Applied Machine Learning for Healthcare and Life Sciences Using AWS: Transformational AI implementations for biotech, clinical, and healthcare organizations

*Machine Learning for Healthcare and Life Sciences on AWS* provides a comprehensive guide to building artificial intelligence applications within the medical and pharmaceutical sectors. The book begins by introducing fundamental machine learning concepts and the specific services available within the AWS machine learning stack, such as Amazon SageMaker and Amazon Comprehend Medical. It systematically explores the unique challenges faced by different industry segments, including healthcare providers, payers, drug discovery researchers, and genomics labs. By leveraging real-world datasets and cloud computing resources, readers learn how to implement practical solutions that address critical issues like patient risk stratification and operational efficiency. The text offers hands-on coding instructions to help data scientists and engineers apply AI to diverse data types, ranging from medical images and clinical notes to complex molecular data. Dedicated chapters cover specific applications such as improving radiology workflows, optimizing pharmaceutical supply chains, and enhancing pharmacovigilance in clinical trials. Beyond technical implementation, the book addresses essential best practices regarding security, privacy, fairness, and explainability, ensuring that solutions meet rigorous industry regulatory requirements. Designed for technology decision-makers and data engineering professionals, this resource bridges the gap between theoretical AI concepts and real-world healthcare utility. The final sections provide a forward-looking perspective on emerging industry trends and future applications of AI in life sciences. By the end of the book, readers will possess the skills to develop machine learning solutions that solve specific problems across the healthcare ecosystem using AWS services.