EDP on "Decoding the Black Box: Explainable AI in Healthcare and Medical Diagnostics"

Date: July 21st - 25th, 2026

AIM of EDP

The aim of the EDP entitled “Decoding the Black Box: Explainable AI in Healthcare and Medical Diagnostics” is designed to provide participants (IT & Healthcare Industry Professionals) with comprehensive knowledge of interpreting complex AI models, ensuring clinical safety, and applying trustworthy, transparent Machine Learning methodologies within medical diagnostics and digital health environments.

Key Objectives of the EDP

To introduce the fundamentals of Explainable AI (XAI) and its critical need in clinical decision-making.

To bridge the gap between complex deep learning models and clinical interpretability for healthcare applications.

To explore transparent methodologies like SHAP, LIME, and Grad-CAM in medical imaging and diagnostics.

To understand the regulatory compliance, bioethics, and safety standards governing AI in medicine.

To familiarize participants with XAI frameworks and tools using python libraries on clinical datasets.

To discuss trustworthy and responsible AI practices to minimize bias in medical automated screening.

To provide insight into real-world case studies of interpretable AI in disease prediction and electronic health records (EHR).

To explore emerging trends such as GenAI in medical reporting and Privacy-Preserving AI in healthcare analytics.