Mamoon Rashid - Böcker

A Handbook of Artificial Intelligence in Drug Delivery explores the use of Artificial Intelligence (AI) in drug delivery strategies. The book covers pharmaceutical AI and drug discovery challenges, Artificial Intelligence tools for drug research, AI enabled intelligent drug delivery systems and next generation novel therapeutics, broad utility of AI for designing novel micro/nanosystems for drug delivery, AI driven personalized medicine and Gene therapy, 3D Organ printing and tissue engineering, Advanced nanosystems based on AI principles (nanorobots, nanomachines), opportunities and challenges using artificial intelligence in ADME/Tox in drug development, commercialization and regulatory perspectives, ethics in AI, and more.

This book will be useful to academic and industrial researchers interested in drug delivery, chemical biology, computational chemistry, medicinal chemistry and bioinformatics. The massive time and costs investments in drug research and development necessitate application of more innovative techniques and smart strategies.

Focuses on the use of Artificial Intelligence in drug delivery strategies and future impacts Provides insights into how artificial intelligence can be effectively used for the development of advanced drug delivery systems Written by experts in the field of advanced drug delivery systems and digital health

Medical imaging informatics play an important role in the effectiveness of present-day healthcare systems. Advancement of artificial intelligence, big data analytics, and internet of things technologies contribute greatly to various healthcare applications. Artificial intelligence techniques are contributing to improvements with traditionally human-based systems and ensuring that the accuracy of prediction and diagnosis is being continually enhanced. The development of reliable and accurate healthcare models is becoming ever more possible with the help of machine learning and deep learning technologies. Artificial intelligence has the power to solve many complex problems in medical imaging and is a technology that will help to design the future of many healthcare systems.This edited book highlights and addresses various issues in medical imaging and provides viable solutions utilising artificial intelligence and big data tools. This book discusses techniques, algorithms, and tools which help build and develop research practices, platforms, and applications in medical image informatics.Medical image enhancement, big data analytics and artificial intelligence models are discussed with relation to applications in the detection of cancer, autism, allergies and diabetes. The design and development of internet of medical things and virtual reality tools for mental health disorders are also explored.This book is suitable reading for researchers and scientists, in both academia and industry, working in computer science and engineering, machine learning, image processing, and healthcare technologies. Those in aligned professions, such as healthcare practitioners, administrators, designers and developers may also find the subject matter of interest.

There are several popular books published in Healthcare Computational Informatics like Computational Bioengineering and Bioinformatics (2020), Springer; In this book, the book it has been attempted to bring all technologies related to computational analytics together and apply them on Biomedical Imaging.

There are several popular books published in Healthcare Computational Informatics like Computational Bioengineering and Bioinformatics (2020), Springer; Health Informatics (2017), Springer; Health Informatics Vision: From Data via Information to Knowledge (2019), IOS Press; Data Analytics in Biomedical Engineering and Healthcare (2020), Elsevier. However, in all these mentioned books, the challenges in Biomedical Imaging are solved in one dimension by use of any specific technology like Image Processing, Machine Learning or Computer Aided Systems.  In this book, the book it has been attempted to bring all technologies related to computational analytics together and apply them on Biomedical Imaging.