Hybrid Clinical Simulation: Integrating Standardized and AI-Powered Virtual Patients

Abstract

Clinical simulation is a fundamental component of early medical training, and standardized patients have long been recognized as essential resources for developing communication skills, socio-emotional competencies, and diagnostic reasoning. However, their implementation is often limited by logistical constraints, high costs, and challenges in scalability—particularly in institutions with large student cohorts. In this context, artificial intelligence–powered virtual patients have emerged as complementary tools that expand opportunities for deliberate practice through repeatable scenarios, immediate feedback, and standardized performance. Recent literature indicates that advanced language models can sustain simulated clinical interviews with acceptable levels of coherence and educational value, enabling hybrid approaches that combine the human richness of standardized patients with the efficiency of technological environments. This convergence offers substantial pedagogical advantages, while simultaneously introducing ethical and educational challenges related to algorithmic bias, student psychological safety, and the need for faculty oversight. For medical schools in Latin America, hybrid simulation models represent a viable strategy to enhance training equity, optimize institutional resources, and increase access without compromising the humanistic dimensions inherent to standardized patient encounters. This manuscript synthesizes current evidence on the potential benefits and limitations of integrating standardized patients with AI-powered virtual patients and outlines initial considerations for their responsible adoption in preclinical curricula.