Book

Description

Trustworthy Medical AI: From Otoscopic Image Classification to Provenance-Gated, Explainable, and Human-Supervised Decision Systems Can artificial intelligence be trusted when medical decisions depend on its predictions? As AI transforms healthcare, achieving high classification accuracy is no longer sufficient. Reliable medical intelligence must also address data integrity, uncertainty, explainability, reproducibility, and responsible human oversight. Trustworthy Medical AI presents a comprehensive exploration of these challenges through the lens of intelligent otoscopic image analysis and clinical decision-support systems. Building upon the author's published research in Elsevier's Array journal, the book introduces provenance-gated hybrid expert systems that integrate machine learning, generative representations, interpretable image features, uncertainty calibration, and selective human-supervised referral. Readers will discover how medical imaging datasets can be audited for duplication, leakage, and acquisition bias; how generative AI can be evaluated without assuming that synthetic data improves performance; and how calibrated predictions and uncertainty-aware referral mechanisms can support safer clinical workflows. The book combines mathematical foundations, system architectures, experimental methodologies, reproducibility principles, practical exercises, and critical discussions of clinical limitations. Particular emphasis is placed on distinguishing promising research findings from evidence sufficient for real-world deployment. Designed for AI researchers, healthcare professionals, postgraduate students, biomedical engineers, and technology innovators, this volume bridges the gap between algorithmic development and responsible medical AI implementation. Beyond prediction, trustworthy medical AI demands evidence, accountability, and human judgment.