As artificial intelligence-based decision support systems aim at assisting human specialists in high-stakes environments, studying the safety of the human-AI team as a whole is crucial, especially in the light of the danger posed by hallucinatory AI treatment suggestions from now ubiquitous large language models. In this work, we propose a method for safety assessment of the human-AI team in high-stakes decision-making scenarios. By studying the interactions between doctors and a decision support tool in a physical intensive care simulation centre, we conclude that most unsafe (i.e. potentially hallucinatory) AI recommendations would be stopped by the clinical team. Moreover, eye-tracking-based attention measurements indicate that doctors focus more on unsafe than safe AI suggestions.