Purpose of review: Pain researchers and clinicians increasingly encounter machine learning algorithms in both research methods and clinical practice. This review provides a summary of key machine learning principles, as well as applications to both structured and unstructured datasets.
Recent findings: Aside from increasing use in the analysis of electronic health record data, machine and deep learning algorithms are now key tools in the analyses of neuroimaging and facial expression recognition data used in pain research.
Summary: In the coming years, machine learning is likely to become a key component of evidence-based medicine, yet will require additional skills and perspectives for its successful and ethical use in research and clinical settings.