Explainable AI Case Studies in Healthcare

This book chapter explores the concept of explainable artificial intelligence (Explainable AI) in healthcare, emphasizing its importance in readdressing the obstacles presented by black box AI models. The introduction highlights the adaptation of AI in healthcare is on the rise and the need for transparency and interpretability. The overview of Explainable AI provides insights into the methods and approaches employed to achieve explainability in AI models. The demonstrated importance of Explainable AI in healthcare highlights its potential to enhance patient outcomes and provide valuable support in clinical decision-making and ensure compliance with ethical and regulatory requirements. The methodology and approach for implementing Explainable AI in healthcare settings, including data collection and preprocessing, are discussed. Various Explainable AI techniques and models used in healthcare, their strengths, limitations, and interpretability levels are examined. Two case studies on retinopathy, skin cancer, and ICU mortality prediction diagnosis showcase the practical application of Explainable AI, illustrating how it enhances diagnostic accuracy, provides transparent insights into AI predictions, and fosters collaboration between clinicians and AI systems. In conclusion, Explainable AI plays a pivotal role in healthcare by ensuring transparency, interpretability, and trust in AI models, promoting responsible adoption, and improving patient care outcomes.