- ホーム
- > 洋書
- > 英文書
- > Computer / General
Full Description
Unlock the future of healthcare innovation with this comprehensive guide to AI and machine learning in medical software. Designed for engineers, data scientists, and healthcare leaders, this book provides a practical roadmap for developing safe, effective, and compliant AI/ML-driven medical devices. From foundational principles to advanced deployment, including Software in Medical Devices (SiMD) and Software as a Medical Device (SaMD), this book covers everything you need to know. Explore a unique end-to-end development methodology tailored for AI-enabled solutions, cloud architectures, and regulated healthcare environments. Discover how to streamline product releases, navigate privacy and security mandates, and master global medical device regulations—all this while accelerating time to market with real-world strategies and tools. Hands-on projects and exclusive case studies, such as Apple's Sleep Apnea Notification and Notal Vision's Home OCT system, provide actionable insights from the cutting edge of AI-powered diagnosis and care. Whether you're just starting or scaling enterprise-grade AI, this book teaches you how to leverage tomorrow's innovations, including Generative AI, federated learning, edge deployment, and cloud-native best practices. Keep usability, trust, and safety at the heart of your solutions. This book bridges the technical and clinical worlds, making it your indispensable companion for building the future of intelligent healthcare.
Unique Selling points:
Describes each stage of AI/ML-enabled medical device development, covering both regulatory and technical requirements;
Illustrates how AI/ML-enabled device development differs from traditional software development in medical devices;
Includes strategies for addressing common challenges during development and regulatory review.
Contents
Introduction.- Clinical data management.- Aiml enabled medical device training algorithm selection.- Aiml clinical model training and evaluation.- clinical aiml model transparency.- Clinical aiml model testing and validation.- Aiml clinical model integration and deployment.- Security and privacy considerations aiml enabled medical devices.- Medical device machine learning operation mlops.- Medical device risk management human factor and harmonized standards for aiml enabled medical devices.- AI based health care applications in the cloud case studies.
