To develop clinical reasoning skills, medical students are often tasked with interacting with trained standardized patients (SPs). Human SPs enable real conversations that can resemble authentic clinical scenarios. However, human SPs require extensive training and are often limited in their accessibility and continual availability to medical students or residents. Virtual SPs offer the ability for medical students to practice clinical interviews in a lower-stakes setting across a broader set of clinical cases. This paper introduces a virtual SP (VSP) that leverages Amazon’s Nova Sonic, a speech-to-speech foundation model designed for human-like conversation. We investigated the ability of Nova Sonic to portray four distinct clinical cases in virtual doctor-patient encounters with 20 third-year medical students. The system’s realism, its perceived learning value, and user experience were all assessed via a survey administered to the students. Students were also asked to compare this experience to interactions with a human SP. Survey results and conversations were analyzed to derive insights for improving the Nova Sonic-based VSP system.

Standardized patients (SPs) are essential for clinical reasoning assessments in medical education. This paper introduces evaluation metrics that apply to both human and simulated SP systems. The metrics are computed using two LLM-as-a-judge approaches that align with human evaluators on SP performance, enabling scalable formative clinical reasoning assessments.