Empowering standardized residency training in China through large language models: problem analysis and solutions

Abstract

BACKGROUND: China's standardized residency training (SRT) faces challenges such as uneven distribution of resources, inadequate clinical skill training, subjective assessment methods, and high burnout rates among physician. The aim of this commentary paper is to explore the underlying causes and impacts of these challenges, analyze the potential of large language models (LLMs) to address these issues, and discuss the ethical concerns they raise. DISCUSSION: Uneven economic development in China has led to disparities in SRT resources and faculty distribution, with residents facing deficiencies in clinical training, research, and communication skills. Heavy workloads and nontechnical tasks exacerbate resident burnout and stagnation, hindering growth and skill development. LLMs have the potential to transform traditional education and learning modes by optimizing teaching resources, delivering real-time medical knowledge, and simulating clinical scenarios, thus effectively bridging gaps between training bases. As virtual mentors, LLMs can provide real-time guidance and personalized feedback, enhancing individuals' clinical and research skills. LLMs also make assessments more objective and improve nontechnical clinical task efficiency, reducing burnout, and increasing job satisfaction. However, their integration creates ethical challenges around information accuracy, privacy protection, biases, and academic misconduct. CONCLUSION: LLMs offer innovative solutions to China's SRT challenges. However, ethical issues must be carefully addressed, viewing LLMs as a potent complement to traditional medical education, and maintain the predominant role of conventional educational methods.

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