Big data and machine learning are transforming clinical medicine. While data alone are not useful, it is the algorithms that will drive this transformation. Machine learning, unlike traditional expert systems, learns from data to identify patterns and predict outcomes. It can handle large numbers of predictors and complex interactions, enabling the use of new types of data that were previously difficult to analyze. For example, machine learning can analyze radiographic images by processing digital pixel matrices to detect abnormalities, and it can also analyze insurance claims data to create a more comprehensive picture of patients' medical histories. However, there are challenges. Algorithms may overfit to spurious correlations, and data quality and quantity are crucial. Biases in data collection can affect model performance and generalizability. Additionally, machine learning does not solve the fundamental issues of causal inference in observational data. Correlation does not imply causation, and this is especially important when including many variables in statistical models. Machine learning has applications in various fields, including astronomy, biomedicine, and clinical medicine. In medicine, it can improve prognosis by using large datasets from electronic health records to predict outcomes more accurately. It can also replace some of the work of radiologists and pathologists by analyzing imaging data with high accuracy. Furthermore, it can enhance diagnostic accuracy by generating differential diagnoses and suggesting appropriate tests. The integration of machine learning into clinical medicine will disrupt three areas: prognosis, diagnostic accuracy, and the roles of radiologists and pathologists. As data complexity increases, machine learning will become an essential tool for clinicians. However, this transformation will also create winners and losers in medicine. Ultimately, patients will benefit from the improved care made possible by machine learning.Big data and machine learning are transforming clinical medicine. While data alone are not useful, it is the algorithms that will drive this transformation. Machine learning, unlike traditional expert systems, learns from data to identify patterns and predict outcomes. It can handle large numbers of predictors and complex interactions, enabling the use of new types of data that were previously difficult to analyze. For example, machine learning can analyze radiographic images by processing digital pixel matrices to detect abnormalities, and it can also analyze insurance claims data to create a more comprehensive picture of patients' medical histories. However, there are challenges. Algorithms may overfit to spurious correlations, and data quality and quantity are crucial. Biases in data collection can affect model performance and generalizability. Additionally, machine learning does not solve the fundamental issues of causal inference in observational data. Correlation does not imply causation, and this is especially important when including many variables in statistical models. Machine learning has applications in various fields, including astronomy, biomedicine, and clinical medicine. In medicine, it can improve prognosis by using large datasets from electronic health records to predict outcomes more accurately. It can also replace some of the work of radiologists and pathologists by analyzing imaging data with high accuracy. Furthermore, it can enhance diagnostic accuracy by generating differential diagnoses and suggesting appropriate tests. The integration of machine learning into clinical medicine will disrupt three areas: prognosis, diagnostic accuracy, and the roles of radiologists and pathologists. As data complexity increases, machine learning will become an essential tool for clinicians. However, this transformation will also create winners and losers in medicine. Ultimately, patients will benefit from the improved care made possible by machine learning.