AED
UAD Dirham
Code: AED
Mentor Based
AI Ethics and Explainable AI (XAI) in Healthcare
Ensuring Ethical and Transparent AI in Healthcare: From Fairness to Explainability
Early access to e-LMS included
About This Course
This program emphasizes the ethical considerations of using AI in healthcare, such as fairness, bias, accountability, and patient privacy. Additionally, it covers Explainable AI (XAI) methodologies that ensure transparency, allowing healthcare professionals and patients to understand the decisions made by AI systems. The program integrates hands-on experience with case studies of AI implementations in healthcare, addressing ethical dilemmas and regulatory frameworks.
Aim
To explore the intersection of AI ethics and Explainable AI (XAI) in the healthcare industry. This program is designed to equip PhD scholars, academicians, and healthcare professionals with knowledge of ethical challenges and explainability in AI-powered healthcare applications, ensuring trust, transparency, and fairness in medical AI systems.
Program Objectives
- Understand key ethical concerns in AI systems for healthcare.
- Master techniques in Explainable AI (XAI) to ensure transparency in medical AI applications.
- Explore real-world case studies of ethical AI in healthcare.
- Learn about regulatory frameworks governing AI in healthcare.
- Gain hands-on experience in building explainable AI models for healthcare.
Program Structure
- Introduction to AI Ethics in Healthcare
- Overview of ethical challenges in healthcare AI applications
- Regulatory considerations and compliance (HIPAA, GDPR)
- Key Ethical Issues in AI
- Bias, fairness, transparency, and accountability in healthcare AI
- Addressing bias in AI-based medical decisions
- Explainable AI (XAI) for Healthcare
- Importance of explainability in clinical AI systems
- Techniques for XAI (LIME, SHAP, counterfactual explanations)
- Case Studies: AI Ethics in Healthcare
- Real-world examples of AI systems in healthcare
- Ethical failures and success stories in healthcare AI
- Regulatory Frameworks and Legal Considerations
- Overview of regulations governing AI in healthcare
- Future trends in AI governance
- Hands-on Session: Building Explainable Healthcare AI
- Implementation of XAI techniques in AI models for healthcare
- Debugging and explaining AI-driven healthcare decisions
Who Should Enrol?
AI researchers, healthcare professionals, ethicists, legal experts, and academicians focusing on healthcare AI.
Program Outcomes
- Proficiency in handling ethical dilemmas in AI healthcare applications.
- Ability to implement and explain AI-driven healthcare models using XAI techniques.
- Understanding of AI regulation and compliance in healthcare systems.
- Hands-on skills in developing transparent, fair, and accountable AI solutions for healthcare.
Fee Structure
Discounted: ₹10999 | $164
We accept 20+ global currencies. View list →
What You’ll Gain
- Full access to e-LMS
- Real-world dry lab projects
- 1:1 project guidance
- Publication opportunity
- Self-assessment & final exam
- e-Certificate & e-Marksheet
Join Our Hall of Fame!
Take your research to the next level with NanoSchool.
Publication Opportunity
Get published in a prestigious open-access journal.
Centre of Excellence
Become part of an elite research community.
Networking & Learning
Connect with global researchers and mentors.
Global Recognition
Worth ₹20,000 / $1,000 in academic value.
★★★★★
AI-Powered Customer Experience
purvesh mehta • ★★★★★
AI for Environmental Monitoring and Sustainablility
Mladen Kulev • ★★★★★
Scientific Paper Writing: Tools and AI for Efficient and Effective Research Communication
Shabihul Sayed • ★★★★★
🌱 AI-Powered Life Cycle Assessment Dashboards
Javad • View All Feedbacks →