The spread of AI-embedded systems involved in medical decision making makes it critical to build these systems according to trustworthiness standards. However, empirically investigating trust is challenging. One reason is the lack of standard protocols to design trust experiments. To get an overview of the current practices in the experimental protocols for studying trust in the context of AI-assisted decision making, we conducted a systematic review of such papers. We annotated, categorized, and analyzed them along the constitutive elements of an experimental protocol (i.e., participants, task). Drawing from empirical practices in social and cognitive studies on human-human trust, we provide practical guidelines and research opportunities to ameliorate the methodology of studying Human-AI trust in medical decision-making contexts. In this workshop, we would like to discuss how these insights could improve the quality of data about users' trust and, thus, lead to new steps towards closing the "last mile" between AI and healthcare workers.