Format: Live instructor-led online training via Zoom / Microsoft Teams

AI Governance in Healthcare Training Course

Artificial Intelligence (AI) is rapidly transforming healthcare by enhancing clinical decision-making, medical imaging, diagnostics, predictive analytics, patient engagement, healthcare operations, and biomedical research. However, the successful adoption of AI requires robust governance frameworks that ensure ethical, secure, transparent, and accountable use of AI technologies. The AI Governance in Healthcare Training Course equips healthcare leaders, policymakers, clinicians, AI practitioners, health informatics professionals, compliance officers, and technology managers with the knowledge and practical skills required to establish effective AI governance frameworks. The course covers high-demand topics including Responsible AI, AI Governance, AI Risk Management, AI Ethics, Explainable AI (XAI), Healthcare Data Governance, Regulatory Compliance, AI Audit, Clinical Decision Support Systems (CDSS), Machine Learning Governance, Generative AI Governance, Healthcare Cybersecurity, Data Privacy, AI Policy Development, Digital Health Governance, Electronic Health Records (EHR), AI Lifecycle Management, and Healthcare Digital Transformation, enabling organizations to deploy trustworthy AI systems that improve healthcare outcomes while minimizing operational, ethical, and legal risks.

Participants will gain practical knowledge in developing AI governance strategies, establishing AI oversight committees, implementing governance policies, managing AI risks, validating machine learning models, monitoring AI performance, ensuring transparency, mitigating algorithmic bias, protecting patient privacy, and integrating governance throughout the AI lifecycle. The course emphasizes internationally recognized governance principles, healthcare regulations, clinical safety requirements, cybersecurity standards, interoperability frameworks, data stewardship, and continuous AI monitoring. Participants will also explore governance strategies for Generative AI, foundation models, autonomous clinical systems, predictive analytics, intelligent medical imaging, and AI-enabled healthcare automation.

Healthcare organizations worldwide are increasingly expected to demonstrate responsible AI implementation that balances innovation with patient safety, regulatory compliance, and public trust. This course provides practical methodologies for AI governance maturity assessment, policy development, regulatory readiness, ethical review processes, procurement governance, vendor management, AI assurance, model documentation, and performance evaluation. Participants will learn how to establish governance structures that support responsible innovation while ensuring fairness, explainability, accountability, security, resilience, and compliance with national and international healthcare regulations.

The training combines instructor-led presentations, governance workshops, policy development exercises, AI risk assessments, compliance simulations, healthcare governance frameworks, collaborative discussions, and comprehensive case studies from hospitals, ministries of health, pharmaceutical companies, insurance providers, academic medical centers, humanitarian organizations, digital health innovators, and regulatory authorities. Upon successful completion, participants will possess the leadership, governance, technical, and strategic competencies required to oversee AI implementation, strengthen institutional governance, promote ethical AI adoption, and guide sustainable digital transformation initiatives across healthcare organizations.

Course Objectives

1. Understand AI governance principles in healthcare.
2. Develop comprehensive AI governance frameworks.
3. Implement responsible AI policies and organizational standards.
4. Establish AI risk management and oversight mechanisms.
5. Strengthen AI ethics, transparency, fairness, and accountability.
6. Ensure compliance with healthcare regulations and AI standards.
7. Evaluate AI model performance, explainability, and reliability.
8. Protect healthcare data privacy, security, and confidentiality.
9. Integrate governance throughout the AI system lifecycle.
10. Lead organizational AI governance and digital transformation initiatives.

Organizational Benefits

1. Improve trust and transparency in AI-enabled healthcare services.
2. Reduce operational, ethical, and regulatory risks associated with AI.
3. Strengthen patient safety through responsible AI implementation.
4. Improve compliance with healthcare laws and international AI standards.
5. Enhance healthcare cybersecurity and data protection.
6. Support effective AI procurement and vendor management.
7. Improve organizational accountability and governance structures.
8. Increase stakeholder confidence in AI-enabled healthcare systems.
9. Promote sustainable healthcare innovation and digital transformation.
10. Build institutional capacity for responsible AI adoption.

Target Participants

• Hospital Executives
• Healthcare Administrators
• Medical Directors
• Medical Doctors
• Nurses
• Health Informatics Specialists
• Artificial Intelligence Engineers
• Machine Learning Engineers
• Data Scientists
• Healthcare Data Analysts
• Compliance Officers
• Risk Management Professionals
• Information Security Managers
• Cybersecurity Specialists
• Clinical Researchers
• Biomedical Engineers
• Digital Health Specialists
• Policy Makers
• Healthcare Regulators
• Legal and Governance Officers
• Pharmaceutical Professionals
• Healthcare Consultants
• NGO Health Program Managers
• University Researchers
• Technology Innovation Managers

Course Outline

Module 1: Foundations of AI Governance in Healthcare

• Introduction to AI governance
• AI governance principles
• Responsible AI concepts
• Healthcare AI ecosystem
• AI governance maturity models
• Case Study: Establishing AI governance in a national hospital

Module 2: AI Strategy and Governance Frameworks

• AI governance structures
• AI policy development
• Organizational governance models
• AI oversight committees
• Strategic AI planning
• Case Study: Enterprise AI governance framework implementation

Module 3: AI Risk Management

• AI risk identification
• Clinical AI risk assessment
• Risk mitigation strategies
• AI incident management
• Continuous risk monitoring
• Case Study: Managing AI risks in clinical decision support systems

Module 4: AI Ethics and Responsible AI

• Ethical AI principles
• Algorithmic fairness
• Bias detection and mitigation
• Explainable AI (XAI)
• Human-centered AI
• Case Study: Ethical review of AI-assisted diagnostics

Module 5: Healthcare Data Governance

• Healthcare data governance frameworks
• Data stewardship
• Data quality management
• Data lifecycle management
• Healthcare interoperability
• Case Study: Strengthening healthcare data governance for AI systems

Module 6: Privacy, Security and Regulatory Compliance

• Healthcare data privacy
• AI cybersecurity
• Regulatory compliance
• Patient consent management
• Secure AI deployment
• Case Study: AI compliance assessment in healthcare organizations

Module 7: AI Lifecycle Governance

• AI system development governance
• Model validation
• Model documentation
• Performance monitoring
• AI change management
• Case Study: Governing machine learning model deployment

Module 8: Governance of Generative AI

• Large Language Models governance
• Prompt governance
• Hallucination management
• Foundation model oversight
• Responsible Generative AI deployment
• Case Study: Governance of AI clinical documentation systems

Module 9: AI Procurement and Vendor Management

• AI procurement strategies
• Vendor evaluation
• Third-party AI risk management
• Contract governance
• AI service monitoring
• Case Study: Procuring AI-powered healthcare platforms

Module 10: AI Audit, Assurance and Performance Evaluation

• AI audit frameworks
• Governance performance indicators
• AI assurance methodologies
• Compliance auditing
• Continuous governance improvement
• Case Study: Conducting an AI governance audit in a hospital

Module 11: Leadership and Organizational Change Management

• AI leadership strategies
• Organizational transformation
• Stakeholder engagement
• Workforce readiness
• AI governance culture
• Case Study: Leading AI adoption across healthcare institutions

Module 12: Future Trends in AI Governance

• Autonomous healthcare systems
• International AI regulations
• Digital health governance
• Future AI governance frameworks
• Sustainable AI innovation
• Case Study: Governance roadmap for intelligent healthcare ecosystems

General Information

1. Customized Training: All our courses can be tailored to meet the specific needs of participants.
2. Language Proficiency: Participants should have a good command of the English language.
3. Comprehensive Learning: Our training includes well-structured presentations, practical exercises, web-based tutorials, and collaborative group work. Our facilitators are seasoned experts with over a decade of experience.
4. Certification: Upon successful completion of training, participants will receive a certificate from Foscore Development Center (FDC-K).
5. Training Locations: Training sessions are conducted at Foscore Development Center (FDC-K) centers. We also offer options for in-house and online training, customized to the client's schedule.
6. Flexible Duration: Course durations are adaptable, and content can be adjusted to fit the required number of days.
7. Onsite Training Inclusions: The course fee for onsite training covers facilitation, training materials, two coffee breaks, a buffet lunch, and a Certificate of Successful Completion. Participants are responsible for their travel expenses, airport transfers, visa applications, dinners, health/accident insurance, and personal expenses.
8. Additional Services: Accommodation, pickup services, freight booking, and visa processing arrangements are available upon request at discounted rates.
9. Equipment: Tablets and laptops can be provided to participants at an additional cost.
10. Post-Training Support: We offer one year of free consultation and coaching after the course.
11. Group Discounts: Register as a group of more than two and enjoy a discount ranging from 10% to 50%.
12. Payment Terms: Payment should be made before the commencement of the training or as mutually agreed upon, to the Foscore Development Center account. This ensures better preparation for your training.
13. Contact Us: For any inquiries, please reach out to us at training@fdc-k.org or call +254712260031.
14. Website: Visit www.fdc-k.org for more information.