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GAICC Healthcare AI Governance Professional (GAICC-HAIGP)

Govern AI across healthcare and life sciences, from regulatory qualification and clinical safety to validation, post-market monitoring and agentic AI workflows.
Last Updated: 2026
4 Days (32 Contact Hours)
English
Certification Trusted by Professionals at:

AI Certification Council

4.8 / 5.0 Rating
27,143+

Students

Our training programs are CPD Accredited by CPDSO

GAICC Healthcare AI Governance Professional

  • Duration: 2 hours
  • 80 scenario-based MCQs (70 scored + 10 unscored pilot)
  • Single best answer from four options · No negative marking
  • Delivery: GAICC Hub, online with AI-assisted proctoring, available 24/7
  • Results: Provisional pass/fail at the end of the exam; official scaled score and domain feedback after GAICC quality checks
  • Eligibility: 32 contact hours of HAIGP training. No work experience or prior credential required.
  • Conditions: Closed book; no notes, reference material or AI tools
  • Scaled Score: 100–500 (350 passing)
  • Passing score: 70% of scored items
  • Validity: 3 years (60 CPD hours per cycle)
  • Standard: Scheme designed to align with ISO/IEC 17024:2012
  • Retake: One complimentary retake within your 12-month eligibility period. Further attempts need a new registration and exam fee.

Exam Content Outline (ECO)

Download the GAICC-HAIGP Exam Content Outline for every domain, task and knowledge statement, plus the reference frameworks and sample questions. The Candidate Handbook covers booking, exam-day rules, fees and renewal.
Download
Candidate Handbook

Career Opportunities

The GAICC-HAIGP prepares you for senior roles where AI meets clinical care, regulation and patient safety.
  • Head of Clinical AI Governance: Lead the AI committee, inventory and approval gates across a health system, and report AI risk to the board.
  • Clinical Safety Officer (Digital and AI): Own clinical safety cases and hazard logs for AI tools, from ambient scribes to early-warning models.
  • AI Regulatory Affairs Manager (SaMD): Qualify AI software as a medical device, plan FDA, EU, UK and APAC pathways, and manage PCCPs and dual MDR/AI Act conformity.
  • Responsible AI Lead, Healthcare and Life Sciences: Embed bias testing, patient transparency and ethical review in AI used for care, coverage and drug development.
  • Health AI Assurance Consultant: Challenge vendor evidence, design validation and output audits, and help clients build ISO/IEC 42001 programmes.
  • Payer AI Compliance Lead: Make sure AI-assisted utilisation decisions rest on individual circumstances and meet CMS, Section 1557 and state-law duties.
Download Course Curriculum

GAICC-HAIGP Certification

Issued By Global AI Certification Council

Prepare for your HAIGP Certification with this course

GAICC-HAIGP Certification

The GAICC HAIGP programme gives equal weight to AI medical devices and to non-device AI: ambient scribes, patient messaging, payer utilisation tools, population health models and drug development AI. It also covers how a tool can drift into device regulation as its functions change. It is built for hospitals, payers, medtech and pharma, and for the technology and services firms that build and run AI for them.

The course runs over four days and 32 contact hours, in 16 modules plus a capstone across 8 domains. It follows the health AI lifecycle: accountability and regulatory pathways; clinical risk and health data; equity, transparency and evidence; and lifecycle, agents and scale. Every module is built around realistic cases, and learners leave with a 12-template working toolkit.

Enroll Now

Practical Outcomes You'll Achieve

By the end of this course, you will be able to:

  • Classify health AI use cases and determine regulatory status across major device and AI regimes.
  • Plan evidence for AI medical devices, including PCCPs and dual MDR/IVDR and EU AI Act conformity.
  • Build clinical safety cases, hazard logs and impact assessments proportionate to clinical risk.
  • Govern health data use for AI under HIPAA, GDPR, the European Health Data Space and NZ privacy rules.
  • Test health AI for subgroup bias and design patient transparency that meets disclosure laws.
  • Specify validation and output audits for predictive models, ambient scribes and generative AI.
  • Run post-market monitoring, change control and incident reporting across jurisdictions.
  • Set autonomy limits, human checkpoints and contract terms for agentic and vendor-supplied AI.

Who Is This Certification For?

Designed for professionals who govern AI in healthcare and life sciences

  • Health system AI and digital governance leads, clinical safety officers and clinical informatics leaders.
  • Medtech regulatory affairs and quality professionals bringing AI-enabled devices to market.
  • Payer utilisation management and compliance leaders overseeing AI in coverage decisions.
  • Pharma and life sciences AI leads working across trials, pharmacovigilance and manufacturing.
  • Privacy, information governance and security officers handling health data used by AI.
  • Consultants, architects, procurement, legal and risk professionals who build, buy or assure health AI.
Enroll Now

Course Modules

Day 1

Accountability and Regulatory Pathways (8 Hours)

Module 1
The Healthcare AI Landscape and Accountability
  • Health AI use case families and intended purpose
  • Patient exposure pathways and decision impact
  • Provider, deployer and manufacturer roles
  • Clinician duty of care when acting on AI outputs
  • Payer accountability for AI-assisted decisions
  • Services providers in the AI value chain

Workshop Exercise

Sort a regional health system's register of 20 AI tools, including sepsis alerts, scribes, coding bots, imaging triage and a patient chatbot, into categories and map the accountable party for each.

  • AI committee terms of reference and membership
  • Clinical safety officer, CMIO and CNIO roles
  • AI inventory fields and shadow AI discovery
  • ISO/IEC 42001 clauses and Annex A controls
  • Integration with ISO 13485, ISO/IEC 27001 and clinical governance
  • Joint Commission and CHAI Responsible Use of AI in Healthcare elements

Workshop Exercise

Draft terms of reference and a RACI for a hospital AI committee, then scope an ISO/IEC 42001 statement of applicability covering the hospital and the services provider that runs its AI platform.

  • GAICC Differentiator: One module covers device qualification and pathways across the US, EU, UK, Australia, Singapore, Malaysia and India, including dual conformity under MDR/IVDR and the EU AI Act.
  • Software device qualification and MDR Rule 11
  • FDA clinical decision support criteria (January 2026)
  • FDA submission content and PCCP components
  • Dual conformity under MDR/IVDR and the AI Act
  • MHRA, TGA, HSA, MDA and CDSCO pathways
  • MHRA AI Airlock and regulatory sandboxes

Workshop Exercise

For an AI chest radiograph triage product, determine qualification and classification in five jurisdictions and design a PCCP for its two-year retraining roadmap.

Day 2

Clinical Risk and Health Data (8 Hours)

Module 5
Device Risk Management for Machine Learning
  • ISO 14971 process and benefit-risk determination
  • Machine learning hazards in ISO/TS 24971-2
  • Limits for LLM-based devices
  • Risk tiering criteria and the IMDRF significance matrix
  • FDA autonomy versus consequence grid

Workshop Exercise:

Build an ISO 14971 risk analysis for a sepsis early-warning model, including data, bias and drift hazards, and tier six other use cases to set proportionate controls.

  • DCB0129 and DCB0160 duties and the manufacturer-to-deployer handover
  • Hazard identification: SWIFT, FMEA and bow-tie analysis
  • ISO/IEC 42005 impact and ISO/IEC 23894 risk assessments
  • Automation bias, alert fatigue and deskilling
  • Usability engineering under IEC 62366-1

Workshop Exercise

Produce a DCB0160 hazard log and safety case summary for an ambient scribe roll-out across emergency and outpatient settings, then redesign an alert interface to reduce automation bias.

  • HIPAA business associate agreements and minimum necessary
  • GDPR Article 9 conditions and processor terms
  • NZ Health Information Privacy Code 2020 and Biometric Processing Privacy Code 2025
  • UK national data opt-out and Caldicott principles
  • EHDS data permits, secure processing environments and research ethics

Workshop Exercise:

Review a data flow in which a vendor fine-tunes a model on hospital EHR data, and identify the HIPAA, GDPR and EHDS requirements and the contract terms needed.

  • Safe Harbor and Expert Determination de-identification
  • Re-identification risk from free text, imaging and genomics
  • Synthetic data testing and model memorisation
  • Scribe consent, retention and opt-out
  • Cross-border transfers and offshore delivery access

Workshop Exercise

Design governance for a population health risk model with iwi partners, then design consent and opt-out for a scribe used in mental health clinics.

Day 3

Equity, Transparency and Evidence (8 Hours)

Module 9
Health Equity, Bias and Proxy Discrimination
  • Subgroup metrics and calibration
  • Representativeness and label bias in clinical data
  • Accent and language bias in speech AI
  • Cost as a proxy for need
  • ACA Section 1557 and CMS individual-circumstances requirements

Workshop Exercise

Analyse subgroup results from a deterioration model and a payer care-management algorithm, identify proxy discrimination, and set remediation thresholds and actions.

  • California AB 3030 and Texas disclosure duties
  • EU AI Act Article 50 and Colorado notice and human review rights
  • WHO guidance on large multi-modal models
  • Informed consent and limits on AI-delivered therapy
  • Explanations, uncertainty and specific denial reasons
  • IMDRF Good Machine Learning Practice (N88) and the N93 draft
  • Data independence and leakage
  • External, temporal and silent-mode validation
  • TRIPOD+AI, CONSORT-AI and DECIDE-AI
  • Documentation error taxonomies and stratified sampling for output audits
  • CHAI Applied Model Card and evidence pack contents
  • Signs that vendor evidence won't transfer locally
  • Independent testing and red teaming
  • IEC 62304 Edition 2, IEC 82304-1 and IEC 81001-5-1
  • ISO/IEC 42006 audits and RUAIH certification

Day 4

Lifecycle, Agents and Scale (8 Hours)

Module 13
Monitoring, Change Control and Incident Response
  • Drift types, monitoring metrics and action thresholds
  • PCCPs, algorithm change protocols and significant change
  • UK, FDA and EU vigilance timelines
  • AI safety event investigation and learning
  • FDA section 524B, SBOMs, decommissioning and clinical fallback

Workshop Exercise

Investigate a sepsis model whose alert rate shifted after an EHR upgrade and a vendor model update, decide whether the change is significant, and prepare the UK, US and EU reports.

GAICC Differentiator

Hands-on governance for agents that act in clinical and payer workflows, covering autonomy limits, kill switches and red teaming against prompt injection hidden in real documents.

  • NHS England and Health NZ scribe guidance
  • Autonomy levels and approval gates
  • Tool permissions, logging and kill switches
  • Prompt injection via EHR text
  • Foundation model dependencies and EU AI Act GPAI obligations

Workshop Exercise

Design autonomy limits, human checkpoints and audit logging for a prior authorisation agent and a care navigation agent, then red team them with prompt injection planted in referral letters.

  • Due diligence questionnaires, NHS DTAC and NICE evidence standards
  • Liability, audit rights and change-notice clauses
  • EU Product Liability Directive and contracts
  • AI Act Article 25: when a supplier becomes a provider
  • Offshore delivery, processor duties and federated governance
  • Capstone: act as governance lead for a multi-site health system rolling out an ambient scribe and an agentic referral assistant, both built by a services provider on a third-party foundation model
  • Produce a risk-tiered approval pack covering regulatory status, safety case, privacy, equity, validation, monitoring and contract terms
  • Defend it before a mock AI committee
  • Exam briefing: ECO walkthrough and timed scenario practice
  • Deliverable: Every participant leaves with a 12-template toolkit, including:
  • AI inventory and risk-tiering template
  • Regulatory qualification decision tree
  • Clinical safety case and hazard log template
  • Scribe output audit protocol
  • Vendor due diligence questionnaire and contract clause library
  • Agentic AI checkpoint register
  • Post-market monitoring plan

Exam Domain Weighting

80 scenario-based questions across 8 domains

Most questions present a realistic sector scenario and ask for the most defensible action. Recall of clause numbers is not tested.

Domain Weight Scored Items
I. Healthcare AI Landscape and Accountability
11%
~8
II. Health AI Regulation and Medical Device Pathways
15%
~11
III. Clinical Risk, Safety and Impact Assessment
15%
~11
IV. Health Data, Privacy and Consent
12%
~8
V. Health Equity, Ethics and Patient Transparency
10%
~7
VI. Clinical Validation, Evaluation and Assurance
13%
~9
VII. Post-Market Monitoring, Change Control and Incident Response
12%
~8
VIII. Generative and Agentic AI, Supply and Scale
12%
~8
Total 100% 70 scored + 10 pilot = 80
4.8 / 5.0 Rating

Trusted by Professionals Across AI, GRC & Legal Industries

Michael Turner

⭐⭐⭐⭐⭐

The course helped me understand AI safety, engineering accountability, and regulatory requirements. The practical examples were useful and gave me greater confidence in evaluating risks across complex engineering systems.

Ananya Rao

⭐⭐⭐⭐⭐

I found the programme informative and practical. The sessions on model validation and lifecycle management provided valuable insights into building safer, more reliable AI enabled engineering solutions.

sansa Brooks

⭐⭐⭐⭐⭐

The certification offered a clear perspective on responsible AI adoption. I particularly enjoyed learning about functional safety, human oversight, and managing emerging risks in modern engineering applications.

Sofia Martinez

⭐⭐⭐⭐⭐

The course connected technical engineering concepts with governance responsibilities effectively. Its coverage of product liability, verification, and validation helped me better understand the challenges of deploying trustworthy AI systems.

Vikram Nair

⭐⭐⭐⭐⭐

I appreciated the structured learning approach and practical scenarios. The programme strengthened my understanding of AI risk assessment, operational resilience, and responsible decision making in engineering environments.

Chloe Anderson

⭐⭐⭐⭐⭐

This programme provided useful knowledge about AI governance and engineering safety. The capstone exercise brought key concepts together and helped me understand how to support accountable AI implementation.

This Course Includes: 32 Total Learning Hours

Healthcare AI Governance Professional

Issued by Global AI Certification Council

What's included?
  • 4 Days · 16 Modules + Capstone
  • 32 CPD Hours on Completion
  • Certificate of Completion
  • Full Timed Mock Exam (80 questions, domain feedback)
  • 1 Exam Voucher Included
  • Digital Certificate & Badge
  • 12-Template Healthcare AI Governance Toolkit

Member Price

US$898

Full Price

US$1075

Book Your Seat Now

US$99 is included in the above member price.

Certification Renewal (Members / 3-yr) – $69

Certification Renewal (Non-Members / 3-yr) – $119

Have Questions?

Frequently asked question.

The Healthcare AI Governance Professional is a senior, sector-specialist credential. It confirms that you can govern AI across healthcare and life sciences, from regulatory qualification and clinical safety to validation, monitoring and agentic workflows.
No. The only eligibility requirement is 32 contact hours of HAIGP training completed before the exam. Five years in healthcare, life sciences, health IT or a related role is recommended but never used to refuse a candidate. CPAIG, or equivalent foundational knowledge, is recommended but not required.

The course runs over four days and 32 contact hours:

  • Day 1: Accountability and Regulatory Pathways
  • Day 2: Clinical Risk and Health Data
  • Day 3: Equity, Transparency and Evidence
  • Day 4: Lifecycle, Agents and Scale, ending in a capstone

It is available in person, live online, or self-paced.

The exam has 80 scenario-based multiple-choice questions (70 scored + 10 unscored pilot) in 120 minutes. It is closed book and delivered online with AI-assisted proctoring, 24/7. The pass mark is 70%, a scaled 350 on a 100–500 scale, and there is no negative marking.
No. AI medical devices are a major part of the course, but non-device AI gets equal weight: ambient scribes, patient messaging, payer utilisation management, population health models and drug development AI.
No. Clinical concepts such as safety cases, automation bias and validation design are taught from first principles for governance, regulatory, privacy, technology and clinical professionals alike.
The primary reference regimes are the US, EU and UK. Australia, Singapore, Malaysia, India and New Zealand are covered where they differ. You must recognise which regime applies and what it requires, not recall every local rule.
The certification is valid for 3 years. Renewal requires 60 CPD hours per cycle (Technology 27, Humanity 12, Strategy 21), reaffirming the Code of Ethics and paying the renewal fee. A three-month grace period applies after expiry.
certify(at)gaicc(dot)org · support(at)gaicc(dot)org · www(dot)gaicc(dot)org
Instructor

Dr Faiz Rasool

Director at the Global AI Certification Council (GAICC) and PM Training School

Linkedin-in

A globally certified instructor in ISO/IEC, PMI®, TOGAF®, SAFe®, and Scrum.org disciplines. With over three years’ hands-on experience in ISO/IEC 42001 AI governance, he delivers training and consulting across New Zealand, Australia, Malaysia, the Philippines, and the UAE, combining high-end credentials with practical, real-world expertise and global reach.

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Start Your ' Healthcare AI Governance Professional (GAICC-HAIGP) ' Today

4.8 / 5.0 Rating
  • Govern AI across device, AI, privacy and coverage regulation
  • Lead clinical AI governance in hospitals, payers, medtech and pharma
  • Leave with a ready-to-use healthcare AI governance toolkit

Self-Paced Course (Certification Exam included)

Member Price

US$898

Full Price

US$1075

GAICC Annual Membership US$99 is included in the above member price.

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