OBJECTIVE: The diversity of types of evidence (eg, case reports, animal studies and observational studies) makes the assessment of a drug\'s safety profile into a formidable challenge. While frequentist uncertain inference struggles in aggregating these signals, the more flexible Bayesian approaches seem better suited for this quest. Artificial Intelligence (AI) offers great promise to these approaches for information retrieval, decision support, and learning probabilities from data.
METHODS: E-Synthesis is a Bayesian framework for drug safety assessments built on philosophical principles and considerations. It aims to aggregate all the available information, in order to provide a Bayesian probability of a drug causing an adverse reaction. AI systems are being developed for evidence aggregation in medicine, which increasingly are automated.
RESULTS: We find that AI can help E-Synthesis with information retrieval, usability (graphical decision-making aids), learning Bayes factors from historical data, assessing quality of information and determining conditional probabilities for the so-called \'indicators\' of causation for E-Synthesis. Vice versa, E-Synthesis offers a solid methodological basis for (semi-)automated evidence aggregation with AI systems.
CONCLUSIONS: Properly applied, AI can help the transition of philosophical principles and considerations concerning evidence aggregation for drug safety to a tool that can be used in practice.

Basic science has delivered unprecedented insights into intricate relationships on the smallest scales within well-controlled environments. Addressing pressing societal decision problems requires an understanding of systems on larger scales in real-world situations.
To assess how well the evidence assessors E-Synthesis and EBM+ assess basic science findings to support medical decision making.
We demonstrate the workings of E-Synthesis and EBM+ on a case study: the suspected causal connection between the widely-used drug amoxicillin (AMX) and the putative adverse drug reaction: Drug Reaction with Eosinophilia and Systemic Symptoms (DRESS).
We determine an increase in the probability that AMX can cause DRESS within the E-Synthesis approach and using the EBM+ standards assess the basic science findings as supporting the existence of a mechanism linking AMX and DRESS.
While progress is made towards developing methodologies which allow the incorporation of basic science research in the decision making process for pressing societal questions, there is still considerable need for further developments. A continued dialogue between basic science researchers and methodologists, philosophers and statisticians seems to offer the best prospects for developing and evaluating continuously evolving methodologies.

Today\'s surge of big data coming from multiple sources is raising the stakes that pharmacovigilance has to win, making evidence synthesis a more and more robust approach in the field. In this scenario, many scholars believe that new computational methods derived from data mining will effectively enhance the detection of early warning signals for adverse drug reactions, solving the gauntlets that post-marketing surveillance requires. This article highlights the need for a philosophical approach in order to fully realize a pharmacovigilance 2.0 revolution. A state of the art on evidence synthesis is presented, followed by the illustration of E-Synthesis, a Bayesian framework for causal assessment. Computational results regarding dose-response evidence are shown at the end of this article.