From Text-Based Exams to Clinical Reality: A Paradigm Shift in Medical Subspecialty Assessment through Visual-Auditory AI-Driven Patient Simulation

Traditional medical specialty assessments primarily rely on text-based multiple-choice exams that privilege rote memorization over clinical reasoning. As artificial intelligence (AI) systems become increasingly adept at solving these examinations within minutes, it becomes urgent to reconsider what is being assessed and why. In this opinion article, we argue that clinical competence requires more than information recall—it demands the ability to recognize patient cues, communicate effectively, and prioritize diagnostic and therapeutic steps in context. We propose a simulation-based assessment model powered by AI-generated visual-auditory patient avatars. These avatars would engage examinees in realistic clinical encounters that test observation, reasoning, empathy, and adaptability. This model reflects the complexity of real-world practice far more accurately than current written exams, and can be implemented with existing technologies without prohibitive infrastructure.