Explainable AI (XAI) in Digital Pathology

The use of AI in digital pathology has transformed the way diseases like cancer, neurological disorders, and other pathologies are diagnosed. However, one of the main challenges with AI in healthcare is the “black-box” nature of many AI models, where even experts cannot understand how predictions are made. Explainable AI (XAI) aims to address this by making AI models more interpretable and understandable, which is especially critical in clinical settings where transparency is essential.
This workshop will cover the importance of XAI in digital pathology, discussing various techniques for explaining AI predictions. Participants will learn how to use XAI methods to visualize, interpret, and validate AI models, ensuring their accuracy and reliability. Real-world examples and case studies in cancer diagnosis and other applications will be explored to showcase how XAI can enhance clinical trust and adoption of AI technologies.

This workshop aims to introduce participants to the principles and applications of Explainable AI (XAI) in digital pathology, focusing on making AI-driven diagnostics more transparent, interpretable, and trustworthy. The workshop will explore how XAI can improve the decision-making process in pathology by providing insights into AI model predictions.

• Understand the role and importance of Explainable AI (XAI) in healthcare and digital pathology.
• Learn the techniques for improving AI transparency and model interpretability in medical imaging.
• Gain hands-on experience with XAI tools to visualize and interpret deep learning models for pathology.
• Explore case studies where XAI has been used to enhance clinical decision-making in pathology.
• Develop the skills to integrate XAI models into clinical workflows, improving diagnostic accuracy and trust in AI systems.

• Undergraduate/Postgraduate Degree in Computer Science, Biomedical Engineering, Bioinformatics, or related fields.
• Professionals in healthcare, pathology, medical imaging, or AI/ML research.
• Data Scientists and AI Engineers interested in applying explainable AI techniques in healthcare.
• Individuals passionate about the intersection of AI, healthcare, and digital pathology.

• Understanding XAI: Grasp the principles of explainable AI and its importance in digital pathology.
• Practical Skills: Learn to apply XAI techniques for interpreting AI models used in medical imaging.
• AI Visualization: Gain experience with XAI tools for visualizing and interpreting deep learning models in pathology.
• Real-World Application: Analyze how XAI improves diagnostic workflows and enhances clinical trust in AI models.
• Integration in Clinical Practice: Develop strategies to integrate XAI-driven AI models into real-world pathology labs and healthcare systems.