The multitude and complexity of data in health sciences has given rise to the increasing use of artificial intelligence (AI). AI technologies of importance include machine (ML) and deep (DL) learning, natural language processing (NLP) and rule-based expert systems (RBES). These AI technologies have also found their way into occupational health in the analyses of structured and unstructured data varying from application in job-codings (e.g. NLP). exposure assessment (e.g. NLP and RBES), data analyses (e.g. ML, DL and Neural networks), and risk assessment (RBES). Examples of AI technologies in Occupational Health will be presented including efforts on job-codings, job-exposure-matrix construction from the EPHOR project, high-dimensional (ML) data analyses within UK-Biobank and the Synergy project, and the application of RBES in risk assessment of benzene. Ethical issues in the application of AI in occupational health will also be discussed.