This book presents recent work on healthcare management and engineering using artificial intelligence and data mining techniques. Specific topics covered in the contributed chapters include predictive mining, decision support, capacity management, patient flow optimization, image compression, data clustering, and feature selection.

Hutan Ashrafian, MBBS, MRCS, PhD, MBA, is a clinician-scientist and active surgeon translating novel technologies and therapeutics in healthcare and policy. He has led R&D as chief scientific adviser at the Institute of Global Health Innovation at Imperial College London and as chief medical officer at a FTSE 100 multinational, and is currently chief scientific officer at the global biotech and venture firm Flagship Pioneering in Preemptive Medicine and Health Security and his own start-ups. He has over 20 years of translational clinical, computational physiology, digital and AI trial, and product development experience, including novel COVID vaccines and national tracing apps. He leads the STARD-AI and QUADAS-AI global guideline initiatives for AI diagnostic accuracy. As honorary lecturer at Imperial College London, he runs the collaboration with Imperial College London, NHS Hospitals, and Google on an AI algorithm for breast screening and also with NICE on health technological assessment classifications for AI.

"Data is quickly emerging as the greatest asset of the healthcare industry. The trend in our industry is to drive many decisions supported by data. The authors have done a great job put- ting together the issues, challenges and benefits of adopting a long view of Big Data. It is a walk of maturity with the real gold nuggets coming in Analytics 3.0 and beyond. This will not be solved with a product or purchased off the shelf. Big Data needs to be part of the DNA of an organization. "Intelligent decisions are best made with data that gives us rich context and a fuller view of all parameters and possibilities.

S. Kevin Zhou, Ph.D. is currently a Principal Key Expert Scientist at Siemens Healthcare Technology Center, leading a team of full time research scientists and students dedicated to researching and developing innovative solutions for medical and industrial imaging products. His research interests lie in computer vision and machine/deep learning and their applications to medical image analysis, face recognition and modeling, etc. He has published over 150 book chapters and peer-reviewed journal and conference papers, registered over 250 patents and inventions, written two research monographs, and edited three books. He has won multiple technology, patent and product awards, including R&D 100 Award and Siemens Inventor of the Year. He is an editorial board member for Medical Image Analysis journal and a fellow of American Institute of Medical and Biological Engineering (AIMBE).

Sergio Consoli is a Senior Scientist within the Data Science department at Philips Research, Eindhoven, focusing on advancing automated analytical methods used to extract new knowledge from data for health-tech applications. He is author of several research publications in peer-reviewed international journals, edited books, and leading conferences in the fields of his work. Diego Reforgiato Recupero is Associate Professor at the Department of Mathematics and Computer Science of the University of Cagliari, Italy. His interests span from Semantic Web, graph theory and smart grid optimization to sentiment analysis, data mining, big data, machine and deep learning and natural language processing. He is also affiliated within the ISTC institute at the National Research Council (CNR) and co-founder of six ICT companies two of which are university spin-offs.

This book is an excellent source for machine learning in computer-aided diagnosis which is a rapidly growing area in medicine, especially medical imaging. It comprehensively covers recent advances in technologies and applications in major areas in the field of computer-aided diagnosis. The editor and contributors are very famous researchers in the field. It is an excellent reference book.

I used this book as a textbook for a special topics course on computational biomedicine that I taught twice so far at a graduate level. The book offered the foundational knowledge level for the course. The CI part is basic and needs good support, and that was not a problem, given that I cater to an engineering audience. No matter how good a book is for a graduate course, you would still need to offer support through readings from recent journal and conference publications. Yet, this book was good enough to put students on track for doing basic experiments using some of its researched ideas and works. Recently, I tried to find a substitute for this book, mainly because my institution requires using recently published books that are no more than 6 years old, and also because I needed an update on the material.