AI Ethics and Governance in Healthcare

Aim

This course provides a practical framework to deploy AI in healthcare responsibly. Participants learn how to manage privacy, bias, transparency, patient safety, and regulatory expectations while setting up governance processes that support compliant, auditable, and trustworthy AI systems.

Who This Course Is For

• Healthcare administrators, hospital leadership, and quality teams
• Clinicians and clinical informatics professionals evaluating AI tools
• Compliance, legal, risk, and data privacy teams
• HealthTech product managers and AI/data teams building healthcare solutions
• Researchers and students working on healthcare AI projects

Prerequisites

• No coding required
• Basic familiarity with healthcare workflows (EHR, diagnostics, clinical decisions) is helpful
• Interest in responsible technology deployment and patient safety

What You’ll Learn

• Ethical principles for healthcare AI: safety, fairness, accountability, transparency
• Patient privacy and data protection basics (consent, access control, retention)
• Bias and fairness: sources of bias, subgroup performance, mitigation approaches
• Explainability: what clinicians and patients need to trust AI outputs
• Clinical validation: evidence, evaluation metrics, and limitations documentation
• Risk management: failure modes, human oversight, escalation pathways
• Governance: model approval workflows, audit trails, monitoring and change control
• GenAI in healthcare: hallucination risk, safe use policies, and guardrails

Program Structure

Module 1: Responsible AI Foundations for Healthcare

• Why ethics and governance matter for patient safety
• AI system lifecycle: development → validation → deployment → monitoring
• Roles and accountability across teams

Module 2: Privacy, Consent, and Data Stewardship

• Consent models and appropriate data use
• De-identification basics and access control
• Data retention, sharing, and vendor considerations

Module 3: Bias, Fairness, and Equity

• Bias sources: sampling, labeling, measurement, historical inequities
• Subgroup evaluation and fairness reporting
• Mitigation: data, modeling, thresholds, and workflow design

Module 4: Transparency and Explainability

• Explainability requirements in clinical contexts
• Reason codes, uncertainty, and limitations communication
• Documentation for clinicians and governance committees

Module 5: Clinical Validation and Safety Controls

• Evaluation planning: clinical endpoints and operational metrics
• Human oversight design and escalation pathways
• Safety checks, QA, and post-deployment monitoring

Module 6: Governance Operating Model

• AI governance committee structure and responsibilities
• Approval workflow: model cards, risk assessments, sign-offs
• Audit trails, change control, and incident response

Module 7: GenAI Governance in Healthcare

• Safe use of LLMs: hallucination, data leakage, prompt security
• Guardrails: retrieval-based answers, redaction, policy filters
• Usage policies for clinicians, staff, and patients

Module 8: Implementation Toolkit

• Governance templates and checklists
• Monitoring dashboard requirements (drift, safety, subgroup performance)
• Deployment readiness checklist for hospitals/HealthTech

Tools & Templates Covered

• AI risk assessment checklist (clinical + operational)
• Model documentation templates (model card / factsheet format)
• Bias and fairness reporting outline
• Monitoring and incident response checklist
• GenAI safe-use policy outline for healthcare teams

Outcomes

• Set up governance workflows for approving and monitoring healthcare AI
• Define privacy, bias, and safety requirements for AI deployments
• Evaluate AI tools with clear documentation and audit readiness
• Create a practical governance toolkit aligned to healthcare workflows

Certificate Criteria (Optional)

• Complete learning checkpoints
• Submit a governance plan (risk checklist + approval workflow + monitoring plan)