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My objective is to analyze, from a critical approach, the levels of evidence and the recommendation degrees most actually accepted by the medical scientific community, with a critical evaluation of the concept of truth and its uncertainty. Then, I will analyze the role of radiomics and imaging biomarkers as the scientific approximation of Imaging to Personalized Medicine. The value of observational in-silico studies with imaging biobanks to generate evidence in the real world (Real World Data RWD, Real World Evidence RWE) will be highlighted. Finally, a methodological proposal will be established to obtain the best inference of the observational assistance data. This proposal relates to the development of observational clinical studies with digital data (in-silico), obtained in a dissociated form from the patients. These studies use well-structured repositories to generate high quality data for the health system. The Real-world data (RWD) allows, when analyzed as experimental clinical studies with virtual cohorts, to obtain inferences with a high degree of evidence (RWE) once data quality, confounding variables, and biases have been controlled. This evidence can be more reproducible and reliable, more generalized and universal, and also more relevant to health systems than standard prospective approaches. The use of a large amount of information (data, RWD, structured reports) and the capacity of computational processing (radiomics, image biomarkers, Deep Learning) will minimize the problem of subsampling, sample heterogeneity, and data variability associated with the lack of standardization of images. Artificial Intelligence will assist in the critical evaluation of evidence levels and degrees of recommendation, truth and uncertainty, implementation of observational in-silico  studies with imaging biobanks and obtaining the best inference with these designs. Palabras clave:

Radiomics and imaging biomarkers in observational clinical studies with retrospective data