AI-driven intelligent training enhances clinical competence in oncology residency: a randomized controlled trial

Abstract

Background: Rapid advances in artificial intelligence (AI) offer new opportunities to address persistent challenges in healthcare professions education, particularly in oncology residency training, where rapidly evolving knowledge, complex decision-making, and limited high-fidelity practice environments hinder competency development. However, evidence from rigorously evaluated educational interventions remains limited. Methods: = 62). The AIEIT model integrated a dynamic knowledge graph for personalized learning, a virtual patient-AI mentor system for adaptive skills training, a mixed-reality multidisciplinary team platform for collaborative decision-making, and a learning analytics dashboard for continuous feedback. Outcomes included knowledge acquisition, clinical reasoning, procedural competence, collaborative performance, cognitive efficiency, and longitudinal clinical outcomes. Results: < 0.001) and better clinical outcomes, including fewer postoperative complications and higher patient satisfaction. Conclusions: This study demonstrates that an AI-driven, closed-loop educational model can substantially enhance clinical competence formation in oncology residency training. By integrating data-driven personalization, human-AI collaboration, and virtual-real learning environments, the AIEIT framework offers a scalable and evidence-based approach for advancing healthcare professions education.

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