Introduction

AI, Big Data, and Healthcare: Data-Driven Applications (Part 2) explores how artificial intelligence, machine learning and big data analytics are advancing diagnosis, disease monitoring and healthcare manufacturing. Chapters present AI-driven depression detection, machine learning frameworks for diabetes and its comorbidities, real-time pneumonia detection in chest X-rays, multimodal deep learning for hepatocellular carcinoma, and wearable-based early detection of Parkinson’s disease. The volume also examines robotic and additive manufacturing, Industry 4.0 automation and predictive analytics, concluding with insights into AI’s evolving role in healthcare.

Key Features

• - Applied case studies in diagnostics and disease prediction.
• - Covers deep learning, multimodal models and wearables.
• - Reviews recent research, challenges and future directions.
• - Connects healthcare AI with Industry 4.0 manufacturing.

Target Readership :

Researchers, graduate students, clinicians, health informaticians, data scientists, biomedical engineers and healthcare technology professionals; also manufacturing engineers and digital health policymakers.