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

人工智能驱动的智能训练可提高肿瘤住院医师的临床能力:一项随机对照试验

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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: We conducted a randomized controlled trial involving 124 breast oncology residents from three tertiary hospitals. Participants were randomly assigned to an AI-empowered intelligent teaching (AIEIT) group (n = 62) or a control group receiving conventional training (n = 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: The AIEIT group outperformed the control group across all domains, demonstrating superior mastery of theoretical knowledge, higher procedural accuracy, and greater multidisciplinary collaboration (all P < 0.001). Cognitive workload and training time were significantly reduced, while technology adaptability and evidence-based practice utilization markedly improved. At 3-month follow-up, the AIEIT group maintained higher knowledge retention (91.2 ± 3.5% vs 76.8 ± 8.4%, P < 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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