Chapter 1, Introducing Machine Learning and the AWS Machine Learning Stack, covers the basic concepts of machine learning and how it differs from a traditional software application.

Chapter 2, Exploring Key AWS Machine Learning Services for Healthcare and Life Sciences, dives into some key machine learning services from AWS that are critical for healthcare and life sciences industries. This chapter will give you an introduction to these services, their key APIs, and some usage examples.

Chapter 3, Machine Learning for Patient Risk Stratification, explains the concept of risk stratification of patients. It shows how common machine learning algorithms for classification and regression tasks can be applied to identify at-risk patients.

Chapter 4, Using Machine Learning to Improve Operational Efficiency for Healthcare Providers, covers operational efficiency in healthcare and why it is important. You will also learn about two common applications of machine learning to improve operational efficiency for healthcare providers.

Chapter 5, Implementing Machine Learning for Healthcare Payors, introduces you to the healthcare payor industry. You will get an understanding of how health insurance organizations process claims.

Chapter 6, Implementing Machine Learning for Medical Devices and Radiology Images, introduces you to the medical device industry. It goes into the details of various regulatory requirements for medical devices to be approved for use based on the type of medical device.

Chapter 7, Applying Machine Learning to Genomics, explores the world of genomes and the evolution of genomic sequencing. We will see how genomic data interpretation and analysis is changing the world of medicine.

Chapter 8, Applying Machine Learning to Molecular Data, introduces molecular data and its interpretation. We will learn about the process of the discovery of new drugs or therapies.

Chapter 9, Applying Machine Learning to Clinical Trials and Pharmacovigilance, covers how we ensure the safety and efficacy of new drugs and therapies before they are available for patients.

Chapter 10, Utilizing Machine Learning in the Pharmaceutical Supply Chain, dives into the world of the pharmaceutical supply chain workflow and introduces you to some challenges in getting new drugs and therapies to patients around the world in a timely manner.

Chapter 11, Understanding Common Industry Challenges and Solutions, summarizes some key challenges, including the regulatory and technical aspects, that deter organizations from adopting machine learning in healthcare and life sciences applications.

Chapter 12, Understanding Current Industry Trends and Future Applications, is all about the future of AI in healthcare and life sciences. We will review some trends in the world of AI/ML and its applications in the healthcare and life sciences industry, understand what’s influencing these trends, and see what may lie in store for us in the future.