Causality assessment is a fundamental biomedical technique for the signal detection performed by Pharmacovigilance centers in a Spontaneous reporting system. Moreover, it is a crucial and important practice for detecting preventable adverse drug reactions. Among different methods for causality assessment, algorithms (such as the Naranjo, or Begaud Methods) seem for their operational procedure and easier applicability one of the most commonly used methods. With the upcoming of the new European Pharmacovigilance legislation including in the definition of the adverse event also effects resulting from abuse, misuse and medication error, all well-known preventable causes of ADRs, there was an emerging need to evaluate whether algorithms could fulfill this new definition. In this review, twenty-two algorithmic methods were identified and none of them seemed to fulfill perfectly the new criteria of adverse event although some of them come close. In fact, several issues were arisen in applying causality assessment algorithms to these new definitions as for example the impossibility to answer the rechallenge question in case of medication error or AEFI (Adverse Event Following Immunization). Moreover, the exact conditions at which events occurred, as for example dosage or mode of administration should be considered to better assess causality in conditions of abuse/overdose, or misuse as well as in conditions of lack of expected efficacy reports for biotechnological drugs and adverse event occurring after mixing of vaccines. Therefore, this review highlights the need of updating algorithmic methods to allow a perfect applicability in all possible clinical scenarios accordingly or not with the terms of marketing authorization.