Medical Robotics and AI-Assisted Diagnostics for a High-Tech Healthcare Industry

Provides an overview of the current state of medical imaging and clinical diagnosis applications, then expands into a roadmap for the future, envisioning the seamless integration of medical robotics and AI-assisted applications in the high-tech healthcare industry.

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While ultra-high field strength diagnosis technologies and artificial intelligence have propelled medicine imaging towards microstructure analysis and precise medicine, persistent challenges remain. These range from long scanning times to motion sensitivity and issues with imaging quality for certain types of tissue. Medical Robotics and AI-Assisted Diagnostics for a High-Tech Healthcare Industry summarizes emerging techniques, outlines clinical applications, and confronts the challenges head-on, proposing avenues for further research. It explores emerging techniques such as human-like robotics, medical Internet of Things (IoT), low-cost CT scanners, portable MRI devices, and breakthroughs in diagnosis technologies like zero echo time (ZTM) and compressed sensing volume interpolation breath-holding test sequences (CS-VIBE). This book provides an overview of the current state of medical imaging and clinical diagnosis applications, then expands into a roadmap for the future, envisioning the seamless integration of medical robotics and AI-assisted applications in the high-tech healthcare industry. As the influence of artificial intelligence continues to grow, the book serves as a clarion call for collaborative efforts, increased research, and unified strategies to navigate the challenges and harness the opportunities presented by the high-tech medical industry. This book is ideal for medical analysts, healthcare scientists, biotechnology analysts, scholars, researchers, academics, professionals, engineers, and students worldwide. It consolidates contributions from a broad spectrum of experts to share ideas, methodologies, technologies, and approaches that can collectively address the challenges associated with the convergence of machine learning, deep learning, computer vision, AI-integrated applications, IoT-based technologies, human-like robotics, healthcare data analytics, and the application of biotechnology in the era of an AI-centric industry.