AI Governance for Healthcare Training Course
AI Governance for Healthcare Training Course equips healthcare leaders, technology professionals, regulators, and data practitioners with the knowledge and tools required to design, implement, and manage responsible AI systems aligned with global healthcare standards, regulatory requirements, and emerging AI policies
Course Overview
AI Governance for Healthcare Training Course
Introduction
Artificial Intelligence (AI) is transforming healthcare through clinical decision support, predictive analytics, medical imaging, personalized medicine, digital health platforms, and automated patient services. However, the rapid adoption of AI in healthcare requires robust AI Governance frameworks to ensure safety, transparency, accountability, privacy, and ethical innovation. AI Governance for Healthcare Training Course equips healthcare leaders, technology professionals, regulators, and data practitioners with the knowledge and tools required to design, implement, and manage responsible AI systems aligned with global healthcare standards, regulatory requirements, and emerging AI policies.
The course explores critical areas including responsible AI, healthcare AI risk management, algorithmic transparency, data governance, model lifecycle management, AI ethics, cybersecurity, compliance, and human-centered AI design. Participants will learn how to establish governance structures that support trustworthy AI adoption while protecting patient rights, improving clinical outcomes, and enabling sustainable digital transformation. Through practical case studies, governance frameworks, and real-world healthcare scenarios, learners will develop the capabilities needed to manage AI-driven healthcare ecosystems effectively.
Course Duration
5 days
Course Objectives
By the end of this course, participants will be able to:
- Understand the principles of AI Governance, Responsible AI, and ethical healthcare innovation.
- Develop healthcare-focused AI governance frameworks and operating models.
- Implement AI risk management and regulatory compliance strategies.
- Apply data governance, privacy, and security principles for healthcare AI systems.
- Evaluate AI models for fairness, transparency, explainability, and accountability.
- Establish AI lifecycle management and monitoring processes.
- Understand emerging AI regulations, standards, and healthcare policies.
- Design governance approaches for clinical AI deployment and decision-support systems.
- Manage algorithmic bias detection and mitigation strategies.
- Implement AI cybersecurity and threat management practices.
- Create effective AI governance committees and oversight structures.
- Apply global best practices for trustworthy and human-centered AI adoption.
- Develop strategies for scalable, sustainable, and compliant healthcare AI transformation.
Target Audience
- Healthcare executives and hospital administrators
- Chief Medical Officers and clinical leaders
- Healthcare IT managers and digital transformation teams
- Data scientists and AI engineers in healthcare organizations
- Health informatics professionals
- Compliance, legal, and regulatory professionals
- Government health agencies and policymakers
- Medical researchers and healthcare innovation teams
Course Modules
Module 1: Foundations of AI Governance in Healthcare
- Introduction to AI transformation in healthcare ecosystems
- Principles of responsible and trustworthy AI
- Healthcare AI governance frameworks and models
- Roles and responsibilities in AI oversight
- Global trends shaping healthcare AI governance
- Case Study: Implementation of an AI governance framework in a large hospital network to manage clinical AI applications.
Module 2: Healthcare AI Ethics and Responsible Innovation
- Ethical principles for healthcare AI deployment
- Patient rights and human-centered AI design
- Transparency and explainability requirements
- Managing ethical risks in automated decisions
- Building trust between clinicians, patients, and AI systems
- Case Study: Evaluation of an AI diagnostic tool to ensure ethical use and clinician accountability.
Module 3: Healthcare Data Governance and Privacy Management
- Healthcare data governance frameworks
- Patient data protection and privacy regulations
- Data quality management for AI systems
- Consent management and data sharing practices
- Secure healthcare data environments
- Case Study: Governance of patient data used for AI-powered disease prediction models.
Module 4: AI Risk Management and Regulatory Compliance
- Identifying healthcare AI risks
- AI impact assessments and risk classification
- Regulatory requirements for medical AI systems
- Compliance monitoring and auditing
- AI governance documentation practices
- Case Study: Managing regulatory compliance for an AI-enabled medical imaging solution.
Module 5: AI Model Lifecycle Governance
- AI model development and approval processes
- Model validation and performance monitoring
- Version control and model documentation
- Continuous improvement and retraining governance
- Managing AI model retirement processes
- Case Study: Governance lifecycle management of an AI clinical decision-support platform.
Module 6: AI Bias, Fairness, and Clinical Safety
- Understanding algorithmic bias in healthcare
- Fairness evaluation methodologies
- Bias detection and mitigation techniques
- Clinical safety assessment for AI tools
- Ensuring equitable healthcare outcomes
- Case Study: Reviewing bias risks in an AI system used for patient risk scoring.
Module 7: AI Cybersecurity and Healthcare Technology Protection
- Cybersecurity risks affecting healthcare AI
- AI system vulnerability management
- Protecting medical AI infrastructure
- Secure AI deployment practices
- Incident response and AI threat monitoring
- Case Study: Protecting an AI-enabled hospital platform from cybersecurity threats.
Module 8: Building Enterprise AI Governance Programs
- Designing healthcare AI governance operating models
- Establishing AI governance committees
- Creating AI policies and standards
- Measuring AI governance maturity
- Scaling AI adoption across healthcare organizations
- Case Study: Developing an enterprise AI governance strategy for a national healthcare provider.
Training Methodology
- Interactive lectures and presentations.
- Group discussions and brainstorming sessions.
- Hands-on exercises using real-world datasets.
- Role-playing and scenario-based simulations.
- Analysis of case studies to bridge theory and practice.
- Peer-to-peer learning and networking.
- Expert-led Q&A sessions.
- Continuous feedback and personalized guidance.
Register as a group from 3 participants for a Discount
Send us an email: info@datastatresearch.org or call +254724527104
Certification
Upon successful completion of this training, participants will be issued with a globally- recognized certificate.
Tailor-Made Course
We also offer tailor-made courses based on your needs.
Key Notes
a. The participant must be conversant with English.
b. Upon completion of training the participant will be issued with an Authorized Training Certificate
c. Course duration is flexible and the contents can be modified to fit any number of days.
d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.
e. One-year post-training support Consultation and Coaching provided after the course.
f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you