Название: Artificial intelligence in Pharmaceutical SciencesАвтор: Mullaicharam Bhupathyraaj, K. Reeta Vijaya Rani, Musthafa Mohamed Essa
Издательство: CRC Press
Год: 2024
Страниц: 181
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB
This cutting-edge reference book discusses the intervention of Artificial Intelligence (AI) in the fields of drug development, modified drug delivery systems, pharmaceutical technology, and medical devices development. This comprehensive book includes an overview of Artificial Intelligence in pharmaceutical sciences and applications in the drug discovery and development process. It discusses the role of Machine Learning (ML) in the automated detection and sorting of pharmaceutical formulations. It covers nanosafety and the role of Artificial Intelligence in predicting potential adverse biological effects.
Artificial Intelligence (AI) has offered a crucial part in significantly developing and nurturing the field of pharmacy throughout the world. The chapters in this book will deal with the omnipotent and omnipresent inevitability of AI, an overview of AI-driven pharmaceutical functionality, AI in drug delivery systems, the role of AI in drug discovery and development, AI applications in scrutinizing health status, and the benefits of robotics in surgery, analysis of pharmaceutical formulations using AI, role of AI in pharmaceutical drugs, gene therapies, and medical device regulation, ethical aspects of applications of AI in medicine and pharmacy, role of Machine Learning in automated detection and sorting of pharmaceutical formulations, AI in community pharmacy, and AI is all about creating a global win-win situation in the pharmaceutical world to enhance healthcare.
Machine Learning (ML) is a part of AI and Computer Science that centers on the use of data and algorithms to model in what manner humans learn, gradually increasing accuracy. The world-renowned technology giant International Business Machine (IBM) has a long history in ML. Huge advancements in ML from a storage and computing perspective are helping innovators discover newer products like Netflix, recommendation systems, or self-driving cars. There are several ML techniques, and neural network–powered ML has become a major method involved in AI. It is further divided into supervised learning and remedial learning, where ML facilitates learning from experience without having to program the task.
Deep Learning (DL) is a function of AI that mimics the activity of the human brain to create patterns for information processing and decision-making. It is a subset of ML in the field of AI that can control learning from unstructured or networked data. This is also referred to as deep neural learning or deep neural network. This allows users to process and predict data using neural networks. This brain-like network is connected to the human brain through a network-based structure. It is a ML function that works in a nonlinear decision-making process. In-depth learning takes place when deciding on unstructured unattended data. Object recognition is part of what is done by learning and translating the voice.
Features:
Includes lucid, step-by-step instructions to apply Artificial Intelligence and Machine Learning in pharmaceutical sciences
Explores the application of artificial intelligence in nanosafety and prediction of potential hazards
Covers application of artificial intelligence in drug discovery and drug development
Reviews the role of artificial intelligence in assessment of pharmaceutical formulations
Provides artificial intelligence solutions for experts in the pharmaceutical and medical devices industries
This book will be very useful in an academic setting (students, faculty, staff, researchers, and scientists), pharmaceutical industry, and regulatory affairs. Thus, this book will clearly and rationally convey and establish the impact of AI in the pharmacy world.
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