AI in Healthcare and Public Health: Ethical Challenges and Governance Solutions Training Course

Course Introduction

Artificial Intelligence (AI) is transforming healthcare and public health by enhancing diagnostics, patient care, epidemiological research, and healthcare management. While AI offers immense potential, its rapid deployment raises significant ethical, legal, and governance challenges. This course, AI in Healthcare and Public Health: Ethical Challenges and Governance Solutions Training Course, equips participants with the knowledge and tools to navigate these challenges responsibly.

The program explores AI applications across clinical care, public health surveillance, and health policy. Participants will examine both the benefits such as precision medicine, predictive analytics, and operational efficiency and the risks, including bias, data misuse, and unequal access to AI-driven healthcare solutions.

Ethical considerations are central to the course. Participants will study privacy, informed consent, equity, transparency, and patient safety. Case studies demonstrate both successful AI applications and failures resulting from ethical or governance oversights.

Participants will learn to implement governance frameworks and compliance strategies to ensure AI adoption in healthcare is responsible, accountable, and aligned with national and international regulations, including HIPAA, GDPR, and WHO guidelines.

Interactive exercises and scenario-based learning allow participants to evaluate AI systems, design ethically aligned interventions, and implement oversight mechanisms. Emphasis is placed on multi-stakeholder collaboration among clinicians, policymakers, technology developers, and patients.

By the end of the course, participants will be prepared to lead AI initiatives in healthcare and public health ethically and effectively. They will gain practical skills to design, evaluate, and govern AI solutions that enhance healthcare delivery while safeguarding patient rights and public trust.

Who Should Attend

• Healthcare administrators and policy makers overseeing AI integration.
• Clinicians and healthcare professionals implementing AI systems.
• AI developers and data scientists working in health technology.
• Legal and compliance officers ensuring ethical AI deployment.
• Public health officials and epidemiologists leveraging AI for surveillance.
• Researchers and academics studying AI in healthcare and public health.
• Hospital IT and innovation managers adopting AI solutions.
• NGOs and international organizations working in health technology.
• Health data privacy and cybersecurity professionals.
• Bioethics and patient advocacy specialists guiding AI adoption.

Duration

10 days

Course Objectives

• Equip participants with knowledge of AI applications in healthcare and public health.
• Strengthen understanding of ethical principles for responsible AI adoption.
• Provide tools to assess risks such as bias, privacy breaches, and inequity.
• Enhance skills in implementing governance frameworks and regulatory compliance.
• Foster transparency, accountability, and explainability in AI healthcare systems.
• Build capacity for multi-stakeholder engagement and collaboration.
• Promote equitable access to AI-driven healthcare solutions.
• Develop strategies to mitigate ethical, legal, and societal risks in AI adoption.
• Encourage responsible AI research, innovation, and deployment in health sectors.
• Empower participants to lead AI initiatives that balance innovation with public health ethics.

Course Outline

Module 1: Introduction to AI in Healthcare and Public Health

• Overview of AI applications in clinical care and public health
• Benefits and risks of AI adoption in healthcare
• Core ethical principles in health technology
• Case studies of AI successes and failures in healthcare

Module 2: Ethical Principles in Health AI

• Privacy, consent, and patient autonomy
• Equity, fairness, and non-discrimination
• Transparency and explainability in AI systems
• Accountability in clinical and public health AI

Module 3: Governance and Regulatory Frameworks

• National and international regulations (HIPAA, GDPR, WHO guidelines)
• Hospital and organizational governance structures
• Compliance strategies for ethical AI deployment
• Monitoring and enforcement mechanisms

Module 4: AI in Clinical Decision-Making

• Diagnostic support systems and predictive analytics
• Risk of algorithmic bias in patient care
• Integrating AI into clinical workflows
• Monitoring patient outcomes and safety

Module 5: AI in Public Health Surveillance

• Predictive modeling for disease outbreaks
• Population health analytics and epidemiology
• Ethical data collection and use
• Multi-agency coordination and response

Module 6: Patient Privacy and Data Protection

• Data security, storage, and sharing protocols
• Privacy-preserving AI techniques
• Legal compliance and data governance
• Balancing innovation with patient rights

Module 7: Algorithmic Bias and Fairness

• Detecting bias in healthcare AI systems
• Fairness metrics and mitigation strategies
• Case studies of biased AI and corrective interventions
• Ensuring equitable healthcare delivery

Module 8: Explainability and Transparency

• Tools for interpretable AI in healthcare
• Communicating AI decisions to patients and clinicians
• Reporting and documentation standards
• Enhancing trust and accountability in AI systems

Module 9: Autonomous Healthcare Systems

• Robotics, autonomous diagnostics, and telemedicine
• Safety, liability, and human oversight
• Ethical deployment in sensitive healthcare contexts
• Regulatory considerations for autonomous technologies

Module 10: AI for Health Equity and Access

• Promoting inclusivity and reducing disparities
• AI-driven solutions for underserved populations
• Monitoring equitable access and outcomes
• Ethical considerations in global health technology

Module 11: AI in Health Research and Innovation

• Ethical considerations in clinical trials using AI
• Data integrity, reproducibility, and scientific ethics
• Informed consent and patient participation
• Risk management in research AI applications

Module 12: Multi-Stakeholder Collaboration

• Engaging clinicians, policymakers, and patients
• Interdisciplinary governance approaches
• Public-private partnerships in health AI
• Participatory decision-making frameworks

Module 13: Emerging AI Technologies in Healthcare

• Generative AI for diagnostics and treatment planning
• AI-enabled wearables and remote monitoring
• Deep learning applications in imaging and genomics
• Ethical and societal implications of new AI tools

Module 14: Risk Management and Ethical Auditing

• Identifying ethical, legal, and operational risks
• Auditing AI systems for compliance and safety
• Risk mitigation strategies
• Continuous evaluation and improvement

Module 15: Case Studies and Lessons Learned

• Successful AI implementation in hospitals and public health programs
• Failures due to ethical or governance oversights
• Comparative analysis across countries and health systems
• Strategies for scaling responsible AI solutions

Module 16: Designing Responsible Health AI Solutions

• Developing an ethically compliant AI project for healthcare
• Integrating governance, risk mitigation, and stakeholder input
• Presenting actionable and sustainable AI initiatives
• Ensuring transparency, equity, and public trust

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue

The training will be held at our Upskill Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment

Unless otherwise agreed between the two parties payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better