Why Choose This AI Certification?
Workforce training in AI governance is essential to equip healthcare professionals with the knowledge and skills to manage emerging AI risks effectively. This includes understanding how to mitigate potential biases, ensuring compliance with regulatory requirements, and safeguarding patient data. Additionally, well-trained teams are better equipped to oversee the ethical implications of AI decisions, thus fostering trust and transparency in AI-driven healthcare.
100% online and self-paced, designed for busy professionals
Specialized knowledge in AI healthcare compliance, a rapidly growing field.
Affordable cost for maximum career value.
“Through our collaboration with AlignMT AI, we’re committed to providing healthcare professionals with the foundational skills necessary to navigate the complexities of AI and make informed, ethical decisions that benefit both their organizations and patients.”
Todd Nelson, Chief Partnership Executive of HFMA
AI Governance Certification: An Overview
AlignMT AI’s Governance Certification equips healthcare professionals with the knowledge and tools to implement robust AI oversight frameworks. Specialized courses cover key topics such as regulatory compliance, risk mitigation, governance committee operations, and real-world monitoring of AI systems. Designed for technical, clinical, operational, and compliance professionals, the program provides actionable insights to ensure AI deployments are transparent, safe, and aligned with evolving regulations like ONC’s HTI-1.
Our Certification Credentials
Outline of AI Certification Courses
Course 1 — Foundation: Enterprise AI Governance Essentials
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What you'll learn: By the end of this course, you'll be able to distinguish between the four types of healthcare AI (rule-based, machine learning, GPAI, and agentic) and how each type changes what governance has to look like; apply the five agentic AI governance guardrails to a deployment scenario; describe the opportunities AI creates across clinical, administrative, and population health functions; navigate the four-layer regulatory environment for healthcare AI; classify AI-related risks across technical, human factors, systemic, and agentic-specific categories; and use the NIST AI Risk Management Framework's four functions as the organizing structure throughout.
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Prerequisites: None.
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Estimated time: 58 minutes.
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Delivery method: Self-paced, online.
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Assessment & passing criteria: A final assessment with a minimum score of 80% required to pass. Missed questions may be retaken up to 4 attempts each.
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Technical requirements: A modern web browser and internet connection. Mobile-responsive and accessible on any device.
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Support: Questions before, during, or after this course can be directed to info at alignmt.ai
Course 2 — AI Governance Committee Operations
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What you'll learn: By the end of this course, you'll be able to run the committee's incident response process for a live AI safety event — selecting the right immediate actions, identifying the top investigation priority, and matching the correct message to each stakeholder group (clinical staff, patients, regulators, vendor); apply the correct weekly, monthly, quarterly, and emergency meeting cadence to specific governance activities; identify what must be documented under CMS's AI patient safety requirements; assign NIST AI RMF function ownership across committee roles; walk through the decommissioning checklist for a high-risk AI system; and distinguish data drift, algorithmic bias, and alert fatigue as related but separate incident signals.
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Prerequisites: None.
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Estimated time: ~1.25 hours.
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Delivery method: Self-paced, online.
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Assessment & passing criteria: A final assessment with a minimum score of 80% required to pass. Missed questions may be retaken up to 4 attempts each.
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Technical requirements: A modern web browser and internet connection. Mobile-responsive and accessible on any device.
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Support: Questions before, during, or after this course can be directed to info at alignmt.ai.
Course 3 — AI Risk & NIST RMF
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What you’ll learn: By the end of this course, you’ll be able to use the NIST AI RMF as the organizing framework for healthcare AI risk management and map its four functions (Govern, Map, Measure, Manage) to the governance committee’s activities; place AI initiatives on the risk prioritization matrix by clinical impact and governance complexity using the six-level risk mitigation hierarchy, and recommend which to advance, redesign, or decline; explain the four-phase validation framework (laboratory testing, shadow mode, limited rollout, full deployment) and the governance question that must be answered before a system advances; recognize the technical risk signals that matter most in healthcare AI (performance degradation, algorithmic bias, data quality failures, and population, practice, seasonal, and technology drift) and the monitoring control for each; determine when AI systems are material to a health insurer’s ORSA reporting and which governance records supply the evidence; and connect NIST’s seven trustworthy AI characteristics to the controls and documentation that demonstrate them, including patient-, clinician-, and audit-level explainability.
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Prerequisites: None. Recommended after Course 1 (Foundation) and Course 2 (Governance Committee Operations).
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Estimated time: ~42 minutes.
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Delivery method: Self-paced, online.
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Assessment & passing criteria: A final assessment with a minimum score of 80% required to pass. Missed questions may be retaken up to 4 attempts each.
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Technical requirements: A modern web browser and internet connection. Mobile-responsive and accessible on any device.
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Support: Questions before, during, or after this course can be directed to info at alignmt.ai.
Course 4 — Regulatory Compliance & Patient Transparency
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What you’ll learn: By the end of this course, you’ll be able to classify AI governance documents into the Three-Tier Documentation Framework (system-level documentation, operational documentation, and compliance records) and identify what belongs in each tier, including where the CHAI Applied Model Card sits; identify the patient transparency, consent, and reporting obligations that specific state laws impose, including disclosure placement for generative-AI clinical communications (California AB 3030), written patient consent before AI records or transcribes a therapy session (Illinois WOPR Act, HB 1806), and quarterly reporting on AI in utilization review (Maryland HB 820); map an AI deployment to the applicable US federal requirements (FDA SaMD classification, ONC HTI-1 transparency, the HIPAA Security Rule, and CMS IA_PSPA_34 documentation of AI-attributable adverse events and near-misses) using the regulatory mapping dashboard; design an ambient-documentation disclosure workflow that specifies when disclosure occurs, the plain-language script used, how disclosure and consent are logged, and a genuine opt-out without clinical penalty; and explain what the EU AI Act requires of US-based healthcare organizations whose AI outputs reach EU patients, including the revised high-risk timelines and the documentation required.
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Prerequisites: None. Recommended after Course 1 (Foundation).
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Estimated time: ~28 minutes.
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Delivery method: Self-paced, online.
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Assessment & passing criteria: A final assessment with a minimum score of 80% required to pass. Missed questions may be retaken up to 4 attempts each.
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Technical requirements: A modern web browser and internet connection. Mobile-responsive and accessible on any device.
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Support: Questions before, during, or after this course can be directed to info at alignmt.ai.
Course 5 — Vendor & Third-Party Risk
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What you’ll learn: By the end of this course, you’ll be able to walk through the four phases of the vendor lifecycle governance framework (pre-procurement assessment, contracting, ongoing oversight, and retirement and offboarding), identifying the governance actions required at each phase and recognizing pre-procurement red flags such as a vendor unable to provide bias testing results across demographic subpopulations; identify the provisions every AI vendor contract should include (bias testing attestation, right to audit, minimum 30 days’ notice of material model updates, termination assistance, indemnification, and data ownership) and select the correct provision for a given vendor scenario; recognize shadow AI, meaning consumer-grade or unapproved AI tools used outside formal procurement, and describe the steps to bring it into formal governance review (a permitted-use policy, inventory methods, and a defined review pathway); and explain why vendor model updates require governance committee approval, shadow mode testing, and repeated bias testing before reaching production, including when an update may require new FDA 510(k) clearance.
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Prerequisites: None. Recommended after Course 1 (Foundation).
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Estimated time: ~11 minutes.
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Delivery method: Self-paced, online.
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Assessment & passing criteria: A final assessment with a minimum score of 80% required to pass. Missed questions may be retaken up to 4 attempts each.
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Technical requirements: A modern web browser and internet connection. Mobile-responsive and accessible on any device.
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Support: Questions before, during, or after this course can be directed to info at alignmt.ai.
Course 6 — Incident Response for AI Applications
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What you’ll learn: By the end of this course, you’ll be able to classify an AI-related event using the five-level severity framework and identify the response timeline and reporting obligation that applies; recognize early warning indicators that an AI system is degrading (technical, clinical, and human-factors signals, including agentic AI access-control signals) and select the correct containment action, including disabling an unsafe system and revoking an AI agent’s API credentials rather than only disabling its interface; activate the incident response protocol using the decision tree, assemble the core and conditional response team for the incident type, and sequence notifications across the four stakeholder circles using the communication templates; and complete the post-incident checklist across its four time windows (48 hours, 1 week, 1 month, and ongoing) and document the eight lessons-learned elements, including identifying the root cause of an incident.
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Prerequisites: None. Recommended after Course 1 (Foundation) and Course 2 (Governance Committee Operations).
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Estimated time: ~14 minutes.
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Delivery method: Self-paced, online.
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Assessment & passing criteria: A final assessment with a minimum score of 80% required to pass. Missed questions may be retaken up to 4 attempts each.
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Technical requirements: A modern web browser and internet connection. Mobile-responsive and accessible on any device.
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Support: Questions before, during, or after this course can be directed to info at alignmt.ai.
Course 7 — Privacy & Data Protection
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What you’ll learn: By the end of this course, you’ll be able to identify AI-generated predictions, risk scores, inferences, and draft notes about identifiable patients as PHI and apply the minimum-necessary standard and BAA requirements to AI systems, including agentic AI; determine the PHI disposition steps required when an AI system is retired, covering training data, model outputs and logs, vendor systems, audit logs, and EU AI Act post-market records; identify which state AI disclosure and consent laws apply to a given patient interaction (including California AB 3030, the Illinois WOPR Act, and Maryland HB 820) and apply the practical disclosure workflow and ambient-documentation guidance; apply HIPAA Breach Notification Rule requirements to an AI-involved privacy incident, including individual, HHS, and media notification thresholds and the business-associate discovery date, alongside parallel AI incident-reporting obligations; apply patient rights (access, amendment, accounting of disclosures, restrictions, and confidential communications) to AI-generated PHI using the four-part practical test, and determine when an AI-generated note qualifies as psychotherapy notes; and evaluate the privacy treatment of PHI sent to public, enterprise-tenanted, and on-premises AI models, and assess whether a de-identification approach adequately addresses AI re-identification risk for model training.
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Prerequisites: None. Recommended after Course 1 (Foundation).
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Estimated time: ~63 minutes.
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Delivery method: Self-paced, online.
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Assessment & passing criteria: A final assessment with a minimum score of 80% required to pass. Missed questions may be retaken up to 4 attempts each.
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Technical requirements: A modern web browser and internet connection. Mobile-responsive and accessible on any device.
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Support: Questions before, during, or after this course can be directed to info at alignmt.ai.
Course 8 — AI Workforce & Integrity
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What you’ll learn: By the end of this course, you’ll be able to describe how agentic AI is reshaping specific healthcare roles (utilization-management nurse, revenue-cycle analyst, and care-management coordinator) and explain where human work concentrates when agents handle routine multi-step tasks; identify the workforce risks that agentic AI creates (steeper deskilling, harder override design, and professional-identity impact) and apply human-factors safeguards such as productive friction, rotation, and psychological safety; recognize conflicts of interest in healthcare AI, including vendor equity, paid advisory roles, and consulting relationships, and identify how the Stark Law, Anti-Kickback Statute, and Sunshine Act apply to AI vendor relationships; apply the five-step COI management framework and the four mitigation techniques (disclosure, recusal, third-party review, and ethical AI guidelines) to a governance-committee or procurement scenario; determine which whistleblower protections apply to a given healthcare worker (False Claims Act qui tam and anti-retaliation provisions, SOX Section 806, OSHA-administered statutes, and state law) and explain why the federal Whistleblower Protection Act and California SB 53 do not cover most healthcare workers; and make an effective AI whistleblower report by documenting specific evidence, leading with patient impact, choosing the appropriate internal or external channel, and protecting yourself after reporting.
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Prerequisites: None. Recommended after Course 1 (Foundation).
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Estimated time: ~60 minutes.
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Delivery method: Self-paced, online.
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Assessment & passing criteria: A final assessment with a minimum score of 80% required to pass. Missed questions may be retaken up to 4 attempts each.
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Technical requirements: A modern web browser and internet connection. Mobile-responsive and accessible on any device.
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Support: Questions before, during, or after this course can be directed to info at alignmt.ai.
Course 9 — AI Governance Policies
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What you’ll learn: By the end of this course, you’ll be able to explain the purpose and value of each policy in an enterprise AI governance policy catalog (for example, enterprise use of AI, data privacy, AI monitoring, vendor, fairness, decommission, and change management) to the governance committee and key stakeholders; design a phased process for implementing AI governance policies, from customization and committee approval through training, pilot testing, phased rollout, feedback, and documentation; assign clear policy ownership and governance responsibilities across clinical, legal, compliance, and IT functions and identify how approvals, refresh cycles, and documentation are tracked; and apply best practices for policy collaboration and regular updates, including review schedules, stakeholder feedback, responses to regulatory and technology change, real-time adjustments, and documented revisions.
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Prerequisites: None. Recommended after Course 2 (Governance Committee Operations).
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Estimated time: ~21 minutes.
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Delivery method: Self-paced, online.
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Assessment & passing criteria: A final assessment with a minimum score of 80% required to pass. Missed questions may be retaken up to 4 attempts each.
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Technical requirements: A modern web browser and internet connection. Mobile-responsive and accessible on any device.
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Support: Questions before, during, or after this course can be directed to info at alignmt.ai.
Course 10 — AI Operations & Readiness
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What you’ll learn: By the end of this course, you’ll be able to define the operations executive’s three AI governance responsibilities (workforce readiness, operational continuity, and responsible scaling) and explain why AI deployment changes the operating model and therefore sits with operations leadership rather than IT or compliance; diagnose why AI adoption fails using the workforce change failure checklist, apply the six change-management practices, and interpret override-rate trends as a leading indicator of trust and automation bias; apply the three human-factors failure modes (automation bias, deskilling, and alert fatigue 2.0) and their countermeasures, and use the Five Rights of Clinical AI as an operational gut-check for a deployment decision; run the daily, weekly, and monthly operational safeguards cadence and determine the correct operational response when a monitoring signal surfaces in the weekly review; and lead the operational first response to an AI incident using the decision tree, activate rehearsed manual fallback protocols, and identify the core and conditional incident command team.
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Prerequisites: None. Recommended after Course 1 (Foundation) and Course 2 (Governance Committee Operations).
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Estimated time: ~15 minutes.
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Delivery method: Self-paced, online.
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Assessment & passing criteria: A final assessment with a minimum score of 80% required to pass. Missed questions may be retaken up to 4 attempts each.
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Technical requirements: A modern web browser and internet connection. Mobile-responsive and accessible on any device.
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Support: Questions before, during, or after this course can be directed to info at alignmt.ai.
Frequently Asked Questions
Who are these courses for?
Healthcare professionals in technical, clinical, operational, and compliance roles who are responsible for governing AI systems in their organizations.
Are there prerequisites?
No. The courses are designed for learners new to AI governance as well as experienced practitioners.
How are the courses delivered?
Fully online, self-paced, and asynchronous through the AlignMT AI Academy. You can start anytime and access courses 24/7 from a modern web browser on a computer or mobile device.
How long does each course take?
Each course has a published completion time on the course details page. For example: Foundation: Enterprise AI Governance Essentials takes about 1 hour; AI Governance Committee Operations takes about 1.2 hours. Your progress is saved, so you can stop and resume at any time.
How do I enroll, and what does it cost?
Create an account at academy.alignmt.ai and select a course or certificate. Prices are shown on each course and certificate page. If your organization has enrolled you, contact your administrator for access.
What do I need to complete a course?
Complete all required modules and score at least 80% on the final assessment. You may attempt each question up to four times and receive feedback on each one. Knowledge checks within the course are required but not graded.
Will I receive a certificate?
Yes. Your certificate is issued automatically when you complete the course and is available anytime under Certificates in the Academy. Certificates and badges are valid for two years.
How do I get a copy of my records?
Use the “How to Request a Copy of Your Records” link at the bottom of any Academy page. Requests are fulfilled within 5 business days.
How do I get help?
Email academy at alignmt.ai or use Ask a Teammate inside any course. We respond within 1 business day.
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