Predictive Analytics in Healthcare, Volume 2

All stakeholders in the health paradigm can now confidently agree that AI is becoming a key component of delivering healthcare. There is growing evidence to establish AI's impact on improving the quality of disease detection, diagnosis, management, and treatment, preventing adverse events, improving overall health and well-being, and decreasing healthcare costs. Several AI and analytics books comprehensively cover the technical implementation details. However, there is a lack of books that present an application-oriented treatment of analytics in healthcare. The purpose of this book is to present to the reader details on where and how AI and its cutting-edge advancements can be employed to improve healthcare. To achieve this purpose, chapters will focus on reviewing and analyzing AI's current and future applications in several healthcare disciplines, which can later contribute to technical implementation.

To begin with, the reader is introduced to academic entrepreneurship and the steps required to bring more research from the lab to real life. The next chapter explores the opportunities of applying generative AI in medical education, such as curriculum development, teaching methodologies, personalized learning, learning assessment, medical program tracking, and medical research. Chapter 3 presents ten ways how AI can improve the adoption of integrative medicine by discussing some successful case studies. Chapter 4 discusses the latest advancements in AI-driven applications across the continuum of the journey of a patient with prostate cancer, from risk assessment and stratification to screening, diagnosis, treatment, and management. Finally, chapter 5 presents the four most innovative AI-powered solutions that have disrupted precision medicine - digital twins, multi-omics integration, AI-driven biomarker discovery, and personalized therapeutics.