[PDF] Foundations Of Artificial Intelligence In Healthcare And Bioscience eBook

Author : Louis J. Catania
Publisher : Academic Press
Page : 562 pages
File Size : 37,25 MB
Release : 2020-11-25
Category : Science
ISBN : 0323860052

GET BOOK

Foundational Handbook of Artificial Intelligence in Healthcare and Bioscience: A User Friendly Guide for IT Professionals, Healthcare Providers, Researchers, and Clinicians uses color-coded illustrations to explain AI from its basics to modern technologies. Other sections cover extensive, current literature research and citations regarding AI’s role in the business and clinical aspects of health care. The book provides readers with a unique opportunity to appreciate AI technology in practical terms, understand its applications, and realize its profound influence on the clinical and business aspects of health care. Artificial Intelligence is a disruptive technology that is having a profound and growing influence on the business of health care as well as medical diagnosis, treatment, research and clinical delivery. The AI relationships in health care are complex, but understandable, especially when discussed and developed from their foundational elements through to their practical applications in health care. Provides an illustrated, foundational guide and comprehensive descriptions of what Artificial Intelligence is and how it functions Integrates a comprehensive discussion of AI applications in the business of health care Presents in-depth clinical and AI-related discussions on diagnostic medicine, therapeutic medicine, and prevalent disease categories with an emphasis on immunology and genetics, the two categories most influenced by AI Includes comprehensive coverage of a variety of AI treatment applications, including medical/pharmaceutical care, nursing care, stem cell therapies, robotics, and 10 common disease categories with AI applications

Author : Ambreen Hamadani
Publisher : Elsevier
Page : 370 pages
File Size : 26,12 MB
Release : 2024-03-15
Category : Computers
ISBN : 0443240000

GET BOOK

A Biologist’s Guide to Artificial Intelligence: Building the Foundations of Artificial Intelligence and Machine Learning for Achieving Advancements in Life Sciences provides an overview of the basics of Artificial Intelligence for life science biologists. In 14 chapters/sections, readers will find an introduction to Artificial Intelligence from a biologist’s perspective, including coverage of AI in precision medicine, disease detection, and drug development. The book also gives insights into the AI techniques used in biology and the applications of AI in food, and in environmental, evolutionary, agricultural, and bioinformatic sciences. Final chapters cover ethical issues surrounding AI and the impact of AI on the future. This book covers an interdisciplinary area and is therefore is an important subject matter resource and reference for researchers in biology and students pursuing their degrees in all areas of Life Sciences. It is also a useful title for the industry sector and computer scientists who would gain a better understanding of the needs and requirements of biological sciences and thus better tune the algorithms. Helps biologists succeed in understanding the concepts of Artificial Intelligence and machine learning Equips with new data mining strategies an easy interface into the world of Artificial Intelligence Enables researchers to enhance their own sphere of researching Artificial Intelligence

Author : Jyotismita Talukdar
Publisher : Springer
Page : 0 pages
File Size : 29,46 MB
Release : 2023-10-13
Category : Computers
ISBN : 9789819931569

GET BOOK

This book presents a systematic evolution of artificial intelligence (AI), its applications, challenges and solutions in the field of healthcare. The book mainly covers the foundations and various methods of learning in artificial intelligence with its application in healthcare industry. This book provides a comprehensive introduction to data analysis using AI as a tool in the generation, normalization and analysis of healthcare data in association with several evaluation techniques and accuracy measurements. The book is divided into three major sections describing the basic foundations of AI and its associated algorithms, history of artificial intelligence in healthcare, recent developments and several modeling techniques for the same. The last section of the book provides insights into several implementations and methods of evaluation and accuracy prediction for healthcare analysis in AI. Extensive use of data for analysis and prediction using several technologies has transformed the lives of normal people indirectly effecting our process to communicate, learn, work and socialize within the society. Thus, the book also provides an insight into the ethics of AI that is very vital in the process of implementation and evaluation of healthcare data. The book provides an organized analysis to a considerable part of data in a digitized society. In view of this, it covers the theory, methodology, perfection and verification of empirical work for health-related data processing. Particular attention is devoted to in-depth experiments and applications.

Author : Tiago Cravo Oliveira Hashiguchi
Publisher :
Page : 33 pages
File Size : 27,19 MB
Release : 2021
Category :
ISBN :

GET BOOK

Artificial intelligence (AI) has the potential to make health care more effective, efficient and equitable. AI applications are on the rise, from clinical decision-making and public health, to biomedical research and drug development, to health system administration and service redesign. The COVID-19 pandemic is serving as a catalyst, yet it is also a reality check, highlighting the limits of existing AI systems. Most AI in health is actually artificial narrow intelligence, designed to accomplish very specific tasks on previously curated data from single settings. In the real world, health data are not always available, standardised, or easily shared. Limited data hinders the ability of AI tools to generate accurate information for diverse populations with potentially very complex conditions. Having appropriate patient data is critical for AI tools because decisions based on models with skewed or incomplete data can put patients at risk. Policy makers should beware of the hype surrounding AI and identify and focus on real problems and opportunities that AI can help address. In setting the foundations for AI to help achieve health policy objectives, one key priority is to improve data quality, interoperability and access in a secure way through better data governance. More broadly, policy makers should work towards implementing and operationalising the OECD AI Principles, as well as investing in technology and human capital. Strong policy frameworks based on inclusive and extensive dialogue among all stakeholders are also key to ensure AI adds value to patients and to societies. AI that influences clinical and public health decisions should be introduced with care. Ultimately, high expectations must be managed, but real opportunities should be pursued.

Author : Reza Borhani
Publisher : Springer Nature
Page : 201 pages
File Size : 31,99 MB
Release : 2022-11-18
Category : Medical
ISBN : 3031195027

GET BOOK

This book provides an accessible introduction to the foundations of machine learning and deep learning in medicine for medical students, researchers, and professionals who are not necessarily initiated in advanced mathematics but yearn for a better understanding of this disruptive technology and its impact on medicine. Once an esoteric subject known to few outside of computer science and engineering departments, today artificial intelligence (AI) is a widely popular technology used by scholars from all across the academic universe. In particular, recent years have seen a great deal of interest in the AI subfields of machine learning and deep learning from researchers in medicine and life sciences, evidenced by the rapid growth in the number of articles published on the topic in peer-reviewed medical journals over the last decade. The demand for high-quality educational resources in this area has never been greater than it is today, and will only continue to grow at a rapid pace. Expert authors remove the veil of unnecessary complexity that often surrounds machine learning and deep learning by employing a narrative style that emphasizes intuition in place of abstract mathematical formalisms, allowing them to strike a delicate balance between practicality and theoretical rigor in service of facilitating the reader’s learning experience. Topics covered in the book include: mathematical encoding of medical data, linear regression and classification, nonlinear feature engineering, deep learning, convolutional and recurrent neural networks, and reinforcement learning. Each chapter ends with a collection of exercises for readers to practice and test their knowledge. This is an ideal introduction for medical students, professionals, and researchers interested in learning more about machine learning and deep learning. Readers who have taken at least one introductory mathematics course at the undergraduate-level (e.g., biostatistics or calculus) will be well-equipped to use this book without needing any additional prerequisites.

Author : Arjen Hommersom
Publisher : Springer
Page : 336 pages
File Size : 20,15 MB
Release : 2016-01-07
Category : Computers
ISBN : 3319280074

GET BOOK

Medicine and health care are currently faced with a significant rise in their complexity. This is partly due to the progress made during the past three decades in the fundamental biological understanding of the causes of health and disease at the molecular, (sub)cellular, and organ level. Since the end of the 1970s, when knowledge representation and reasoning in the biomedical field became a separate area of research, huge progress has been made in the development of methods and tools that are finally able to impact on the way medicine is being practiced. Even though there are huge differences in the techniques and methods used by biomedical researchers, there is now an increasing tendency to share research results in terms of formal knowledge representation methods, such as ontologies, statistical models, network models, and mathematical models. As there is an urgent need for health-care professionals to make better decisions, computer-based support using this knowledge is now becoming increasingly important. It may also be the only way to integrate research results from the different parts of the spectrum of biomedical and clinical research. The aim of this book is to shed light on developments in knowledge representation at different levels of biomedical application, ranging from human biology to clinical guidelines, and using different techniques, from probability theory and differential equations to logic. The book starts with two introductory chapters followed by 18 contributions organized in the following topical sections: diagnosis of disease; monitoring of health and disease and conformance; assessment of health and personalization; prediction and prognosis of health and disease; treatment of disease; and recommendations.

Author : Ambreen Hamadani
Publisher : Elsevier
Page : 368 pages
File Size : 15,90 MB
Release : 2024-03
Category : Computers
ISBN : 0443240019

GET BOOK

A Biologist's Guide to Artificial Intelligence: Building the Foundations of Artificial Intelligence and Machine Learning for Achieving Advancements in Life Sciences provides an overview of the basics of Artificial Intelligence for life science biologists. In 14 chapters/sections, readers will find an introduction to Artificial Intelligence from a biologist's perspective, including coverage of AI in precision medicine, disease detection, and drug development. The book also gives insights into the AI techniques used in biology and the applications of AI in food, and in environmental, evolutionary, agricultural, and bioinformatic sciences. Final chapters cover ethical issues surrounding AI and the impact of AI on the future. This book covers an interdisciplinary area and is therefore is an important subject matter resource and reference for researchers in biology and students pursuing their degrees in all areas of Life Sciences. It is also a useful title for the industry sector and computer scientists who would gain a better understanding of the needs and requirements of biological sciences and thus better tune the algorithms.

Author : Dr. Shahul Hameed Pakkir Mohamed
Publisher : Priya Lokare
Page : 161 pages
File Size : 45,2 MB
Release : 2022-07-08
Category : Computers
ISBN : 8195694152

GET BOOK

Artificial Intelligence (AI) and related propels are logically dominating in business and society, and are beginning to be applied to clinical benefits. These advancements might perhaps change various pieces of patient thought, as well as administrative cycles inside the provider, payer, and medication affiliations. There are currently different investigation studies recommending that AI can continue as well as or better than individuals at key clinical benefits tasks, such as diagnosing disease. Today, estimations are at this point beating radiologists at spotting perilous developments and guiding experts in how to fabricate associates for costly clinical starters. In any case, in light of multiple factors, we acknowledge that it will be various earlier years AI replaces individuals for far-reaching clinical cycle regions. In this article, we depict both the potential that AI offers to robotize parts of care and a piece of the blocks too fast execution of AI in clinical benefits.

Author : Taneja, Sanjay
Publisher : IGI Global
Page : 378 pages
File Size : 40,17 MB
Release : 2024-02-12
Category : Business & Economics
ISBN :

GET BOOK

In the field of finance, the pervasive influence of algorithms has transformed the very fabric of the industry. Today, over 75% of trades are orchestrated by algorithms, making them the linchpin for trade automation, predictions, and decision-making. This algorithmic reliance, while propelling financial services into unprecedented efficiency, has also ushered in a host of challenges. As the financial sector becomes increasingly algorithm-driven, concerns about risk assessment, market manipulation, and the ethical implications of automated decision-making have taken center stage. Artificial Intelligence and Machine Learning-Powered Smart Finance, meticulously examines the intersection of computational finance and advanced algorithms and the challenges associated with this technology. As algorithms permeate various facets of financial services, the book takes a deep dive into their applications, spanning forecasting, portfolio optimization, market trends analysis, and cryptoanalysis. It sheds light on the role of AI-based algorithms in personnel selection, implementing trusted financial services, developing recommendation systems for financial platforms, and detecting fraud, presenting a compelling case for the integration of innovative solutions in the financial sector. As the book unravels the intricate tapestry of algorithmic applications in finance, it also illuminates the ethical considerations and governance frameworks essential for navigating the delicate balance between technological innovation and responsible financial practices.