Samra Gurtek Singh, Ramoutar Vashisht, Chen Kelley, Chaudhry Muiz, Patel Hrithika, Bird Terese, Rodwell Vanessa
Department of Medicine, University of Leicester, Leicester, UK.
Ulverscroft Eye Unit, School of Psychology and Vision Sciences, University of Leicester, Leicester, UK.
Clin Teach. 2025 Aug;22(4):e70139. doi: 10.1111/tct.70139.
Chest X-ray (CXR) interpretation is a fundamental yet challenging skill for medical students to master. Traditional resources like Radiopaedia offer extensive content, while newer artificial intelligence (AI) tools, such as Chester, provide pattern recognition and real-time feedback. This study aims to evaluate Radiopaedia and Chester's effectiveness as educational tools and to explore student perspectives on AI.
A teaching session on CXR interpretation fundamentals was delivered to establish a standardised baseline of knowledge among participants, followed by a live tutorial introducing students to the functionality of both Chester AI and Radiopaedia. Students engaged with both tools to answer a 25-item workbook assessing complex CXR pathologies. CXRs were deliberately selected for their complexity to examine student engagement with online learning tools amid diagnostic uncertainty, encouraging applied clinical reasoning.
Preclinical medical students were recruited and randomly assigned to the Chester AI (n = 5) or Radiopaedia group (n = 5). During the workbook task, participants were instructed to engage with the workbook using Radiopaedia and Chester AI. Post-session, participants took part in focus groups to share their experiences. Thematic analysis highlighted Chester's efficiency and potential as a revision tool but noted limitations with complex CXR pathologies. Radiopaedia was valued for its comprehensiveness but was less efficient for the workbook task due to its vast array of content.
AI tools such as Chester show promise as complementary resources alongside traditional learning materials. Combining Chester's efficiency and real-time feedback with Radiopaedia's in-depth content may optimise learning and improve CXR interpretation skills.
胸部X光（CXR）解读是医学生需要掌握的一项基本但具有挑战性的技能。像Radiopaedia这样的传统资源提供了丰富的内容，而诸如Chester等更新的人工智能（AI）工具则提供模式识别和实时反馈。本研究旨在评估Radiopaedia和Chester作为教育工具的有效性，并探讨学生对人工智能的看法。
开展了一次关于CXR解读基础知识的教学课程，以在参与者中建立标准化的知识基线，随后进行了一次现场教程，向学生介绍Chester AI和Radiopaedia的功能。学生使用这两种工具回答一本包含25个项目的练习册，该练习册评估复杂的CXR病变。特意选择复杂的CXR图像，以考察学生在诊断不确定性情况下对在线学习工具的参与度，鼓励应用临床推理。
招募临床前医学生并将他们随机分配到Chester AI组（n = 5）或Radiopaedia组（n = 5）。在练习册任务期间，指导参与者使用Radiopaedia和Chester AI来完成练习册。课程结束后，参与者参加焦点小组以分享他们的经验。主题分析强调了Chester作为复习工具的效率和潜力，但也指出了其在复杂CXR病变方面的局限性。Radiopaedia因其内容全面而受到重视，但由于其内容繁多，在练习册任务中效率较低。
诸如Chester这样的人工智能工具作为传统学习材料的补充资源显示出前景。将Chester的效率和实时反馈与Radiopaedia的深入内容相结合，可能会优化学习并提高CXR解读技能。
