Artificial Intelligence in Medical Software
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.
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.
More details
Other editions
Additional editions
Persons
Ajit K. Pandey, with over two decades of experience in the medical device industry, boasts extensive hands-on expertise in various therapeutic areas. His expertise encompasses artificial intelligence and machine learning in medical software, where he possesses a profound understanding of medical software development, global AI regulations, AI quality systems, and AI software development processes. Ajit holds a B.E. degree from the National Institute of Technology Surat and M.S. degrees from the University of Cincinnati and the University of Southern California.
Pramod Gupta has over 25 years of experience as a researcher and academic in various organizations, including collaborations with NASA, GE, VISA, the University of California, and startups. He holds a Ph.D. in Electrical and Computer Engineering from McMaster University, with a specialization in Neuro-Control of Robotic Manipulators. His research areas include Neural Networks, Machine Learning, Artificial Intelligence, Data Modeling and Analytics, and Data Mining. He has more than 40 publications on these subjects and has co-authored three books published by Springer. Presently, he is an Adjunct Faculty at various universities in the US and works as an independent data science consultant.
Naresh K. Sehgal is currently a Cloud Computing Consultant. Before that he worked at Nova Signal for 3 years and at Intel for 31 years in various Engineering and Management roles. Naresh has earned his B.E. from Punjab Engineering College, M.S. and Ph.D. from Syracuse University. He taught a Cloud Computing class at Santa Clara University, where he also earned an MBA. Naresh has 9 patents, co-authored 8 books, published 40 technical papers in various conferences and journals.
Content
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.
