Explainable AI in Chronic Disease Diagnosis and Decision Making

Overview

This lecture is part of the 3rd Public Health Conference in 2025, offering healthcare professionals the opportunity to explore innovative AI solutions for managing chronic diseases, ultimately improving health outcomes and system efficiency. This year's theme emphasizes the role of artificial intelligence (AI) in addressing the challenges posed by chronic diseases, which significantly burden healthcare systems globally.

What I will learn?

• • Define Explainable AI (XAI) and its significance in healthcare.
  • Identify key concepts and principles of XAI relevant to chronic disease diagnosis.
  • Application of XAI in Chronic Diseases:
  • Describe how XAI can enhance the diagnosis of chronic diseases such as diabetes, heart disease, and cancer.
  • Analyze case studies demonstrating the implementation of XAI in clinical settings.

Related Classes

• Large Language Models for Health: Practical Examples
• Prompt Engineering for Chronic Diseases
• The Importance of Data - Health care
• AI in Health Care : Lessons Learned
• AI in Predictive Modeling for Epidemics
• Predictive Analytics for Disease Management
• Public Health Surveillance & Global Research Collaborations
• Telemedicine & Remote Monitoring Tools