Artificial intelligence methods for a Bayesian epistemology-powered evidence evaluation

Francesco DE PRETIS, Jürgen LANDES, William PEDEN*

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

10 Citations (Scopus)


Rationale, aims and objectives

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. 


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. 


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. 


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.

Original languageEnglish
Pages (from-to)504-512
Number of pages9
JournalJournal of Evaluation in Clinical Practice
Issue number3
Publication statusPublished - Jun 2021
Externally publishedYes

Bibliographical note

Francesco De Pretis and William Peden acknowledge funding from the European Research Council (PhilPharm—GA n. 639276) through the Marche Polytechnic University (Ancona, Italy). The Authors are grateful to Durham University for providing open access for this article. Jürgen Landes gratefully acknowledges funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—432308570 and 405961989.


  • artificial intelligence
  • drug safety
  • E-Synthesis
  • evidence evaluation
  • pharmacosurveillance
  • pharmacovigilance


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