and population subgroups.
• Evaluate human oversight, explainability and fallback arrangements before go-live.
• Conduct vendor due diligence and build compliance requirements into contracts.
• Set up monitoring for drift, performance decline, user overrides and safety signals.
• Maintain audit trails, model inventories and version records ready for inspection.
• Handle model changes and AI incidents through controlled review and reporting.
• Prepare an AI assurance and compliance plan for the organization's health AI portfolio.

Course Outline