AI Ethics and Explainable AI (XAI) in Healthcare

Ensuring Ethical and Transparent AI in Healthcare: From Fairness to Explainability

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MODE
Online/ e-LMS

TYPE
Self Paced

LEVEL
Advanced

DURATION
4 Weeks

About

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

Participant’s Eligibility

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

Standard Fee: INR ~~5,998~~ USD 90

Discounted Fee: INR 2999 USD 45

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Key Takeaways

Program Assessment

Certification to this program will be based on the evaluation of following assignment (s)/ examinations:

Exam	Weightage
Mid Term Assignments	50 %
Project Report Submission (Includes Mandatory Paper Publication)	50 %

To study the printed/online course material, submit and clear, the mid term assignments, project work/research study (in completion of project work/research study, a final report must be submitted) and the online examination, you are allotted a 1-month period. You will be awarded a certificate, only after successful completion/ and clearance of all the aforesaid assignment(s) and examinations.