The ethical challenges in the integration of artificial intelligence and large language models in medical education: A scoping review - Xinrui Li; Xiaodan Yan; Han Lai (PLoS ONE, 2025)
With the rapid development of artificial intelligence (AI), large language models (LLMs), such as ChatGPT have shown potential in medical education, offering personalized learning experiences. However, this integration raises ethical concerns, including privacy, autonomy, and transparency. This study employed a scoping review methodology, systematically searching relevant literature published between January 2010 and August 31, 2024, across three major databases: PubMed, Embase, and Web of Science. Through rigorous screening, 50 articles which met inclusion criteria were ultimately selected from an initial pool of 1,192 records. During data processing, the Kimi AI tool was utilized to facilitate preliminary literature screening, extraction of key information, and construction of content frameworks. Data reliability was ensured through a stringent cross-verification process whereby two independent researchers validated all AI-generated content against original source materials. The study delineates ethical challenges and opportunities arising from the integration of AI and LLMs into medical education, identifying seven core ethical dimensions: privacy and data security, algorithmic bias, accountability attribution, fairness assurance, technological reliability, application dependency, and patient autonomy. Corresponding mitigation strategies were formulated for each challenge. Future research should prioritize establishing dedicated ethical frameworks and application guidelines for AI in medical education while maintaining sustained attention to the long-term ethical implications of these technologies in healthcare domains.