Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare covers exciting developments at the intersection of computer science and statistics. While much of machine-learning is statistics-based, achievements in deep learning for image and language processing rely on computer science's use of big data. Aimed at those with a statistical background who want to use their strengths in pursuing AI research, the book: -       Covers broad AI topics in drug development, precision medicine, and healthcare. -       Elaborates on supervised, unsupervised, reinforcement, and evolutionary learning methods. -       Introduces the similarity principle and related AI methods for both big and small data problems. -       Offers a balance of statistical and algorithm-based approaches to AI. -       Provides examples and real-world applications with hands-on R code. -       Suggests the path forward for AI in medicine and artificial general intelligence .   As well as covering the history of AI and the innovative ideas, methodologies and software implementation of the field, the book offers a comprehensive review of AI applications in medical sciences. In addition, readers will benefit from hands on exercises, with included R code.