Can medical agents build transferable clinical competence？ A dual-competency framework and evaluation system for medical agent education

The key issue in using generative artificial intelligence in medical education (AI-on) is not whether models can complete tasks, but whether learners can retain and transfer clinical competence after AI is withdrawn (AI-off), while recognizing and controlling AI-induced unsafe reasoning. However, existing studies have focused mainly on AI-enabled immediate knowledge, skills, and satisfaction, whereas reports on AI-off transfer ability and changes in patient-related outcomes remain scarce. Therefore, based on narrative evidence syntheses published before July 2026, this article proposes the dual-competency agentic medical education (DC-AME) framework. The framework consists of two complementary competencies: AI-off independent clinical competence and AI-on safe human–AI collaboration competence, organized into a closed loop comprising learner diagnosis, contextual simulation, scaffolding fading, and transfer validation. Built on shared cases, competency objectives, and an evidence base, intelligent tutors, virtual patients, and clinical reasoning agents jointly support a minimal training unit of “AI-off independent answering—AI-on assisted reflection—AI-off transfer to unseen cases.” Nevertheless, DC-AME remains a design proposition awaiting validation. Future studies may designate delayed transfer after AI-off and the recognition and control of AI-induced unsafe reasoning as co-primary outcomes, and list AI reliance, transfer gap, and performance after scaffold withdrawal as exploratory indicators; ultimately, the value of DC-AME should be evaluated in practice.