AI-based Self-verification Supports for Clinical Guideline E-learning

Education is a challenging task in the healthcare context. Clinical practice guidelines (CPGs) encode the "best" evidence-based medical procedures to manage a specific disease, and are fundamental tools to specify how to act on patients following evidence-based recommendations. In this paper, we propose to complement traditional medical education by introducing advanced AI techniques to facilitate the learning of CPGs. The starting point of our approach is GLARE (Guideline Acquisition, Representation and Execution), a system to acquire and represent Computer-Interpretable Clinical Guidelines (CIGs), and to execute them on patients. In particular, we propose a new computer-based tool to support students' self-verification. Our tool proposes students a "shadow" of node in the CIG representation, and let students fill it with concrete medical actions and decisions. At each step, our tool compares and evaluates the student's proposals with the "golden standard" provided by the CIG.