Springer Explainable AI in Health Informatics - Computational Intelligence Methods

Explainable AI in Health Informatics explores the intersection of artificial intelligence and healthcare through the lens of computational intelligence methods and applications. This Springer publication provides comprehensive coverage of how explainability techniques enhance AI systems in medical environments, enabling clinicians and researchers to understand and trust machine learning models used in diagnostic support, patient monitoring, and treatment planning. The book addresses the critical need for transparency in healthcare AI, covering methodologies that make complex algorithms interpretable whilst maintaining predictive accuracy. Ideal for academics, healthcare professionals, and AI practitioners, this resource bridges the gap between theoretical foundations and practical implementation of explainable AI systems in real-world health informatics scenarios.