AI Governance for Health in Africa

AI governance

AI Governance for Health in Africa

  • Pacfaie Courses
Course menu
Module 1: AI fundamentals for health professionals
  • Welcome: AI governance in health and how this course works
  • What AI is (and isn’t) in health care
  • How a diagnostic AI learns
  • The AI lifecycle in a health organisation
  • Generative and agentic AI in health
  • How AI fails in health care
  • Measuring performance: sensitivity, specificity and why accuracy misleads
  • The Africa lens and ten questions to ask
  • Module 1 quiz
Module 2: AI across African health systems
  • Where AI sits across the health system
  • Diagnostic and imaging AI
  • Community health and primary care
  • Public health, operations and health financing
  • Donors, partners and pilots: who is accountable?
  • Mapping opportunity and harm
  • Module 2 quiz
Module 3: Ethics, equity and patient rights
  • Ethics principles for AI in health
  • Health equity and bias
  • Measuring equity in health AI
  • Telling patients and respecting autonomy
  • Responsibility when AI contributes to harm
  • Vulnerable patients and sensitive services
  • Module 3 quiz
Module 4: Health AI regulation in Africa
  • The regulatory landscape for health AI in Africa
  • Software as a medical device
  • Health data, health laws and AI
  • Research ethics and AI
  • International guidance and mapping obligations
  • Assignment 1: A regulatory and ethical obligations map
  • Module 4 quiz
Module 5: Building an AI governance framework for health organisations
  • What an AI governance framework is for in health
  • Roles, committees and clinical ownership
  • The AI inventory
  • Risk tiering for health AI
  • Proportionate controls for each tier
  • Governance for smaller facilities and at national level
  • Module 5 quiz
Module 6: Health data governance, privacy and sharing
  • Health data quality and representativeness
  • Lawful basis, consent and transparency
  • De-identification and privacy techniques
  • Data sharing, sovereignty and benefit sharing
  • Cloud, cross-border data and security
  • Data protection impact assessments for health AI
  • Module 6 quiz
Module 7: Clinical validation, safety and performance
  • Levels of evidence for health AI
  • Reading validation evidence critically
  • Local validation and silent mode
  • Clinical safety: hazards and safety cases
  • Equity in validation
  • Performance after deployment
  • Assignment 2: Appraising evidence and planning local validation
  • Module 7 quiz
Module 8: AI in clinical care and human oversight
  • Clinical decision support in the workflow
  • Automation bias, alert fatigue and deskilling
  • Designing meaningful human oversight
  • Task-shifting with AI
  • Professional responsibility and documentation
  • Training staff to use clinical AI
  • Module 8 quiz
Module 9: Generative AI, digital health and community care
  • Generative AI in health: uses and risks
  • Patient-facing chatbots and symptom checkers
  • AI scribes and clinical documentation
  • Local languages, literacy and voice
  • Community health and mobile platforms
  • Misinformation and security
  • Module 9 quiz
Module 10: AI policy, procurement and shadow AI
  • Putting up an organisational AI policy
  • Drafting, approving and rolling out the policy
  • Acquiring AI: purchases, donations and partnerships
  • Agreements with vendors, donors and research partners
  • Shadow AI in health organisations
  • Responding to shadow AI
  • Generative AI rules for clinical staff
  • Assignment 3: AI policy, procurement and shadow AI
  • Module 10 quiz
Module 11: Monitoring, evaluation and compliance assurance
  • Monitoring health AI in use
  • Drift and AI incidents
  • Evaluating whether AI improved health
  • Compliance assurance
  • Layers of assurance
  • Reporting, readiness and improvement
  • Assignment 4: A monitoring and assurance plan
  • Module 11 quiz
Module 12: Final exam and capstone
  • Putting it all together: a 12-month roadmap
  • Final exam
  • Capstone: an AI governance programme for Bonde County Health Department
  • Where to go from here
Length
About 45 hours, self-paced
Lessons
87
Language
English

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Across Africa, AI now reads chest X-rays for TB, helps community health workers decide who needs referral, answers patients’ health questions on WhatsApp, and processes medical insurance claims. Done well, it can extend scarce clinical expertise to millions of people who rarely see a doctor. Done badly, it can miss disease, give unsafe advice, widen health inequities and expose the most sensitive data people have. This course trains health professionals to govern AI so it does the first and not the second.

Who it’s for

Hospital and health system managers, clinicians and nurses in digital health roles, health informaticians, quality and patient safety leads, Ministry of Health and county or state health officials, medicines and medical device regulators, research ethics committee members, digital health start-ups, NGO and donor programme staff, and health insurers and medical schemes. No technical background or coding is needed: everything is built up from first principles, with health examples throughout.

What you’ll be able to do

  • Explain how machine learning, generative AI and AI agents work in health care, and how they fail.
  • Map where AI is used across African health systems, from hospitals to community health.
  • Apply ethics, equity and patient rights to AI in clinical care and public health.
  • Identify the medical device, health, data protection and research ethics rules that apply in African countries, and international guidance such as WHO’s.
  • Design an AI governance framework for a health organisation, linked to clinical governance and patient safety.
  • Judge whether an AI tool has been properly validated for your patients, and keep clinicians meaningfully in control.
  • Write an organisational AI policy, procure AI safely (including donated tools), and bring shadow AI under control.
  • Monitor and evaluate AI in use and provide internal compliance assurance.

How it works

Twelve modules, about 45 hours in total, including assignments and the capstone, at your own pace. Each module mixes short readings with interactive practice set in fictional African hospitals, clinics, programmes and digital health services. Practice is never marked.

Assessment: a quiz after each module, four applied assignments marked against a published rubric, a timed final exam, and a capstone in which you design AI governance for a fictional African health organisation, with peer review and assessor marking.

Certificate

Complete every step to earn the Certificate in AI Governance for Health, a verifiable digital certificate and Open Badge. Your capstone result (pass, merit or distinction) is shown on it.

Certificate and badge

Completed twelve modules covering AI fundamentals, AI across African health systems, ethics, equity and patient rights, health AI regulation in Africa, governance frameworks, health data governance, clinical validation, AI in clinical care and human oversight, generative AI and community care, AI policy, procurement and shadow AI, and monitoring, evaluation and compliance assurance; passed every module quiz, four rubric-marked applied assignments and a timed final exam; and completed an assessor-marked capstone.

Instructor

  • Pacfaie Courses
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