This is an open access book. The book provides an overview of the state of research in developing countries – Africa, Latin America, and Asia (especially India) and why research and publications are important in these regions. It addresses budding but struggling academics in low and middle-income countries. It is written mainly by senior colleagues who have experienced and recognized the challenges with design, documentation, and publication of health research in the developing world. The book includes short chapters providing insight into planning research at the undergraduate or postgraduate level, issues related to research ethics, and conduct of clinical trials. It also serves as a guide towards establishing a research question and research methodology. It covers important concepts such as writing a paper, the submission process, dealing with rejection and revisions, and covers additional topics such as planning lectures and presentations. The book will be useful for graduates, postgraduates, teachers as well as physicians and practitioners all over the developing world who are interested in academic medicine and wish to do medical research.

This book constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019. The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning.

The United States is among the wealthiest nations in the world, but it is far from the healthiest. Although life expectancy and survival rates in the United States have improved dramatically over the past century, Americans live shorter lives and experience more injuries and illnesses than people in other high-income countries. The U.S. health disadvantage cannot be attributed solely to the adverse health status of racial or ethnic minorities or poor people: even highly advantaged Americans are in worse health than their counterparts in other, "peer" countries. In light of the new and growing evidence about the U.S. health disadvantage, the National Institutes of Health asked the National Research Council (NRC) and the Institute of Medicine (IOM) to convene a panel of experts to study the issue. The Panel on Understanding Cross-National Health Differences Among High-Income Countries examined whether the U.S. health disadvantage exists across the life span, considered potential explanations, and assessed the larger implications of the findings. U.S. Health in International Perspective presents detailed evidence on the issue, explores the possible explanations for the shorter and less healthy lives of Americans than those of people in comparable countries, and recommends actions by both government and nongovernment agencies and organizations to address the U.S. health disadvantage.

The global response to the COVID-19 pandemic is the greatest science policy failure in a generation. We knew this was coming. Warnings about the threat of a new pandemic have been made repeatedly since the 1980s and it was clear in January that a dangerous new virus was causing a devastating human tragedy in China. And yet the world ignored the warnings. Why? In this short and hard-hitting book, Richard Horton, editor of the medical journal The Lancet, scrutinizes the actions that governments around the world took – and failed to take – as the virus spread from its origins in Wuhan to the global pandemic that it is today. He shows that many Western governments and their scientific advisors made assumptions about the virus and its lethality that turned out to be mistaken. Valuable time was lost while the virus spread unchecked, leaving health systems unprepared for the avalanche of infections that followed. Drawing on his own scientific and medical expertise, Horton outlines the measures that need to be put in place, at both national and international levels, to prevent this kind of catastrophe from happening again. Were supposed to be living in an era where human beings have become the dominant influence on the environment, but COVID-19 has revealed the fragility of our societies and the speed with which our systems can come crashing down. We need to learn the lessons of this pandemic and we need to learn them fast because the next pandemic may arrive sooner than we think.

As we taste, chew, swallow, digest, and excrete, our foods transform us, while our eating, in its turn, affects the wider earthly environment. In Eating in Theory Annemarie Mol takes inspiration from these transformative entanglements to rethink what it is to be human. Drawing on fieldwork at food conferences, research labs, health care facilities, restaurants, and her own kitchen table, Mol reassesses the work of authors such as Hannah Arendt, Maurice Merleau-Ponty, Hans Jonas, and Emmanuel Levinas. They celebrated the allegedly unique capability of humans to rise above their immediate bodily needs. Mol, by contrast, appreciates that as humans we share our fleshy substance with other living beings, whom we cultivate, cut into pieces, transport, prepare, and incorporate—and to whom we leave our excesses. This has far-reaching philosophical consequences. Taking human eating seriously suggests a reappraisal of being as transformative, knowing as entangling, doing as dispersed, and relating as a matter of inescapable dependence.

Offers an innovative study of visual traditions in modern medical history through debates about the causes, impact and spread of AIDS.

A history of the World Health Organization, covering major achievements in its seventy years while also highlighting the organization's internal tensions. This account by three leading historians of medicine examines how well the organization has pursued its aim of everyone, everywhere attaining the highest possible level of health.

Intelligence-Based Medicine: Data Science, Artificial Intelligence, and Human Cognition in Clinical Medicine and Healthcare provides a multidisciplinary and comprehensive survey of artificial intelligence concepts and methodologies with real life applications in healthcare and medicine. Authored by a senior physician-data scientist, the book presents an intellectual and academic interface between the medical and the data science domains that is symmetric and balanced. The content consists of basic concepts of artificial intelligence and its real-life applications in a myriad of medical areas as well as medical and surgical subspecialties. It brings section summaries to emphasize key concepts delineated in each section; mini-topics authored by world-renowned experts in the respective key areas for their personal perspective; and a compendium of practical resources, such as glossary, references, best articles, and top companies. The goal of the book is to inspire clinicians to embrace the artificial intelligence methodologies as well as to educate data scientists about the medical ecosystem, in order to create a transformational paradigm for healthcare and medicine by using this emerging new technology. - Covers a wide range of relevant topics from cloud computing, intelligent agents, to deep reinforcement learning and internet of everything - Presents the concepts of artificial intelligence and its applications in an easy-to-understand format accessible to clinicians and data scientists - Discusses how artificial intelligence can be utilized in a myriad of subspecialties and imagined of the future - Delineates the necessary elements for successful implementation of artificial intelligence in medicine and healthcare

"This reference set provides a complete understanding of the development of applications and concepts in clinical, patient, and hospital information systems"--Provided by publisher.

Medical Image Analysis presents practical knowledge on medical image computing and analysis as written by top educators and experts. This text is a modern, practical, self-contained reference that conveys a mix of fundamental methodological concepts within different medical domains. Sections cover core representations and properties of digital images and image enhancement techniques, advanced image computing methods (including segmentation, registration, motion and shape analysis), machine learning, how medical image computing (MIC) is used in clinical and medical research, and how to identify alternative strategies and employ software tools to solve typical problems in MIC. - An authoritative presentation of key concepts and methods from experts in the field - Sections clearly explaining key methodological principles within relevant medical applications - Self-contained chapters enable the text to be used on courses with differing structures - A representative selection of modern topics and techniques in medical image computing - Focus on medical image computing as an enabling technology to tackle unmet clinical needs - Presentation of traditional and machine learning approaches to medical image computing