Preface

We have seen multiple ways in which AI is touching our lives in a meaningful way. It’s almost like having a superpower that you never knew you needed, but now that you have it, you can see how it is changing our lives for the better. Automation driven by AI is helping organizations achieve levels of operational efficiency they never thought were possible, which in turn is boosting economies. The accessibility of cutting-edge infrastructure and models has improved tremendously with the power of cloud computing, which has democratized AI by putting it in the hands of everyone. It is no wonder that the last decade has seen the use of AI in the healthcare and life sciences industry increase massively. As the industry is undergoing a transformation driven by technology and digitization, it produces large volumes of data in multiple modalities. To utilize the full potential of this data, organizations are applying machine learning to process, analyze, and interpret critical information from these datasets to improve and save the lives of patients. It is helping improve provider efficiencies and improve care quality, and is bringing the costs of drugs and therapies down.

This book will help you understand how this is happening. It will introduce you to the different verticals of the healthcare and life sciences industry such as providers, payors, pharmaceuticals, genomics, and medical imaging. It begins by introducing you to the concept of machine learning and then progresses to show how you can apply machine learning to workloads in each of these industry verticals. The book gradually builds your Amazon Web Services (AWS) machine learning knowledge. You will be introduced to low-code AI services from AWS and each chapter progresses to more advanced topics. The exercises at the end of the chapters are designed for you to practice what you learned and apply the learning to an actual problem in the industry vertical. I hope you enjoy this ride and find what you learn from this book valuable for a long time to come.