Schroeder Kristopher M, Elkassabany Nabil
Anesthesiology, University of Wisconsin-Madison, Madison, Wisconsin, USA
University of Virginia School of Medicine, Charlottesville, Virginia, USA.
Reg Anesth Pain Med. 2025 Jul 4;50(7):592-594. doi: 10.1136/rapm-2024-105522.
Artificial intelligence (AI) has demonstrated a disruptive ability to enhance and transform clinical medicine. While the dexterous nature of anesthesiology work offers some protections from AI clinical assimilation, this technology will ultimately impact the practice and augment the ability to provide an enhanced level of safe and data-driven care. Whether predicting difficulties with airway management, providing perioperative or critical care risk assessments, clinical-decision enhancement, or image interpretation, the indications for AI technologies will continue to grow and are limited only by our collective imagination on how best to deploy this technology.An essential mission of academia is education, and challenges are frequently encountered when working to develop and implement comprehensive and effectively targeted curriculum appropriate for the diverse set of learners assigned to teaching faculty. Curriculum development in this context frequently requires substantial efforts to identify baseline knowledge, learning needs, content requirement, and education strategies. Large language models offer the promise of targeted and nimble curriculum and content development that can be individualized to a variety of learners at various stages of training. This technology has not yet been widely evaluated in the context of education deployment, but it is imperative that consideration be given to the role of AI in curriculum development and how best to deploy and monitor this technology to ensure optimal implementation.
人工智能（AI）已展现出增强和变革临床医学的颠覆性能力。虽然麻醉学工作的灵活性为其临床应用提供了一些保障，但这项技术最终将影响麻醉学实践，并增强提供更高水平的安全且基于数据的医疗服务的能力。无论是预测气道管理的困难、提供围手术期或重症监护风险评估、增强临床决策还是进行图像解读，人工智能技术的应用范围都将不断扩大，其发展仅受限于我们对如何最佳应用这项技术的集体想象力。学术界的一项重要使命是教育，在为不同类型的学生制定全面且针对性强的课程时，教师们经常会遇到挑战。在这种情况下，课程开发通常需要付出巨大努力来确定基础知识、学习需求、内容要求和教育策略。大语言模型有望实现针对性强且灵活的课程与内容开发，能够针对处于不同培训阶段的各类学生进行个性化定制。这项技术在教育领域的应用尚未得到广泛评估，但必须考虑人工智能在课程开发中的作用，以及如何最佳地应用和监控这项技术以确保其得到优化实施。
