This book takes an in-depth look at the emerging technologies transforming the way clinicians manage patients while simultaneously emphasizing that the best practitioners use both artificial and human intelligence to make decisions. AI and machine learning are extensively explored, with plain clinical English explanations of convolutional neural networks, backpropagation, and digital image analysis. Real-world examples of how these tools are employed are also discussed, including their value in diagnosing diabetic retinopathy, melanoma, breast cancer, cancer metastasis, and colorectal cancer, as well as in managing severe sepsis. With all the enthusiasm about AI and machine learning, it was also necessary to outline some of these new tools criticisms, obstacles, and limitations. Among the complaints discussed: are the relative lack of hard scientific evidence supporting some of the latest algorithms and the so-called black box problem. A chapter on data analytics takes a deep dive into new ways to conduct subgroup analysis and how it's forcing healthcare executives to rethink how they apply the results of large clinical trials to everyday medical practice. This re-evaluation is slowly affecting how diabetes, heart disease, hypertension, and cancer are treated. The research also suggests that data analytics will impact emergency medicine, medication management, and healthcare costs. An examination of the diagnostic reasoning process looks at how diagnostic errors are measured, what technological and cognitive errors are to blame, and what solutions are most likely to improve the process. It explores Type 1 and Type 2 reasoning methods; mental mistakes like availability bias, affective bias, and anchoring; and potential solutions such as the Human Diagnosis Project. Finally, the book explores the role of systems biology and precision medicine in clinical decision support and provides several case studies of how next-generation AI is transforming patient care.