Machine learning (ML), which converts complex data into algorithms, challenges the traditional epidemiologic approach of evidence-based medicine (EBM). Here I outline the differences, strengths, and limitations of these 2 approaches and suggest areas of reconciliation.
Beginning in the 1970s, scientists extolled the virtues of EBM's hypothesis-driven, protocolized experiments involving well-defined populations and preselected exposure and outcome variables. Inferences were made using traditional biostatistics.
First, EBM wasn’t around in the 70’s. It started, as a movement, in 1992. Second, EBM is not a method of research. It is, rather, a system of approaching the clinical care of individual patients. One patient at a time. The rest of the article is equally confusing.