Explainable AI in Clinical Practice

Explainable AI in Clinical Practice (1st Edition) addresses the main challenge of linking artificial intelligence capabilities with real-world clinical implementation. Edited by Arvind Panwar, Achin Jain, Saurav Mallik, Aimin Li, and Korhan Cengiz, this 526-page volume published by Academic Press delivers a thorough investigation into transparent computational methods built for modern medical environments.

The volume structures its analytical content around core technical themes, examining medical image analysis alongside natural language processing in clinical documentation. It systematically addresses time series analysis for patient monitoring, demonstrating how the integration of multiple data modalities assists computational specialists in structuring interpretable models for complex patient data streams.

By featuring real-world case studies that show practical deployments of explainable systems, the volume offers clear examples of translating computational designs into clinical environments. The material provides dedicated guidance for bioinformatics researchers, scholars and postgraduate students in artificial intelligence, healthcare informatics researchers, healthcare professionals, and technical teams implementing healthcare AI.