Program Structure

Module 1: Introduction to AI Ethics in Healthcare

• What is AI in healthcare? Overview of AI applications from clinical decision support to diagnostics and patient management.
• The ethical significance of AI in healthcare: impact on patient care, clinician decision-making, and health outcomes.
• Key ethical principles: autonomy, beneficence, non-maleficence, justice, and transparency in AI systems.

Module 2: Data Privacy and Security in AI Healthcare Systems

• Data privacy laws and regulations: HIPAA, GDPR, and the role of informed consent in AI systems.
• Protecting sensitive health data: encryption, secure access protocols, and breach mitigation strategies.
• AI and the challenge of data sharing: balancing innovation with patient confidentiality.

Module 3: Bias and Fairness in AI for Healthcare

• How bias can emerge in AI healthcare systems and the consequences of biased algorithms on patient outcomes.
• Strategies to detect and mitigate bias in healthcare AI, including diverse datasets and fairness algorithms.
• Case studies: examining AI applications that have faced fairness challenges in healthcare.

Module 4: Transparency, Accountability, and Explainability in Healthcare AI

• The need for transparency in AI models: ensuring healthcare professionals understand and trust AI recommendations.
• Explainability in AI: techniques for making AI decision-making processes understandable to users.
• Building accountable AI systems: who is responsible when AI decisions affect patient outcomes?

Module 5: Regulatory and Legal Frameworks for AI in Healthcare

• The evolving regulatory landscape for AI in healthcare: FDA, EMA, and other global standards.
• How regulations affect AI product development, clinical trials, and market access.
• Ethical and legal implications of AI-powered healthcare solutions: intellectual property, liability, and compliance.

Module 6: AI Governance in Healthcare

• What is AI governance and why it is crucial in healthcare settings?
• Building a governance framework: risk assessment, oversight, stakeholder engagement, and decision-making processes.
• Case studies of successful AI governance models in healthcare organizations.

Module 7: Ensuring Equity in AI Healthcare Applications

• Addressing health disparities with AI: opportunities for improving access to care for underserved populations.
• Ensuring equitable AI access: barriers to adoption in rural and low-income settings.
• Promoting diversity in AI development teams to better represent patient populations.

Module 8: Ethical AI in Healthcare Research

• AI in healthcare research: using AI to drive innovation while ensuring research ethics are upheld.
• Ensuring patient-centric research practices: informed consent, autonomy, and data use agreements.
• Ethical dilemmas in clinical AI trials: participant recruitment, data transparency, and post-trial accessibility.

Module 9: Future of AI Ethics and Governance in Healthcare

• The future of AI regulation: upcoming challenges and opportunities in global healthcare markets.
• AI in precision medicine and its ethical implications.
• Preparing for AI governance in evolving healthcare ecosystems: preparing clinicians, researchers, and policymakers for the future.

Final Project

• Design a comprehensive AI ethics and governance framework for a specific AI healthcare application (e.g., telemedicine, clinical decision support, personalized medicine).
• Define key ethical considerations, governance strategies, regulatory compliance, and recommendations for fairness, transparency, and privacy.
• Example projects include designing a governance model for AI diagnostics tools, AI-powered patient monitoring systems, or predictive health analytics platforms.