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AI for Healthcare Diagnostics: Deep Learning Applications: Building Intelligent Models for Medical Imaging and Disease Detection
Paperback
Medical ReferenceGeneral Science
Publisher: Independently Published
Published: Dec 7 2025
Pages: 428
Weight: 1.25
Height: 0.87 Width: 6.00 Depth: 9.00
Language: English
In a world where diagnostic accuracy defines outcomes, this book shows clinicians, researchers, and data scientists how to harness deep learning to elevate medical decision-making. AI for Healthcare Diagnostics demystifies the algorithms transforming radiology, pathology, cardiology, oncology, and clinical workflow optimization.
Written with clarity and scientific rigor, the book walks readers through the full lifecycle of building diagnostic AI systems. You will learn how to source and prepare medical datasets, architect neural networks tailored to diagnostic tasks, and validate models using industry-grade performance metrics. Each chapter is grounded in real-world case studies, from automated tumor detection to retinal disease screening, ICU risk prediction, genomic pattern recognition, and multimodal fusion models that combine imaging, text, and sensor data.
This book is a guide to precision. You will discover how to navigate the regulatory landscape, address bias in clinical datasets, ensure reproducibility, and deploy models responsibly in hospitals and research environments. With step-by-step frameworks, implementation patterns, and practical code templates, readers gain the ability to build diagnostic tools that are scalable, safe, and clinically meaningful.
Whether you are an AI engineer entering the healthcare domain or a medical professional looking to understand the systems reshaping your field, AI for Healthcare Diagnostics offers a definitive, future-oriented playbook. It equips you with the analytical mindset and technical foundations to create next-generation diagnostic intelligence capable of improving patient outcomes on a global scale.
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