This work defines a research on data strategy focused on medical imaging and derived image biomarkers to critically assess the concept of causal inference and uncertainties. Computational observational studies will be valued to generate casual inference from real world data. Our main goal is to propose a scientific methodology that allows to estimate causalities from observational studies through quality control of large databases, definition of plausible hypotheses, using computational estimated models and artificial intelligence tools. The computational approach of radiology to precision medicine by using epidemiological strategies is based on causal inference studies relies on real-world data observational, longitudinal, case-control analysis designed (being case the presence, and control the absence of the event to be estimated). In this new research setting, we consider disease in classical epidemiology as phenotyping, response to treatment and final prognosis; and exposure equals to the presence of a radiomic, dynamic image biomarker or AI modeling solution. Research with data on which causality is to be inferred must control for recruitment of closed cases, in which the researcher does not intervene in the patient’s clinical history but works on databases, collecting data to be secondary used in generating consistent causalities.
Causal Inference From Real-World Data