Medical Argument Mining: Exploitation of Scarce Data Using NLI Systems

In recent years, there has been a growing interest in developing intelligent systems to assist healthcare professionals, particularly in the field of Evidence-Based Medicine (EBM). EBM systems aim to extract pertinent information from unstructured clinical documents and transform it into a structured, machine-readable format, enabling automated analysis. Argument Mining (AM), aligning with EBM, examines the evidence and reasoning clinicians use in clinical cases. This process involves identifying argumentative structures within texts--specifically, finding claims (a point to be proved) and premises (evidence that supports or refutes a claim), and establishing support or attack relations between them. In the clinical context, this process enables the extraction of logical relationships that justify clinical decision-making (Stylianou and Vlahavas, 2021).