Assessing whether a drug works or not typically means, in a regulatory environment, showing a statistically significant result for the comparison of interest. But how does one determine whether the Benefit-Risk (BR) profile of a drug is "positive"? The latter is a different question from the former although they are clearly connected. Different tools are required to address the question of whether the BR profile of a new medicinal product supports decisions about progression of a program to the next stage, market authorization, and reactions to post-marketing surveillance data. In recent years, qualitative frameworks and evaluations of BR have been proposed to support this challenging process. Bayesian analysis, with its formal utilization of prior information and repeated updates based on accumulating knowledge, naturally supports decision theory. In this talk, we discuss the rationale for adopting a Bayesian framework when evaluating BR for decision-making purposes. Furthermore, we outline specific gaps and opportunities in the current practice of BR evaluation that support the case for greater application of statistical decision theory and Bayesian analysis.