Translational Application of Artificial Intelligence in Healthcare: - A Textbook

Algorithmic Medicine integrates Artificial Intelligence (AI) in healthcare to address critical challenges, offering a textbook with practical steps for transitioning AI from experimental to real-time clinical integration. It covers technical, clinical, ethical, regulatory, and legal considerations, with evidence-based solutions and case studies.

Format: Paperback / softback
Length: 132 pages
Publication date: 11 December 2023
Publisher: Taylor & Francis Ltd

In the realm of Algorithmic Medicine, the integration of Artificial Intelligence (AI) in healthcare presents a remarkable opportunity to address the pressing challenges that the industry faces. Drawing upon the expertise and experience of the authors, who possess backgrounds in medicine, data science, medical informatics, administration, and entrepreneurship, this textbook goes beyond theoretical discussions to provide practical guidance for transitioning AI from its experimental phase to real-time clinical integration. Employing the Translational Science methodology, each chapter of the book concisely and clearly addresses the critical issues associated with AI implementation in healthcare. These issues encompass technical, clinical, ethical, regulatory, and legal considerations, and the authors present evidence-based solutions and frameworks to overcome them. Engaging case studies and a comprehensive literature review of peer-reviewed studies and official documents from reputable organizations offer a balanced perspective, bridging the gap between AI research and actual clinical practice.

The authors of this textbook have made a significant contribution by compiling a comprehensive resource that caters to healthcare professionals, researchers, policymakers, and students alike. By delving into the complexities of AI implementation in healthcare, they have provided a valuable guide for navigating the challenges and maximizing the benefits of this emerging technology. The book is organized into five chapters, each focusing on a different aspect of AI integration.

Chapter 1 provides an introduction to AI and its potential applications in healthcare. It explores the different types of AI algorithms, their strengths and limitations, and the ethical considerations that arise when using AI in healthcare. The authors also discuss the importance of data privacy and security in AI implementation and provide insights into the regulatory framework that governs AI in healthcare.

Chapter 2 focuses on the technical aspects of AI implementation in healthcare. It covers topics such as machine learning, natural language processing, computer vision, and robotics. The authors provide a detailed explanation of these algorithms and their applications in healthcare, including their advantages, disadvantages, and potential challenges. They also discuss the importance of data quality and the need for standardized data formats to facilitate AI integration.

Chapter 3 explores the clinical applications of AI in healthcare. It discusses the use of AI in diagnosis, treatment planning, drug discovery, and patient monitoring. The authors present case studies and examples of how AI has been successfully implemented in various healthcare settings, such as hospitals, clinics, and research institutions. They also discuss the challenges associated with integrating AI into clinical practice, such as the need for interdisciplinary collaboration and the potential for bias in AI algorithms.

Chapter 4 addresses the ethical and legal considerations of AI implementation in healthcare. It explores the ethical principles that guide AI development and use, such as privacy, transparency, and accountability. The authors also discuss the legal framework that governs AI in healthcare, including the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR). They provide practical guidance on how healthcare organizations can comply with these regulations and ensure the ethical use of AI.

Chapter 5 concludes the textbook by discussing the future of AI in healthcare. It explores the potential for AI to revolutionize healthcare delivery, including its ability to improve patient outcomes, reduce healthcare costs, and enhance healthcare workforce productivity. The authors also discuss the challenges that need to be addressed to realize the full potential of AI in healthcare, such as the need for robust infrastructure, the development of ethical guidelines, and the need for ongoing research and development.

In conclusion, Algorithmic Medicine: Integrating Artificial Intelligence in Healthcare is a valuable resource for anyone interested in understanding the potential of AI in healthcare. The textbook provides a comprehensive and practical guide for transitioning AI from its experimental phase to real-time clinical integration. By covering technical, clinical, ethical, regulatory, and legal considerations, the authors have presented evidence-based solutions and frameworks to overcome the challenges associated with AI implementation. The engaging case studies and comprehensive literature review offer a balanced perspective, bridging the gap between AI research and actual clinical practice. This textbook is a must-read for healthcare professionals, researchers, policymakers, and students who want to stay informed about the latest developments in AI and its applications in healthcare.

Weight: 268g
Dimension: 173 x 246 x 12 (mm)
ISBN-13: 9781032200880