In recent years, artificial intelligence has increasingly been playing an essential role in diverse areas in medicine, assisting clinicians in patient management. In nephrology and transplantation, artificial intelligence can be utilized to enhance clinical care, such as through hemodialysis prescriptions and the follow-up of kidney transplant patients. Furthermore, there are rapidly expanding applications and validations of comprehensive, computerized medical records and related databases, including national registries, health insurance, and drug prescriptions. For this Special Issue, we made a call to action to stimulate researchers and clinicians to submit their invaluable works and present, here, a collection of articles covering original clinical research (single- or multi-center), database studies from registries, meta-analyses, and artificial intelligence research in nephrology including acute kidney injury, electrolytes and acid–base, chronic kidney disease, glomerular disease, dialysis, and transplantation that will provide additional knowledge and skills in the field of nephrology and transplantation toward improving patient outcomes.

In recent years, artificial intelligence has increasingly been playing an essential role in diverse areas in medicine, assisting clinicians in patient management. In nephrology and transplantation, artificial intelligence can be utilized to enhance clinical care, such as through hemodialysis prescriptions and the follow-up of kidney transplant patients. Furthermore, there are rapidly expanding applications and validations of comprehensive, computerized medical records and related databases, including national registries, health insurance, and drug prescriptions. For this Special Issue, we made a call to action to stimulate researchers and clinicians to submit their invaluable works and present, here, a collection of articles covering original clinical research (single- or multi-center), database studies from registries, meta-analyses, and artificial intelligence research in nephrology including acute kidney injury, electrolytes and acid-base, chronic kidney disease, glomerular disease, dialysis, and transplantation that will provide additional knowledge and skills in the field of nephrology and transplantation toward improving patient outcomes.

Our world is facing unprecedented technological development, which affects all the sectors of society. The 4th industrial revolution has brought numerous advances that are currently integrated in our daily life, including artificial intelligence (A.I.), internet of things (IoT), genetic engineering, 3D-printing and robotics. The health care sector is one of the most impacted by these technologies of the so-called digital era. From the simple advent of medical records to robotic surgery, health care has significantly changed from the XX to XXI century and is constantly changing, incorporating novel technologies. Nephrology is itself an innovative branch of medicine, created as a discipline in the 1960s, with breakthrough inventions, such as the dialysis machine, which made it possible to prolong life of those who suffer from chronic kidney disease; kidney transplant, with point-of-care immunosuppression that favours maintenance of kidney allografts for long years; kidney biopsy, which made it possible to discover the mysteries of glomerulonephritis and nephropathology. Novel technologies, such as A.I., IoT, robotics, stem cells, 3D-printing, mHealth, eHealth and several others are starting to be applied in nephrology, with promising results. It is possible that a great part of these technologies will become routinely available in clinical practice, and the burden of kidney diseases will significantly decrease once prevention, prediction, detection, monitoring and treatment of kidney diseases are more precise, with patients taking part in the process and becoming more and more connected. This book gathers essential information on the technologies that have been applied in nephrology and that can be applied in the future, with real possibilities of improving the care of kidney diseases. At first glance, this work is directed to the entire nephrology community and all the healthcare professionals that deal with kidney diseases. Researchers from different fields, not directly linked to nephrology, may also be interested in the book since many of the topics presented are related to other areas and serve as examples of their uses in medicine, such as artificial intelligence, robotics, and big data. Finally, the content provides an important resource to medical students, discussing technologies that will certainly be integrated in their professional practice.

This book is a thorough and comprehensive guide to the use of modern data science within health care. Critical to this is the use of big data and its analytical potential to obtain clinical insight into issues that would otherwise have been missed and is central to the application of artificial intelligence. It therefore has numerous uses from diagnosis to treatment. Clinical Applications of Artificial Intelligence in Real-World Data is a critical resource for anyone interested in the use and application of data science within medicine, whether that be researchers in medical data science or clinicians looking for insight into the use of these techniques.

This book highlights a timely and accurate insight at the endeavour of the bioinformatics and genomics clinicians from industry and academia to address the societal needs. The contents of the book unearth the lacuna between the medication and treatment in the current preventive medicinal and pharmaceutical system. It contains chapters prepared by experts in life sciences along with data scientists for examining the circumstances of health care system for the next decade. It also highlights the automated processes for analyzing data in clinical trial research, specifically for drug development. Additionally, the data science solutions provided in this book help pharmaceutical companies to improve on what had historically been manual, costly and laborious process for cross-referencing research in clinical trials on drug development, while laying the groundwork for use with a full range of other drugs for the conditions ranging from tuberculosis, to diabetes, to heart attacks and many others.

The confluence of big data, artificial intelligence (AI), and machine learning (ML) has led to a paradigm shift in how innovative medicines are developed and healthcare delivered. To fully capitalize on these technological advances, it is essential to systematically harness data from diverse sources and leverage digital technologies and advanced analytics to enable data-driven decisions. Data science stands at a unique moment of opportunity to lead such a transformative change. Intended to be a single source of information, Data Science, AI, and Machine Learning in Drug Research and Development covers a wide range of topics on the changing landscape of drug R & D, emerging applications of big data, AI and ML in drug development, and the build of robust data science organizations to drive biopharmaceutical digital transformations. Features Provides a comprehensive review of challenges and opportunities as related to the applications of big data, AI, and ML in the entire spectrum of drug R & D Discusses regulatory developments in leveraging big data and advanced analytics in drug review and approval Offers a balanced approach to data science organization build Presents real-world examples of AI-powered solutions to a host of issues in the lifecycle of drug development Affords sufficient context for each problem and provides a detailed description of solutions suitable for practitioners with limited data science expertise

This book seeks to promote the exploitation of data science in healthcare systems. The focus is on advancing the automated analytical methods used to extract new knowledge from data for healthcare applications. To do so, the book draws on several interrelated disciplines, including machine learning, big data analytics, statistics, pattern recognition, computer vision, and Semantic Web technologies, and focuses on their direct application to healthcare. Building on three tutorial-like chapters on data science in healthcare, the following eleven chapters highlight success stories on the application of data science in healthcare, where data science and artificial intelligence technologies have proven to be very promising. This book is primarily intended for data scientists involved in the healthcare or medical sector. By reading this book, they will gain essential insights into the modern data science technologies needed to advance innovation for both healthcare businesses and patients. A basic grasp of data science is recommended in order to fully benefit from this book.