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MSK Library Resources
Browse these lists to view selected electronic journal and book resources related to artificial intelligence available via the MSK Library.
Covers international literature in the biomedical and pharmaceutical fields.
Includes references from MEDLINE as well as references from other biomedical and life sciences databases.
Scope includes medicine, chemistry, physics, psychology, health sciences and more.
Web of Science
A multidisciplinary resource covering open access materials, conference proceedings, and scholarly publications.
Selected Electronic Journal Titles
Note: Many medical journals not specifically focused on AI topics publish articles related to artificial intelligence.
Below are AI-related titles to which the Library has current full-text access:
Selected Electronic Book Titles
Deep Learning in Medical Image Analysis: Challenges and Applications by
Publication Date: 2020
This book presents cutting-edge research and applications of deep learning in a broad range of medical imaging scenarios, such as computer-aided diagnosis, image segmentation, tissue recognition and classification, and other areas of medical and healthcare problems.
Artificial Intelligence in Medical Imaging Opportunities, Applications and Risks by
Publication Date: 2019
This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.
Deep learning: fundamentals, theory and applications by
Publication Date: 2019
The purpose of this edited volume is to provide a comprehensive overview on the fundamentals of deep learning, introduce the widely-used learning architectures and algorithms, present its latest theoretical progress, discuss the most popular deep learning platforms and data sets, and describe how many deep learning methodologies have brought great breakthroughs in various applications of text, image, video, speech and audio processing. Deep learning (DL) has been widely considered as the next generation of machine learning methodology. DL attracts much attention and also achieves great success in pattern recognition, computer vision, data mining, and knowledge discovery due to its great capability in learning high-level abstract features from vast amount of data. This new book will not only attempt to provide a general roadmap or guidance to the current deep learning methodologies, but also present the challenges and envision new perspectives which may lead to further breakthroughs in this field. This book will serve as a useful reference for senior (undergraduate or graduate) students in computer science, statistics, electrical engineering, as well as others interested in studying or exploring the potential of exploiting deep learning algorithms. It will also be of special interest to researchers in the area of AI, pattern recognition, machine learning and related areas, alongside engineers interested in applying deep learning models in existing or new practical applications.
Digital Medicine by
Publication Date: 2019
This book provides an up to date user friendly resource on the emerging field of digital medicine and its present and potential future role in modern healthcare. Chapters are written by a specialist on each area in an easy to read format, which broadly covers the potential of digital medicine in epidemiology, precision medicine and surgery. Chapters focus on aspects of telemedicine, the applications of big data, artificial intelligence, blockchain, regenerative medicine, legal aspects and business models. Furthermore, guidance is given on medical ethics and how to manage doctor patient relationships in the modern age. Digital Medicine comprehensively reviews the emerging field of digital medicine in modern healthcare and is therefore a critical resource for physicians and medical trainees who are looking for comprehensive resource on digital medicine and its potential role in modern healthcare.
Personalized psychiatry : big data analytics in mental health by
Publication Date: 2019
This book integrates the concepts of big data analytics into mental health practice and research. Mental disorders represent a public health challenge of staggering proportions. According to the most recent Global Burden of Disease study, psychiatric disorders constitute the leading cause of years lost to disability. The high morbidity and mortality related to these conditions are proportional to the potential for overall health gains if mental disorders can be more effectively diagnosed and treated. In order to fill these gaps, analysis in science, industry, and government seeks to use big data for a variety of problems, including clinical outcomes and diagnosis in psychiatry. Multiple mental healthcare providers and research laboratories are increasingly using large data sets to fulfill their mission. Briefly, big data is characterized by high volume, high velocity, variety and veracity of information, and to be useful it must be analyzed, interpreted, and acted upon. As such, focus has to shift to new analytical tools from the field of machine learning that will be critical for anyone practicing medicine, psychiatry and behavioral sciences in the 21st century. Big data analytics is gaining traction in psychiatric research, being used to provide predictive models for both clinical practice and public health systems. As compared with traditional statistical methods that provide primarily average group-level results, big data analytics allows predictions and stratification of clinical outcomes at an individual subject level.
Intelligent Orthopaedics Artificial Intelligence and Smart Image-guided Technology for Orthopaedics by
Publication Date: 2018
This book introduces readers to the latest technological advances in the emerging field of intelligent orthopaedics. Artificial intelligence and smart instrumentation techniques are now revolutionizing every area of our lives, including medicine. The applications of these techniques in orthopaedic interventions offer a number of potential benefits, e.g. reduced incision size and scarring, minimized soft tissue damage, and decreased risk of misalignment. Consequently, these techniques have become indispensable for various orthopaedic interventions, which has led to the emerging field of intelligent orthopaedics. Addressing key technologies and applications, this book offers a valuable guide for all researchers and clinicians who need an update on both the principles and practice of intelligent orthopaedics, and for graduate students embarking on a career in this field.
Machine Learning in Radiation Oncology Theory and Applications by
Publication Date: 2015
This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.
Machine Learning in Medicine by
Publication Date: 2013
Machine learning is a novel discipline concerned with the analysis of large and multiple variables data. It involves computationally intensive methods, like factor analysis, cluster analysis, and discriminant analysis. It is currently mainly the domain of computer scientists, and is already commonly used in social sciences, marketing research, operational research and applied sciences. It is virtually unused in clinical research. This is probably due to the traditional belief of clinicians in clinical trials where multiple variables are equally balanced by the randomization process and are not further taken into account. In contrast, modern computer data files often involve hundreds of variables like genes and other laboratory values, and computationally intensive methods are required. This book was written as a hand-hold presentation accessible to clinicians, and as a must-read publication for those new to the methods.