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Introduction to Medical Software: Foundations for Digital Health, Devices, and Diagnostics (Cambridge Texts in Biomedical Engineering) - Hardcover

 
9781316514993: Introduction to Medical Software: Foundations for Digital Health, Devices, and Diagnostics (Cambridge Texts in Biomedical Engineering)

Inhaltsangabe

Providing a concise and accessible overview of the design, implementation and management of medical software, this textbook will equip students with a solid understanding of critical considerations for both standalone medical software (software as a medical device/SaMD) and software that is integrated into hardware devices. It includes: practical discussion of key regulatory documents and industry standards, and how these translate into concrete considerations for medical software design; detailed coverage of the medical software lifecycle process ; accessible introduction to quality and risk management systems in the context of medical software; succinct coverage of essential topics in data science, machine learning, statistics, cybersecurity, software engineering and healthcare bring readers up-to-speed; six cautionary real-world case studies illustrate the dangers of improper or careless software processes. Accompanied by online resources for instructors, this is the ideal introduction for undergraduate students in biomedical engineering, electrical engineering and computer science, junior software engineers, and digital health entrepreneurs.

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Über die Autorinnen und Autoren

Xenophon Papademetris is Professor of Radiology and Biomedical Imaging, and Biomedical Engineering at Yale University whose research focuses on biomedical image analysis and software development. He has over twenty years of experience in medical software design.

Ayesha N. Quraishi is an MD serving as the Chief of Otolaryngology at the Cambridge Health Alliance; Faculty at Harvard Medical School; former Clinical Director of the Yale Center for Biomedical Innovation and Technology; a member of the MIT H@cking Medicine Initiative and Lecturer in the Practice of Management at Yale School of Management. She holds an MBA from the MIT Sloan School of Management with an emphasis on global leadership and innovation.

Gregory P. Licholai teaches at Yale School of Management and is Co-Director of the Center for Digital Health. He also co-teaches Innovating Health Care at Harvard Business School. He is Chief Medical and Innovation Officer at ICON plc, a leading pharmaceutical service, healthcare data and contract research provider. He serves on multiple company and non-profit boards including the Digital Medicine Society (DiMe) and is an advisor on the Clinical Trials Transformation Initiative (CTTI), a public–private partnership co-founded by Duke University and the Food and Drug Administration (FDA). He received his MD from Yale School of Medicine and MBA from Harvard Business School. He writes about innovation in healthcare for Forbes.

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  • VerlagCambridge University Press
  • Erscheinungsdatum2022
  • ISBN 10 1316514994
  • ISBN 13 9781316514993
  • EinbandTapa dura
  • SpracheEnglisch
  • Anzahl der Seiten342
  • Kontakt zum HerstellerNicht verfügbar

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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 'The goal of this book is to provide in one brief and accessible volume a survey of the critical material involved in the design, implementation, and management of medical software for both standalone software ('software as a medical device - SaMD') and software that is part of a physical medical device. One will find more detailed treatments of many of the topics covered in this book in specialized books that focus on some of the topics we cover (e.g. software engineering, systems engineering, probability theory, machine learning). Depth was not our goal; this book is explicitly designed to provide a broad survey. Our hope is to familiarize the reader with the span of topics he or she may need in entering this field and to provide pointers to more specialized publications as this becomes necessary. For example, most computer scientists have very limited exposure to statistical decision theory, and we think that even the cursory coverage in this book will at least enable them to understand 'what they do not know' and seek help as opposed to being ignorant of this entire field and attempting to reinvent the wheel in an amateurish manner! An emerging challenge in medical software is the increasing use of big data and artificial intelligence/machine learning (AI/ML) techniques. This places an even greater stress on proper software design and management. Given that these are 'black box' methods, in which the human understanding of what actually is going on is limited, a proper software quality process will be even more critical in creating reliable software tools. We introduce this topic in Section 1.3. In that section we also provide pointers to the other sections of the book in which we discuss issues related to the use of AI/ML methods. This is an introductory book. One can and should follow the material here with further study, using both original regulatory materials, industry standards,1 and more advanced books.2 Our goal can be summarized by the phrase 'to convert unknown unknowns to known unknowns.' Our goal is to make our reader aware of important material he or she is not as familiar with as one should be, and to pursue further study to acquire such knowledge. This is not a programming book. Our goal is to describe the enabling activities that support programmers in producing high-quality software in the context of medical applications. We are less concerned by questions such as 'How should we code ' Our focus, rather, is on answering higher-level questions such as 'How do we decide what we need to code ' and 'How should the process be organized ' There is a wealth of material available that describes the actual coding process, and, therefore, we chose not to duplicate this type of description here'--. Artikel-Nr. 9781316514993

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