The idea of implementing artificial intelligence in medicine is as old as artificial intelligence itself. So far, technical difficulties have prevented the integration of artificial intelligence in day-to-day healthcare. During the coronavirus disease 2019 (COVID-19) pandemic, a substantial amount of funding went into projects to research and implement artificial intelligence in healthcare. So far, artificial intelligence-based tools have had little impact in the fight against COVID-19. The reasons for the lack of success are complex. With advancing digitalisation, new data-based developed methods and research are finding their way into intensive care medicine. Data scientists and medical professionals, representing two different worlds, are slowly uniting. These two highly specialised fields do not yet speak a uniform language. Each field has its own interests and objectives. We took this idea as a starting point for this technical guide and aim to provide a deeper understanding of the terminology, applications, opportunities and risks of such applications for physicians. The most important terms in the field of machine learning are defined within a medical context to assure that the same language is spoken. The future of artificial intelligence applications will largely depend on the ability of artificial intelligence experts and physicians to cooperate in order to release the true power of artificial intelligence. Large research consortia, covering both technical and medical expertise, will grow because of growing demand in the future.
- The current COVID-19 pandemic boosted many artificial intelligence-based research projects, so far without breakthrough.
- We offer a look behind the workings of an artificial intelligence setup.
- Review typical obstacles associated with data acquisition and data processing.
- We show why it is necessary to combine expertise from medicine and IT to establish artificial intelligence applications successfully.