MOLINET , Benjamin

Argument(ation) mining, a rapidly growing area of Natural Language Processing (NLP) and computational models of argument, aims at the automatic recognition of argument structures (i.e., components and relations) in natural language textual resources. In the healthcare domain, argument mining has proven beneficial in providing methods to...

Background: A huge amount of research is carried out nowadays in Artificial Intelligence to propose automated ways to analyse medical data with the aim to support doctors in delivering medical diagnoses. However, a main issue of these approaches is the lack of transparency and interpretability of the achieved results, making it hard to employ...

The automatic generation of explanations to improve the transparency of machine predictions is a major challenge in Artificial Intelligence. Such explanations may also be effectively applied to other decision making processes where it is crucial to improve critical thinking in human beings. An example of that consists in the clinical cases...

Evidence-based medicine aims at making decisions about the care of individual patients based on the explicit use of the best available evidence in the patient clinical history and the medical literature results. Argumentation represents a natural way of addressing this task by (i) identifying evidence and claims in text, and (ii) reasoning upon...

International audience

The need for transparent AI systems in sensitive domains like medicine has become key. In this paper we present ANTIDOTE, a software suite proposing different tools for argumentation-driven explainable Artificial Intelligence for digital medicine. Our system offers the following functionalities: multilingual argumentative analysis for the...

International audience