Welcome to our book review site go-pdf.online!
You may have to Search all our reviewed books and magazines, click the sign up button below to create a free account.
Learning What Works
It is essential for patients and clinicians to have the resources needed to make informed, collaborative care decisions. Despite this need, only a small fraction of health-related expenditures in the United States have been devoted to comparative effectiveness research (CER). To improve the effectiveness and value of the care delivered, the nation needs to build its capacity for ongoing study and monitoring of the relative effectiveness of clinical interventions and care processes through expanded trials and studies, systematic reviews, innovative research strategies, and clinical registries, as well as improving its ability to apply what is learned from such study through the translation an...
Challenging Cases in Clinical Research Ethics
Clinical research ethics consultation has emerged in the last 15 years as a service to those involved in the conduct of clinical research who face challenging issues for which more than one course of action may be justified. To respond to a growing field and need for opportunities to share knowledge and experience, the Clinical Research Ethics Consultation Collaborative, established in 2014, holds monthly webinars for its 90 members to present their most challenging cases to each other and engage in substantive discussion. Every year, the group selects the four most interesting cases with accompanying commentaries for publication in the American Journal of Bioethics. This timely book brings ...
Medical Experimentation
This new edition of Charles Fried's Medical Experimentation includes a general introduction by Franklin Miller and the late Alan Wertheimer, a reprint of the 1974 text, an in-depth analysis by Harvard Law School scholars I. Glenn Cohen and D. James Greiner, and a new essay by Fried reflecting on the original text and how it applies to the contemporary landscape of medicine and medical experimentation.
The Economics of Artificial Intelligence
A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.
The Ethical Governance of Artificial Intelligence and Machine Learning in Healthcare
This book explores the ethical governance of Artificial Intelligence (AI) & Machine Learning (ML) in healthcare. AI/ML usage in healthcare as well as our daily lives is not new. However, the direct, and oftentimes long-term effects of current technologies, in addition to the onset of future innovations, have caused much debate about the safety of AI/ML. On the one hand, AI/ML has the potential to provide effective and efficient care to patients, and this sways the argument in favor of continuing to use AI/ML; but on the other hand, the dangers (including unforeseen future consequences of the further development of the technology) leads to vehement disagreement with further AI/ML usage. Due to its potential for beneficial outcomes, the book opts to push for ethical AI/ML to be developed and examines various areas in healthcare, such as big data analytics and clinical decision-making, to uncover and discuss the importance of developing ethical governance for AI/ML in this setting.
Reconsidering Race
To better understand the idea of "race" in the postgenomic age, social science ought to move beyond merely repeating the "race is a social construct" mantra. This collection directly engages the interface between social-scientific and natural-scientific perspectives on race considering recent developments in genomics. The book provides views that go beyond US-centered or Western-based paradigms on race.
Redesigning the Clinical Effectiveness Research Paradigm
Recent scientific and technological advances have accelerated our understanding of the causes of disease development and progression, and resulted in innovative treatments and therapies. Ongoing work to elucidate the effects of individual genetic variation on patient outcomes suggests the rapid pace of discovery in the biomedical sciences will only accelerate. However, these advances belie an important and increasing shortfall between the expansion in therapy and treatment options and knowledge about how these interventions might be applied appropriately to individual patients. The impressive gains made in Americans' health over the past decades provide only a preview of what might be possib...
States of Health
States of Health identifies the practical relevance of federalism in the United States to people facing ethical decisions about health and health care, and it considers the theoretical justifications for permissible differences among states. It asks whether authority over important aspects of health is misaligned in the United States today, with some matters problematically left to the states while others are taken over by the federal government.
Artificial Intelligence and Healthcare
This book explores the ethical problems of algorithmic bias and its potential impact on populations that experience health disparities by examining the historical underpinnings of explicit and implicit bias, the influence of the social determinants of health, and the inclusion of racial and ethnic minorities in data. Over the last twenty-five years, the diagnosis and treatment of disease have advanced at breakneck speeds. Currently, we have technologies that have revolutionized the practice of medicine, such as telemedicine, precision medicine, big data, and AI. These technologies, especially AI, promise to improve the quality of patient care, lower health care costs, improve patient treatment outcomes, and decrease patient mortality. AI may also be a tool that reduces health disparities; however, algorithmic bias may impede its success. This book explores the risks of using AI in the context of health disparities. It is of interest to health services researchers, ethicists, policy analysts, social scientists, health disparities researchers, and AI policy makers.
Artificial Intelligence Technologies for Computational Biology
This text emphasizes the importance of artificial intelligence techniques in the field of biological computation. It also discusses fundamental principles that can be applied beyond bio-inspired computing. It comprehensively covers important topics including data integration, data mining, machine learning, genetic algorithms, evolutionary computation, evolved neural networks, nature-inspired algorithms, and protein structure alignment. The text covers the application of evolutionary computations for fractal visualization of sequence data, artificial intelligence, and automatic image interpretation in modern biological systems. The text is primarily written for graduate students and academic ...
